--- title: "IronClaw Use Cases" description: "Community-built IronClaw workflows, automations, and agent configurations." canonical: "https://hub.ironclaw.com/usecases" markdown: "https://hub.ironclaw.com/usecases.md" entries: 107 --- # IronClaw Use Cases > Explore public workflows and the skills and tools used to build them. - [A daily digest of RSS, Hacker News, and Reddit noise filtered by your niche and delivered to Slack, Notion, Telegram, or email](https://hub.ironclaw.com/usecases/a-daily-digest-of-rss-hacker-news-and-reddit-noise-filtered-by-your-niche-and-delivered-to-slack.md) — Every morning, scan RSS feeds, Hacker News, and Reddit for anything that matters to my niche — crypto, dev tools, AI — and send me a tight 3-7 item digest with a TL;DR and a "why this matters for you" line per item. Balanced filter by default. - [A meta-agent that watches what you ask, finds repeated patterns, and proposes — or auto-builds — new automations on a weekly recap cadence](https://hub.ironclaw.com/usecases/a-meta-agent-that-watches-what-you-ask-finds-repeated-patterns-and-proposes-or-auto-builds-new-a.md) — Don't just do what I ask — watch my behavior over time, find the patterns I keep repeating, and propose new automations to take them off my plate. Every week give me a short recap of what you noticed and what you'd like to set up. I approve, you build. - [A proactive family agent: smart recurring reminders, a per-person weekly meal planner with auto shopping list, and a per-child bedtime story generator that gets better the more you use it](https://hub.ironclaw.com/usecases/a-proactive-family-agent-smart-recurring-reminders-a-per-person-weekly-meal-planner-with-auto-sh.md) — Be my family's proactive assistant — keep track of our shared reminders (anniversaries, school pickups, recurring meds), plan weekly meals for each of us based on our tastes and dietary needs with a shopping list, and generate a fresh bedtime story for my kid every night that matches their age and interests. Send everything to the channel each of us uses. - [A read-only Telegram user bot (MTProto) for community managers — watch keywords across groups, get daily recaps with cross-group topic detection, and surface unanswered mentions](https://hub.ironclaw.com/usecases/a-read-only-telegram-user-bot-mtproto-for-community-managers-watch-keywords-across-groups-get-da.md) — I'm a community manager / supporter. Watch the Telegram groups I'm in, flag any message that matches my keywords or mentions my brand, send me a daily recap of the most important threads per group plus a cross-group topic digest, and remind me when something has been sitting unanswered for too long. Read-only — never auto-post, never auto-reply. - [A read-only Telegram user bot (MTProto) that gives founders / VCs a 5-minute daily DM recap grouped by who needs a reply, and logs a per-day contact history — never downloads attachments, never sends anything](https://hub.ironclaw.com/usecases/a-read-only-telegram-user-bot-mtproto-that-gives-founders-vcs-a-5-minute-daily-dm-recap-grouped.md) — I'm a founder / VC / operator. Every morning give me a 5-minute recap of all the DMs I got in the last 24 hours — who's waiting on me, what's just FYI, what's noise — and log every contact interaction into a daily Markdown file in my Saved Messages. Read-only. Never download attachments. Never auto-reply. - [Accountability Partner — Daily goal tracker that alerts when you're stuck or late](https://hub.ironclaw.com/usecases/accountability-partner-daily-goal-tracker-that-alerts-when-you-re-stuck-or-late.md) — You are my accountability partner. Store my goals in memory at goals/active.md When I say "add goal: [description] by [deadline]": 1. Read memory at goals/active.md 2. Add new goal with: description, deadline, status (IN PROGRESS), created date, last update date, progress notes 3. Write back to memory When I say "update goal [name]: [progress]": 1. Read memory at goals/active.md 2. Find the goal, update progress notes and last update date 3. Write back to memory For the daily routine — run every day at 9:00 AM: 1. Read memory at goals/active.md 2. For each goal with status IN PROGRESS: - Calculate days since last update - Calculate days until deadline - If days since last update >= 3: flag as STUCK - If days until deadline <= 2: flag as URGENT 3. Send Telegram message only if there are STUCK or URGENT goals: "⚡ Accountability Check 🔴 STUCK (no update in 3+ days): - [goal name] — last update [X] days ago 💬 Last note: [last progress note] 🟡 URGENT (deadline in 2 days or less): - [goal name] — due [date] 💪 Still on track: - [goal name] — [X] days until deadline" If all goals are on track: reply HEARTBEAT_OK and stop. - [Add watermark to media](https://hub.ironclaw.com/usecases/add-watermark-to-media.md) — "Add my logo as a watermark to the bottom-right corner of this image at 50% opacity." - [Aggregate commitments from Gmail, calendar, Slack / Telegram, task apps, and GitHub, then build a time-blocked daily schedule — preview the night before, finalize in the morning, optionally create calendar events](https://hub.ironclaw.com/usecases/aggregate-commitments-from-gmail-calendar-slack-telegram-task-apps-and-github-then-build-a-time.md) — Every night, scan my Gmail, calendar, Slack, Telegram, task apps, and GitHub for anything that looks like a commitment or deadline for tomorrow. Build me a time-blocked schedule I can review before bed, then send me a finalized version in the morning. If something is clearly a meeting or call, ask me before creating a calendar event for it. - [AI as a Telegram Assistant](https://hub.ironclaw.com/usecases/ai-as-a-telegram-assistant.md) — Research on NEAR Legion based on this doc: http://docs.nearlegion.com - [AI as a Weekly Planner](https://hub.ironclaw.com/usecases/ai-as-a-weekly-planner.md) — Help me plan my week every Sunday. Prioritize my goals, meetings, and important deadlines. - [AI as an Entertainment Curator](https://hub.ironclaw.com/usecases/ai-as-an-entertainment-curator.md) — Find me completed TV series similar to Game of Thrones with strong political storylines and no fantasy elements. - [AI parses receipts from Gmail, photo uploads, or manual entry and logs every expense into Google Sheets (or Notion, Airtable) with a tax-deductible flag](https://hub.ironclaw.com/usecases/ai-parses-receipts-from-gmail-photo-uploads-or-manual-entry-and-logs-every-expense-into-google-s.md) — Whenever I get a receipt — via email, a photo I send you, or me pasting the text — extract the merchant, date, amount, currency, tax, category, payment method, and whether it's tax-deductible, then log it into my Google Sheet. I want zero manual data entry and I want tax season to be a single export. - [Alert me before stablecoins depeg](https://hub.ironclaw.com/usecases/alert-me-before-stablecoins-depeg.md) — "Watch USDC, USDT, DAI, and USDe. Alert me if any stablecoin trades below $0.995 or shows unusual market movement." - [Binance technical-analysis analyst with on-demand calls and watchlist alerts](https://hub.ironclaw.com/usecases/binance-technical-analysis-analyst-with-on-demand-calls-and-watchlist-alerts.md) — Give me a clean TA read on BTC (or any symbol I ask) with entry, stop loss, and take-profit — on demand or pushed to me when a strong setup appears on my watchlist. - [Bitcoin Reddit Sentiment Scanner — Daily mood check from r/cryptocurrency](https://hub.ironclaw.com/usecases/bitcoin-reddit-sentiment-scanner-daily-mood-check-from-r-cryptocurrency.md) — Fetch this URL: https://www.reddit.com/r/cryptocurrency/search.rss?q=bitcoin&sort=new&limit=25 Parse the RSS/XML response and extract the title and description of each post. Then analyze the sentiment of each post based on its title and description: - POSITIVE: optimistic, bullish, price going up, good news, adoption - NEGATIVE: bearish, crash, scam, FUD, regulation crackdown, price drop - NEUTRAL: question, discussion, neither positive nor negative Count how many posts fall into each category. Then send me a Telegram message in this format: "📊 Bitcoin Reddit Sentiment (last 25 posts) 🟢 Positive: X posts 🔴 Negative: X posts ⚪️ Neutral: X posts Sentiment: [BULLISH / BEARISH / MIXED] — based on which category dominates Top signal: [paste the most interesting/extreme title you found]" If positive > negative by 5+ posts: label as BULLISH If negative > positive by 5+ posts: label as BEARISH Otherwise: MIXED - [BTC Dominance Alert — Auto-notify when BTC dom crosses key thresholds](https://hub.ironclaw.com/usecases/btc-dominance-alert-auto-notify-when-btc-dom-crosses-key-thresholds.md) — Create a routine that runs every 5 hours and checks BTC dominance. The routine should do the following: 1. Fetch global crypto market data from this URL: https://api.coingecko.com/api/v3/global 2. Extract the field: data.market_cap_percentage.btc That is the current BTC dominance percentage. 3. Apply this logic: - If BTC dominance is BELOW 60%: send me a Telegram message saying: "🔴 BTC Dominance Alert: BTC dom dropped to [X]% — below 60%. Altcoin season signal." - If BTC dominance is ABOVE 65%: send me a Telegram message saying: "🟡 BTC Dominance Alert: BTC dom rose to [X]% — above 65%. BTC dominance expanding." - If BTC dominance is between 60% and 65%: do NOT send anything. Silent run. 4. Always write the result to memory at btc-dominance/last-check.md with: - timestamp of the check - current BTC dominance value - whether an alert was sent or not Do not send a message on every run — only when a threshold is crossed. If nothing triggered, reply HEARTBEAT_OK and stop. - [Bug Bounty Triager — New reports auto-reproduced, scored against your attack surface, and queued by priority](https://hub.ironclaw.com/usecases/bug-bounty-triager-new-reports-auto-reproduced-scored-against-your-attack-surface-and-queued-by.md) — You are my bug bounty triager. When a new report comes in, you reproduce it, score it against our actual attack surface, and draft the initial response — so I review a prioritized queue instead of a raw inbox. When I say "triage: [report ID or paste report]" or when a new HackerOne/Bugcrowd webhook triggers via `event_emit`: 1. Parse the report: vulnerability type, affected endpoint, steps to reproduce, claimed severity 2. `grep` the codebase to check if the endpoint exists in current code (it might be stale or already removed) 3. Attempt to reproduce the vulnerability in a sandboxed environment using `shell`: - Set up the minimal conditions described - Execute the reproduction steps - Capture evidence (HTTP requests, responses, error output) 4. Score severity against actual attack surface using `read_file` on config/auth files: - Is this endpoint publicly exposed or internal-only? - Does it require authentication? What privilege level? - What data is accessible if exploited? - Is the exploit reliable or conditional? 5. `memory_write` to save the triage result at bounty/triage/[REPORT-ID].md with full analysis 6. Draft the initial response to the researcher "🎯 Bug Bounty Triage — [REPORT-ID] **Claimed:** [XSS on /api/v2/users/profile] **Reproduced:** ✅ Yes (or ❌ No — reason) **Actual severity:** High (claimed: Critical) **Reasoning:** Endpoint requires authenticated user, no admin escalation possible. PII accessible but limited to own profile. Downgraded from Critical because no unauthenticated access. **Evidence:** - Request: [curl command] - Response: [sanitized output] - Impact: reflected XSS executes in user's own session, no cross-user impact **Suggested response to researcher:** 'Thank you for the report. We have confirmed the vulnerability. Based on our impact assessment, we are classifying this as High severity. The bounty for High severity findings on our program is [$X].' **Priority queue position:** 2 of 7 open reports (1 Critical above this)" === COMMANDS === "triage: queue" — `memory_search` for all open reports sorted by actual severity "triage: respond [ID] accept/reject/need-info" — `memory_write` to log decision, `gmail` tool to send response "triage: duplicate [ID1] of [ID2]" — `memory_write` to mark as duplicate and link to original report "show bounty stats" — `memory_search` for total reports, triage rate, severity distribution "bounty: out of scope [ID]" — `memory_write` to mark as out of scope with reason, `gmail` to draft rejection - [Build a complete travel itinerary](https://hub.ironclaw.com/usecases/build-a-complete-travel-itinerary.md) — Plan a 10-day Japan trip for two adults under 4,00,000 INR. - [Build onboarding packs for new hires](https://hub.ironclaw.com/usecases/build-onboarding-packs-for-new-hires.md) — "Create an onboarding pack for our new designer: accounts to create, docs to read, people to meet, and first-week tasks." - [Changelog to Release Comms Pipeline — Repo tags a release, agent drafts release notes, customer email, and tweet thread](https://hub.ironclaw.com/usecases/changelog-to-release-comms-pipeline-repo-tags-a-release-agent-drafts-release-notes-customer-emai.md) — You are my release communications pipeline. When a new tag lands in the repo, you diff the changelog, draft release notes for three different audiences, and present them for review. When I say "release: [version]" or when a new tag webhook triggers via `event_emit`: 1. Find the previous tag and diff: `shell` runs `git log [prev]..[new]` 2. Categorize every commit/PR using `github` tool for PR details: - Breaking changes - New features - Bug fixes - Performance improvements - Internal/refactor (skip in public notes) 3. `memory_search` for releases/style-guide.md — the project's past tone and format 4. `memory_search` for releases/past-releases/ — consistency with previous notes 5. Generate three outputs: **A. GitHub Release Notes** (technical audience): Release notes format: **[version] — [date]** Breaking Changes: [description with migration path] New Features: [description with PR link] Bug Fixes: [description with PR link] Contributors: [list of contributors] **B. Customer Email** (non-technical, sent via `gmail` tool): Subject: What's new in [Product] [version] [2-3 sentence executive summary] [Feature in customer language, not commit language] [Fix in terms of the problem it solves, not the technical cause] **C. Tweet Thread** (sent via `message` for review): ``` 🚀 [Product] [version] is live. [Hook — the single most exciting thing] 🧵 Thread: 1/ [Feature 1 with emoji] 2/ [Feature 2 with emoji] [Try it: link] ``` 6. `memory_write` to save all drafts at releases/[version]/ 7. `message` to send to Telegram for review: "📦 Release [version] ready for review 3 drafts generated: GitHub release notes, customer email, tweet thread. Reply 'ship github' / 'ship email' / 'ship all' to publish, or send edits." When I reply "ship all": - `github` tool to create the GitHub release with notes - `gmail` tool to send the customer email to the mailing list - `memory_write` to log the published release === COMMANDS === "release: preview [version]" — show drafts without publishing "release: edit tone [more technical/less technical/more casual]" — `memory_write` to update style guide for next release "release: history" — `memory_search` for past releases with links to published notes - [Cold Outreach Researcher — Research a prospect, draft outreach referencing the last 48 hours](https://hub.ironclaw.com/usecases/cold-outreach-researcher-research-a-prospect-draft-outreach-referencing-the-last-48-hours.md) — You are my cold outreach researcher. When I give you a prospect's name, you research them, find something timely to reference, and draft a personalized message that proves I did my homework — not a template with [FIRST_NAME] swapped in. When I say "outreach: [person] at [company]" — for example "outreach: Vitalik Buterin at Ethereum Foundation": 1. Search for the person's recent activity using `web-search` (Brave): - Recent tweets/posts (last 30 days) - Recent blog posts or articles - Recent talks, podcasts, or interviews - Recent company news (fundraise, product launch, hires) 2. Fetch detailed content using `llm-context` (Brave) for the top 3-5 most relevant results 3. Search for company context using `web-search`: - Recent funding round or financial events - Product launches or major releases - Press coverage or news mentions - Open positions (signals about priorities) 4. `memory_search` for outreach/history.md — past interactions with this person or company 5. Identify 2-3 specific, timely angles — things from the last 48 hours or last week 6. Draft 3 outreach variants (short email, LinkedIn message, tweet reply) 7. `memory_write` to save research and drafts at outreach/prospects/[name].md "🎯 Outreach Research — [person] at [company] **Profile:** [title, role, focus area] **Timely hooks (last 7 days):** - Posted about [topic] on [date] — specifically mentioned [detail] - Company announced [event] on [date] - Gave talk at [event] about [topic] on [date] **Past interactions:** None found (or: "emailed 3 months ago, no response") **Draft 1 — Email (via `gmail` tool):** Subject: [specific reference to their recent work] [first sentence references the specific post/talk/news item] [2-3 sentences on why I'm reaching out and what's relevant to them] [clear ask with low commitment] **Draft 2 — LinkedIn (shorter):** [similar angle, 3 sentences max] **Draft 3 — Twitter reply:** [draft reply to their specific recent tweet] **Recommended angle:** [which hook is strongest and why]" === COMMANDS === "outreach: batch [names]" — research multiple prospects and queue drafts via `create_job` "outreach: sent [person]" — `memory_write` to log sent outreach, start follow-up timer via `routine_create` "outreach: follow-up [person]" — `memory_search` for original message, draft follow-up "outreach: track" — `memory_search` for all sent outreach with response status "outreach: stats" — `memory_search` for response rate by channel, response rate by angle type - [Compare prices before I buy](https://hub.ironclaw.com/usecases/compare-prices-before-i-buy.md) — "I want to buy this phone. Compare prices across stores and tell me when it drops below ₹35,000." - [Competitor Page Watcher — Get alerted the day a competitor changes their pricing, jobs, or product](https://hub.ironclaw.com/usecases/competitor-page-watcher-get-alerted-the-day-a-competitor-changes-their-pricing-jobs-or-product.md) — You are my competitor page watcher. You monitor specific web pages and tell me the day anything changes — pricing, job postings, product updates. When I say "watch: [URL] — [what it is]" — for example "watch: https://competitor.com/pricing — their pricing page": 1. Fetch the URL using the http tool 2. Extract the meaningful text content (ignore navigation, footers, scripts) 3. Read memory at competitors/pages.md using memory_read 4. Save: URL, label, a summary of the current content, key facts (prices, plan names, job titles, etc.), date captured 5. Write back to memory 6. Confirm: "Now watching [label]. I'll check daily and alert you on any change." Create a routine that runs every day at 8:00 AM: 1. Read memory at competitors/pages.md 2. For each watched page: - Fetch the current URL content - Extract meaningful text - Compare against the saved version in memory 3. If meaningful content changed, identify WHAT changed specifically: - Price changes (old value → new value) - New or removed plan tiers - New or removed job postings - New product features or announcements 4. Update the saved version in memory with the new content 5. If any page changed, send Telegram alert: "🔍 Competitor Change Detected — [date] 📄 [Competitor pricing page] Changed: - Pro plan: $49/mo → $59/mo (price increase) - New tier added: 'Enterprise' at $199/mo 📄 [Competitor careers page] Changed: - New posting: 'Head of Sales' — they're building a sales team - Removed: 'Junior Developer' 💡 What this might signal: [one-line interpretation per change]" 6. If nothing changed on any page: reply HEARTBEAT_OK and stop. === COMMANDS === "show watched pages" — list all monitored URLs with last-change date "stop watching [URL]" — remove a page from monitoring "check [URL] now" — force an immediate check of one page - [Convert documents and images to PDF locally](https://hub.ironclaw.com/usecases/convert-documents-and-images-to-pdf-locally.md) — "Convert these three scanned JPEGs into a single PDF, and also turn this Word document into a PDF — all without uploading anything to an external service." - [Convert SVG vector files to PNG](https://hub.ironclaw.com/usecases/convert-svg-vector-files-to-png.md) — "Convert this SVG logo to a PNG at 2x resolution with a transparent background." - [Create a personal emergency folder](https://hub.ironclaw.com/usecases/create-a-personal-emergency-folder.md) — "Help me create an emergency folder with important documents, contacts, medical info, and instructions." - [Create event marketing image](https://hub.ironclaw.com/usecases/create-event-marketing-image.md) — "Create an X post image promoting the Barcelona IronClaw Hackathon taking place June 12–18. Include key IronClaw capabilities — AI Agents, Skills, MCP Support, Memory & Knowledge, Workflow Automation, Secure Vaults, Integrations, and Web3. Use IronClaw branding, Barcelona visuals, and optimize for social media engagement." - [Cross-Exchange Spread Monitor — Detect arbitrage and exchange problems before the crowd](https://hub.ironclaw.com/usecases/cross-exchange-spread-monitor-detect-arbitrage-and-exchange-problems-before-the-crowd.md) — You are my cross-exchange spread monitor for NEAR. Alert me when price differences between exchanges exceed 1.5% — this signals either an arbitrage opportunity or problems on one of the exchanges. Create a routine that runs every 2 hours: 1. Fetch NEAR price from 3 exchanges: Binance: https://api.binance.com/api/v3/ticker/price?symbol=NEARUSDT Bybit: https://api.bybit.com/v5/market/tickers?category=spot&symbol=NEARUSDT Coinbase: https://api.coinbase.com/v2/prices/NEAR-USD/spot 2. Calculate spread between every pair: - Binance vs Bybit - Binance vs Coinbase - Bybit vs Coinbase Spread % = (higher price - lower price) / lower price * 100 3. Read memory at spreads/history.md using memory_read. If file does not exist, create it with today's first snapshot as baseline. Append current snapshot: - Timestamp - All 3 prices - Max spread % and which pair 4. If ANY spread >= 1.5%: send Telegram alert: "⚡ NEAR Cross-Exchange Spread Alert [EXCHANGE A]: $[price] [EXCHANGE B]: $[price] Spread: [X]% [If one exchange is significantly higher:] 💡 [Exchange] premium — demand surge on that exchange OR withdrawal issues on the cheaper one. If withdrawals are paused somewhere, the price gap cannot be arbitraged away — that's why it persists. Check exchange status before trading. [Arbitrage angle:] 💰 Theoretical arb: buy on [cheaper exchange] at $[X], sell on [expensive exchange] at $[X] = [X]% before fees ⏰ [timestamp] Normal spread for NEAR: <0.5%" 5. Write updated snapshot to spreads/history.md using memory_write. If all spreads < 1.5%: reply HEARTBEAT_OK and stop. - [Crypto Hypothesis Tester — Automatically validate your market theories over time](https://hub.ironclaw.com/usecases/crypto-hypothesis-tester-automatically-validate-your-market-theories-over-time.md) — You are my crypto hypothesis tester. I want to test this hypothesis: "When ETH price rises more than 3% in 24h, NEAR price rises within the next 48 hours" Do the following: 1. Read memory at hypotheses/eth-near-correlation.md using memory_read. If file does not exist, create it with: # Hypothesis: ETH pump → NEAR follows within 48h - Hypothesis: When ETH rises >3% in 24h, NEAR rises within 48h - Status: TESTING - Started: [today's date] - Observations: [] - Confirmed: 0 - Refuted: 0 2. Fetch current prices from CoinGecko: https://api.coingecko.com/api/v3/simple/price?ids=ethereum,near&vs_currencies=usd&include_24hr_change=true 3. Apply hypothesis logic: - If ETH 24h change > +3%: log as TRIGGER EVENT with today's date and ETH price - If a TRIGGER EVENT was logged 48h ago: check if NEAR price is now higher than at trigger time - If YES: mark as CONFIRMED, increment confirmed counter - If NO: mark as REFUTED, increment refuted counter 4. Write updated log back to memory at hypotheses/eth-near-correlation.md 5. Every Sunday send me a Telegram summary: "🧪 Hypothesis Test Report Theory: ETH pump → NEAR follows within 48h Status: TESTING (day [X] of 30) Results so far: ✅ Confirmed: [X] times ❌ Refuted: [X] times 📊 Hit rate: [X]% Last trigger: [date] — ETH +[X]% → NEAR [outcome] Verdict: [PROMISING if >60% / INCONCLUSIVE if 40-60% / REJECTED if <40%]" Create a routine that runs every 6 hours to check prices and update the hypothesis log, and sends the weekly report every Sunday at 10:00 AM. - [Crypto Macro Confluence Monitor — Alert when Fear & Greed, BTC dominance and funding rates align](https://hub.ironclaw.com/usecases/crypto-macro-confluence-monitor-alert-when-fear-greed-btc-dominance-and-funding-rates-align.md) — You are my crypto macro signal monitor. Check 4 independent indicators and alert me only when multiple signals align. Create a routine that runs every day at 7:00 PM UTC: 1. Fetch Fear & Greed Index: https://api.alternative.me/fng/?limit=1 Extract: value (0-100) and value_classification 2. Fetch BTC Dominance: https://api.coingecko.com/api/v3/global Extract: market_cap_percentage.btc 3. Fetch BTC Funding Rate: https://fapi.binance.com/fapi/v1/fundingRate?symbol=BTCUSDT&limit=1 Extract: fundingRate (multiply by 100 for %) 4. Fetch ETH Funding Rate: https://fapi.binance.com/fapi/v1/fundingRate?symbol=ETHUSDT&limit=1 Extract: fundingRate (multiply by 100 for %) 5. Apply signal logic: Fear & Greed: - Value < 25 = EXTREME FEAR (bullish contrarian signal) - Value > 75 = EXTREME GREED (bearish contrarian signal) - Otherwise = NEUTRAL BTC Dominance: - > 60% = BTC DOMINANCE HIGH (alts under pressure, bearish for alts) - < 50% = ALTSEASON (capital rotating to alts, bullish) - Otherwise = NEUTRAL BTC Funding Rate: - > 0.05% = OVERLEVERAGED LONGS (bearish, long squeeze risk) - < -0.03% = OVERLEVERAGED SHORTS (bullish, short squeeze likely) - Otherwise = NEUTRAL ETH Funding Rate: - > 0.05% = OVERLEVERAGED LONGS (bearish) - < -0.03% = OVERLEVERAGED SHORTS (bullish) - Otherwise = NEUTRAL 6. Count how many signals point in the same direction: BEARISH confluence: - Extreme Greed + BTC Dominance High + BTC Overleveraged Longs + ETH Overleveraged Longs BULLISH confluence: - Extreme Fear + Altseason + BTC Overleveraged Shorts + ETH Overleveraged Shorts 7. Send Telegram ONLY if 3 or more signals align: "🎯 Macro Signal Confluence — [Date] [BULLISH/BEARISH] — [X] of 4 signals aligned 😱 Fear & Greed: [value] ([classification]) — [signal] 📊 BTC Dominance: [X]% — [signal] ₿ BTC Funding: [X]% — [signal] Ξ ETH Funding: [X]% — [signal] 🧠 Interpretation: [what this combination historically means] ⚡ Signal: [STRONG BUY / STRONG SELL / REDUCE LEVERAGE / ADD EXPOSURE]" 8. If fewer than 3 signals align: reply HEARTBEAT_OK and stop. - [Crypto Sector Rotation Radar](https://hub.ironclaw.com/usecases/crypto-sector-rotation-radar.md) — Fetch CoinGecko coin categories with their 24h market cap change. Rank the top 5 and bottom 5 sectors and tell me where capital is rotating in and out of right now. - [Crypto Trade Journal with Live P&L Tracker](https://hub.ironclaw.com/usecases/crypto-trade-journal-with-live-p-l-tracker.md) — You are my personal crypto trade journal. Your job is to log trades I tell you about, and give me P&L analysis when I ask. When I say something like "bought 500 NEAR at $3.20" or "sold 200 SOL at $145": 1. Use the time tool to get the current date and time 2. Read memory at trades/journal.md (use memory_read) 3. Append the new trade entry in this exact format: [DATE] | BUY/SELL | AMOUNT TOKEN | @ $PRICE | Total: $TOTAL_USD 4. Write the updated file back with memory_write to trades/journal.md 5. Confirm: "Logged: bought 500 NEAR @ $3.20 on [date]" When I say "show my trades" or "show my history": 1. Read memory at trades/journal.md with memory_read 2. Display all entries as a clean table When I say "show P&L" or "how am I doing": 1. Read memory at trades/journal.md with memory_read 2. For each token I hold (more buys than sells), figure out my average entry price and total position size 3. Fetch current prices from CoinGecko: https://api.coingecko.com/api/v3/simple/price?ids=COIN_IDS&vs_currencies=usd (map token names to CoinGecko IDs yourself — NEAR=near, SOL=solana, BTC=bitcoin, ETH=ethereum, etc.) 4. Show a table: Token | Avg Entry | Current Price | Size | Cost Basis | Current Value | P&L $ | P&L % 5. Show total portfolio: total invested, total current value, total P&L When I say "show P&L for NEAR" (specific token): - Same as above but only for that token, plus show all individual trade entries for it Rules: - Never overwrite trades/journal.md from scratch — always read first, then append - If trades/journal.md does not exist yet, create it with a header line: "# Trade Journal\n\n" then add the entry - For sells: reduce the open position, don't just log and ignore - If I give you a token not on CoinGecko, just skip the price fetch for that one and note it as "price unavailable" - When calculating average entry: weight by size, not just simple average - [Daily Crypto Briefing — NEAR price, portfolio P&L and Reddit news every morning](https://hub.ironclaw.com/usecases/daily-crypto-briefing-near-price-portfolio-p-l-and-reddit-news-every-morning.md) — You are my daily crypto briefing agent. Every morning at 9 AM UTC send me a Telegram digest. Do the following steps: 1. Fetch NEAR price from CoinCap: https://api.coincap.io/v2/assets/near-protocol Extract: current price, 24h change %, 24h volume 2. Fetch prices for my other positions: https://api.coincap.io/v2/assets/hyperliquid https://api.coincap.io/v2/assets/zcash 3. Read my portfolio from memory at defi/portfolio.md using memory_read. If file does not exist, create it with: # My Portfolio - NEAR: 1000 tokens, entry price $2.00 - HYPE: 50 tokens, entry price $50.00 - ZEC: 1 token, entry price $400.00 4. Fetch latest NEAR news from Reddit RSS: https://www.reddit.com/r/nearprotocol/new.rss Extract top 3 post titles. 5. Calculate P&L for each position: - Current Value = holdings * current price - P&L % = (current price - entry price) / entry price * 100 6. Send Telegram message in this format: "🌅 Daily Crypto Briefing — [Date] ═══════════════════════════════════ 📊 NEAR/USDT PRICE NEAR: $[price] ([24h change]%) 24h Volume: $[volume] ═══════════════════════════════════ 📰 TOP 3 NEAR NEWS (Reddit) 1. [title] 2. [title] 3. [title] ═══════════════════════════════════ 💼 YOUR POSITIONS Token | Entry | Current | Value | P&L NEAR | $2.00 | $[x] | $[x] | [x]% HYPE | $50 | $[x] | $[x] | [x]% ZEC | $400 | $[x] | $[x] | [x]% 📈 Total Invested: $[x] 📈 Current Value: $[x] 📈 Total P&L: $[x] ([x]%) ═══════════════════════════════════ 🔔 Next briefing: Tomorrow 9 AM UTC" Create a routine that runs every day at 9:00 AM UTC and executes this entire briefing automatically. - [Daily inbox summarizer with reply drafts and to-dos, delivered where you work](https://hub.ironclaw.com/usecases/daily-inbox-summarizer-with-reply-drafts-and-to-dos-delivered-where-you-work.md) — Every morning, summarize my inbox, flag what needs a reply, and post the digest (with draft replies for the urgent ones) to the channel I pick. - [Daily morning digest that pulls from GitHub, Telegram, and Slack and sends one AI summary to the channel you pick](https://hub.ironclaw.com/usecases/daily-morning-digest-that-pulls-from-github-telegram-and-slack-and-sends-one-ai-summary-to-the-c.md) — Every morning at the time I pick, send me one tight digest summarising what happened overnight across my GitHub repos, the Telegram chats and groups I care about, and the Slack channels I care about — all rolled into a single message with a TL;DR on top and a per-source section below. - [Daily or weekly AI email digest pushed to Slack or email — stay on top of your inbox without reading everything](https://hub.ironclaw.com/usecases/daily-or-weekly-ai-email-digest-pushed-to-slack-or-email-stay-on-top-of-your-inbox-without-readi.md) — Every weekday morning, give me a short AI digest of the important emails from the last 24 hours — 3 to 7 items max, each with a one-line summary and a "needs action" flag — and send it to me in Slack (or email, whatever I pick). - [Daily positive news & insight briefer for a better-mood workday](https://hub.ironclaw.com/usecases/daily-positive-news-insight-briefer-for-a-better-mood-workday.md) — Every weekday morning, send me a short briefer of 3–5 positive, constructive news and insights (mix of general web + tech/industrial parks), so I can start the day in a good headspace instead of doomscrolling. - [DCA into NEAR](https://hub.ironclaw.com/usecases/dca-into-near.md) — Buy $50 worth of NEAR whenever the price falls below $2 - [Decision Journal with Follow-up — Log decisions, get confronted with results later](https://hub.ironclaw.com/usecases/decision-journal-with-follow-up-log-decisions-get-confronted-with-results-later.md) — You are my decision journal. Your job is to log my decisions and come back to me later with a reality check — because people remember their wins and forget their mistakes, and never actually learn from their own track record. When I say "decision: [description] | check in [timeframe]" — for example: "decision: rejected the job offer from company X, staying at current job | check in 3 months" "decision: not buying the MacBook at $2000, waiting for a discount | check in 1 month" "decision: I predict project Y shuts down within a year | check in 12 months" 1. Read memory at decisions/journal.md using memory_read 2. Add the decision with: description, date logged, follow-up date, status PENDING 3. Write back to memory and confirm: "Logged. I'll come back to you on [date] and we'll see how this aged." Create a routine that runs every day at 10:00 AM: 1. Read all decisions from decisions/journal.md 2. Check if any decision's follow-up date is today or has passed 3. For each due decision, send a Telegram message: "📓 Decision Follow-up On [date] you decided: '[exact decision text]' It's been [X weeks/months]. Time for the verdict: - Was this the right call? - What actually happened? Reply 'verdict: [right/wrong/mixed] — [short note]' and I'll log the outcome." When I reply with a verdict: 1. Update the decision status to RESOLVED with the verdict and note 2. Recalculate my overall stats 3. Reply with my updated track record: "Logged. Your track record so far: ✅ Right: [X] decisions ❌ Wrong: [X] decisions ➗ Mixed: [X] decisions [If a pattern is visible:] 📊 Pattern: [e.g. 'your purchase-related decisions are right 80% of the time, but your predictions about people are mostly wrong']" When I say "show my stats": display full track record grouped by decision type with hit rates. If no follow-ups are due: reply HEARTBEAT_OK and stop. - [Deep codebase explorer that explains why code works, with on-demand doc export](https://hub.ironclaw.com/usecases/deep-codebase-explorer-that-explains-why-code-works-with-on-demand-doc-export.md) — Point this at a repo (GitHub URL or local path) and ask it things like "why does this auth flow work this way?" — get a clear inline answer, then optionally save it as Markdown docs. - [DeFi Portfolio Monitor — Daily risk assessment and yield opportunities via DefiLlama](https://hub.ironclaw.com/usecases/defi-portfolio-monitor-daily-risk-assessment-and-yield-opportunities-via-defillama.md) — You are my DeFi portfolio monitor. First, read my portfolio from memory at defi/portfolio.md using memory_read. If the file does not exist, create it with this example portfolio and confirm: # My DeFi Portfolio - Lido: 2 ETH staked - Aave: $500 USDC supplied - Uniswap: $300 ETH/USDC LP Then do the following: 1. For each protocol in my portfolio, fetch its current data from DefiLlama: https://api.llama.fi/protocol/[protocol-slug] (slugs: lido, aave, uniswap-v3 — map protocol names to slugs yourself) Extract for each: - Current TVL - TVL change 24h (%) - TVL change 7d (%) 2. Fetch the top 5 stablecoin yields from: https://yields.llama.fi/pools Filter only pools where: - stablecoin = true - apy > 5% - tvlUsd > 1000000 3. Based on the data, evaluate risk for each of my positions: - TVL dropped >20% in 7d = HIGH RISK - TVL dropped 10-20% in 7d = MEDIUM RISK - TVL stable or growing = LOW RISK 4. Send me a Telegram message in this format: "📊 DeFi Portfolio Report [Protocol] — [my position] 💰 TVL: $[X]B | 24h: [X]% | 7d: [X]% ⚠️ Risk: [LOW/MEDIUM/HIGH] 💡 [one line recommendation] --- 🌾 Best yield opportunities right now: 1. [Pool] — [APY]% APY | TVL: $[X]M 2. [Pool] — [APY]% APY | TVL: $[X]M 3. [Pool] — [APY]% APY | TVL: $[X]M Overall portfolio health: [GOOD/WATCH/DANGER]" Create a routine that runs every day at 9:00 AM and executes this entire portfolio check automatically. - [Dependency CVE Sentinel — Alerts that tell you if you're actually vulnerable, not just that a CVE exists](https://hub.ironclaw.com/usecases/dependency-cve-sentinel-alerts-that-tell-you-if-you-re-actually-vulnerable-not-just-that-a-cve-e.md) — You are my dependency security analyst. Dependabot spams me with CVEs — most are in transitive deps I never call. Your job is to tell me which ones actually matter for my codebase. When I say "cve: check" or when a new CVE alert fires: 1. Read the current dependency tree: `shell` runs `npm ls`, `cargo tree`, or `go mod graph` (whichever exists in the workspace) 2. Fetch the CVE details using `web-search` (Brave): affected versions, severity, attack vector, what function/path is vulnerable 3. Read the actual source code that imports the affected dependency using `read_file` and `grep` 4. Determine if the vulnerable codepath is reachable: - Does my code call the affected function/class directly? - Does any direct dependency call it in a way my code triggers? - Is it only reachable through a feature flag or optional import I don't use? 5. Classify each CVE into one of four buckets: - **EXPLOITABLE** — vulnerable codepath is directly reachable in my code - **CONDITIONAL** — reachable only through a specific feature flag, config, or indirect path - **TRANSITIVE NOISE** — the dep is in my tree but the vulnerable path is never called - **NOT AFFECTED** — my version is outside the affected range 6. Save the triage result to workspace memory using `memory_write` at security/cve-triage.md with date, CVE ID, and verdict 7. Send Telegram alert via `message` only for EXPLOITABLE and CONDITIONAL items "🔒 CVE Triage — [date] 🔴 EXPLOITABLE: - CVE-2026-1234 (lodash <4.17.22, CVSS 9.1) — prototype pollution in mergeWith, called directly in src/utils/deepMerge.ts:42. Upgrade lodash to >=4.17.22. 🟡 CONDITIONAL: - CVE-2026-5678 (express <4.19, CVSS 7.5) — open redirect in res.redirect, reachable only if admin panel feature flag is enabled (currently disabled). Low urgency. ⚪ TRANSITIVE NOISE (4 CVEs silenced): lodash, express, axios, debug. None reach vulnerable codepaths in this project. Full list in security/cve-triage.md." Create a `routine` that runs every Monday at 7:00 AM via `routine_create`: 1. `shell` to check for new CVEs affecting the project's dependencies (npm audit, cargo audit, etc.) 2. Re-run reachability analysis for any new findings 3. `message` to alert only on actionable items === COMMANDS === "cve: status" — `memory_search` for current triage summary with counts by bucket "cve: detail [CVE-ID]" — `memory_search` for full analysis of a specific CVE "cve: false positive [CVE-ID]" — `memory_write` to mark as permanently ignored with reason "show cve history" — `memory_search` for all triaged CVEs with dates and verdicts - [Draft polite replies to difficult messages](https://hub.ironclaw.com/usecases/draft-polite-replies-to-difficult-messages.md) — "Help me reply politely to this angry customer / landlord / friend message." - [Explain official letters in simple language](https://hub.ironclaw.com/usecases/explain-official-letters-in-simple-language.md) — "Explain this bank/government/insurance letter in simple language and tell me what action I need to take." - [Extract risky contract clauses](https://hub.ironclaw.com/usecases/extract-risky-contract-clauses.md) — "Read these vendor contracts and flag any auto-renewal, termination, liability, exclusivity, and payment penalty clauses." - [Extract subtitles from YouTube videos](https://hub.ironclaw.com/usecases/extract-subtitles-from-youtube-videos.md) — "Download just the English subtitles from this YouTube video as an SRT file, without downloading the video itself." - [Fetch GitHub trending every day, filter it by your interests and language, and get a tight digest of 5-10 repos with trend reasoning — plus ready-to-post social angles](https://hub.ironclaw.com/usecases/fetch-github-trending-every-day-filter-it-by-your-interests-and-language-and-get-a-tight-digest.md) — Every morning, give me today's GitHub trending — 5-10 repos that fit my interests (AI agents, developer tools, Rust, crypto) — with a one-line "why it's trending" per repo, and for each one a one-sentence social-post angle I can turn into a tweet or LinkedIn post. - [Flight Price Monitor — Alert when ticket prices drop below your target](https://hub.ironclaw.com/usecases/flight-price-monitor-alert-when-ticket-prices-drop-below-your-target.md) — You are my flight price monitor. My API token for Aviasales is stored in memory at travel/aviasales-token.md Every day at 10:00 AM, do the following: 1. Read my watchlist from memory at travel/watchlist.md using memory_read. If file does not exist, create it with this example: # Flight Watchlist - Route: TBS → HKT (Tbilisi to Phuket), alert if price drops below $400 - Route: TBS → BKK (Tbilisi to Bangkok), alert if price drops below $350 2. For each route, fetch the cheapest ticket price from Aviasales API: https://api.travelpayouts.com/v1/prices/cheap?origin=[ORIGIN]&destination=[DESTINATION]¤cy=usd&token=[YOUR_TOKEN] Extract the minimum price available. 3. Read previous prices from memory at travel/price-history.md using memory_read. Compare current price with yesterday's price for each route. 4. Apply this logic: - If current price is BELOW my alert threshold: send Telegram alert immediately - If current price dropped more than 15% vs yesterday: send Telegram alert - Otherwise: silent run, just update price history 5. Send Telegram alert in this format when triggered: "✈️ Flight Price Alert! [ORIGIN] → [DESTINATION] 💰 Current price: $[X] 📉 Yesterday: $[X] ([change]%) 🎯 Your target: $[X] 👉 Book now: https://www.aviasales.com/search/[ORIGIN][DESTINATION]" 6. Write updated prices to memory at travel/price-history.md using memory_write. If no alerts triggered: reply HEARTBEAT_OK and stop. Create a routine that runs every day at 10:00 AM and executes this automatically. - [Follow source Telegram channels, rewrite each post in your target language and voice, batch them into a digest, and post to your owned target channel — with a hard no-spam throttle](https://hub.ironclaw.com/usecases/follow-source-telegram-channels-rewrite-each-post-in-your-target-language-and-voice-batch-them-i.md) — Follow these 3 Russian news channels, rewrite the posts in English in a casual / social tone with a "📰" header and a source-attribution footer, batch them into one digest post every 3 hours, and publish to my owned English-language channel. No spam. I can tweak the voice and the format per source-target pair. - [Free Trial Guardian — Never get charged for a forgotten trial again](https://hub.ironclaw.com/usecases/free-trial-guardian-never-get-charged-for-a-forgotten-trial-again.md) — You are my free trial guardian. Save my trials in memory and alert me before they charge me. When I say "trial: [service], [days] days" — for example "trial: Netflix, 14 days": 1. Read memory at trials/active.md using memory_read 2. Add entry: service name, start date (today), end date (today + days), status ACTIVE 3. Write back to memory 4. Confirm: "Saved. I'll warn you on [end date - 2 days]." Create a routine that runs every day at 9:00 AM: 1. Read memory at trials/active.md 2. Get today's date using the time tool 3. For each ACTIVE trial calculate days remaining 4. Apply logic: - 2 days remaining: send URGENT alert - 5 days remaining: send WARNING - 0 days or less: mark as EXPIRED, send final alert 5. Send Telegram only if something needs attention: "⚠️ Free Trial Alert 🔴 EXPIRES IN 2 DAYS: - Netflix — ends [date]. Cancel now or you'll be charged. 🟡 EXPIRES IN 5 DAYS: - Spotify — ends [date]. Decide soon. ⛔ JUST EXPIRED: - Adobe — trial ended [date]. Check if you were charged." If nothing urgent: reply HEARTBEAT_OK and stop. - [Get a 24-hour Telegram warning before any token unlock hits the market](https://hub.ironclaw.com/usecases/get-a-24-hour-telegram-warning-before-any-token-unlock-hits-the-market.md) — Here is the AVAX (Avalanche) token unlock schedule: - June 9, 2026 — 1,670,000 AVAX — ~$11.34M — 0.23% of total supply — Team/Investor vesting - September 9, 2026 — 1,670,000 AVAX — ~$11.34M — 0.23% of total supply — Team/Investor vesting - December 9, 2026 — 1,670,000 AVAX — ~$11.34M — 0.23% of total supply — Team/Investor vesting - March 9, 2027 — 1,670,000 AVAX — ~$11.34M — 0.23% of total supply — Team/Investor vesting - June 9, 2027 — 1,670,000 AVAX — ~$11.34M — 0.23% of total supply — Team/Investor vesting Save each event to memory, show me a clean table with price impact verdict, and set up a daily routine that automatically pings me in Telegram 24 hours before any unlock — without me having to ask every time. Image - [GitHub Issues / PRs / Review requests from any repo → instant notification in Slack or Telegram, optional task creation in Todoist / Trello / Notion / Asana](https://hub.ironclaw.com/usecases/github-issues-prs-review-requests-from-any-repo-instant-notification-in-slack-or-telegram-option.md) — Watch my GitHub repos and push every new issue, opened PR, PR review request, and merged or closed PR to my Slack channel (or Telegram, or both) with a short AI summary — and if a thread is labeled as actionable, also create a task for me in Todoist / Trello / Notion / Asana. - [Grant/Funding Deadline Tracker — Monitor grant databases, match eligibility, alert on deadlines](https://hub.ironclaw.com/usecases/grant-funding-deadline-tracker-monitor-grant-databases-match-eligibility-alert-on-deadlines.md) — You are my grant and funding deadline tracker. You monitor grant databases, match them against my project profiles, and alert me before deadlines so I never miss free money. When I say "profile: [project description]" — for example "profile: open-source NEAR Protocol wallet, Firefox extension, self-custody, privacy-focused": 1. `memory_search` for grants/profiles.md 2. `memory_write` to save the project profile with key attributes: tech stack, ecosystem, stage, team size, location, open-source status 3. Immediately scan known grant sources for matches (see routine below) Create a `routine` via `routine_create` that runs every Wednesday at 9:00 AM: 1. `memory_search` for all project profiles from grants/profiles.md 2. Scan grant sources using `web-search` (Brave) and `llm-context` (Brave): - NEAR Foundation grants (near.org/grants) - Web3 Foundation grants (w3f.io) - Ethereum Foundation grants (ethereum.org) - Gitcoin Grants rounds - EU Horizon Europe calls matching tech keywords - US SBIR/STTR matching project keywords - Mozilla MOSS grants (if open-source) - Solana Foundation, Polygon grants (if applicable) - Devfolio hackathons with prize pools 3. For each new grant found: - Check eligibility against project profiles - Calculate match score (0-100) based on: topic alignment, tech stack match, geographic eligibility, stage fit, deadline proximity - `memory_write` to save at grants/opportunities/[grant-id].md 4. `message` to send Telegram alert for high-match opportunities (score > 60): "💰 Grant Opportunity — [match score: 85/100] **Grant:** [name] **Source:** [NEAR Foundation / EU Horizon / etc.] **Amount:** [$X - $Y] **Deadline:** [date] ([X days away]) **Eligibility:** [requirements] **Why it matches your profile:** - [specific alignment point 1] - [specific alignment point 2] **Required materials:** - [ ] Project description (max [X] words) - [ ] Team background - [ ] Budget proposal - [ ] Technical architecture doc - [ ] Timeline / milestones **Pre-drafted application outline:** 1. Problem statement: [auto-drafted from profile] 2. Solution: [auto-drafted] 3. Technical approach: [auto-drafted from tech stack] 4. Budget breakdown: [template] 5. Timeline: [template] Reply 'start application [grant-id]' to begin drafting." If nothing new: no message (silent heartbeat). === COMMANDS === "grants: list" — `memory_search` for all tracked opportunities sorted by deadline "grants: apply [id]" — begin drafting an application using the project profile from `memory_search` "grants: submitted [id]" — `memory_write` to mark as submitted, log the date and amount requested "grants: won [id]" — `memory_write` to mark as awarded, log amount and start date "grants: stats" — `memory_search` for total applied, total awarded, total funding received, success rate - [Hacker News AI Agents Digest — Weekly top discussions delivered to Telegram](https://hub.ironclaw.com/usecases/hacker-news-ai-agents-digest-weekly-top-discussions-delivered-to-telegram.md) — Fetch this URL using the http tool: https://hn.algolia.com/api/v1/search?query=AI+agents&tags=story&hitsPerPage=10&numericFilters=points>50 Parse the JSON response. For each story extract: - Title - Points - Date - URL Sort by points (highest first). Take top 5. Then send me a Telegram message in this format: "🗞 Hacker News — AI Agents Digest 1. [Title] ⬆️ [Points] points · [Date] 🔗 [URL] 2. [Title] ⬆️ [Points] points · [Date] 🔗 [URL] (repeat for all 5 stories) 💬 Most discussed: [title of #1 post]" If you want this automatically every week, create a routine that runs every Monday at 9:00 AM with this exact task. - [Hyperliquid Market Data](https://hub.ironclaw.com/usecases/hyperliquid-market-data.md) — "Show me the top funding rates on Hyperliquid right now, and check the open positions for my wallet 0xabc...123." - [Index every text message in your Telegram Saved Messages, then ask the agent to find, summarise, or "chat with" your past notes — no cloud upload, no attachment indexing, no write back](https://hub.ironclaw.com/usecases/index-every-text-message-in-your-telegram-saved-messages-then-ask-the-agent-to-find-summarise-or.md) — Index all the text I've ever saved into my Telegram Saved Messages so I can ask things like "3 months ago what did I save about NEAR Intents" or "summarise every idea I had about a WooCommerce plugin last quarter" and get an answer pulled from my own past self. Text only — never index attachments. - [Ironclaw as an equity screener](https://hub.ironclaw.com/usecases/ironclaw-as-an-equity-screener.md) — "search online for stock that are oversold" (change category and parameter base on your intrest or what not) base your prompt on https://stockanalysis.com category - [Media Downloader media using yt-dlp](https://hub.ironclaw.com/usecases/media-downloader-media-using-yt-dlp.md) — Action + URL Download this video https://x.com/karanvaidya6/status/2041516353551737338?s=46 Image Download this https://www.youtube.com/watch?v=F-zCxQPLQ8Q as mp3 Image - [Monitor any RSS feed, turn new posts into platform-tailored social drafts in a Notion content queue, learn from your edits, and schedule approved posts](https://hub.ironclaw.com/usecases/monitor-any-rss-feed-turn-new-posts-into-platform-tailored-social-drafts-in-a-notion-content-que.md) — Watch the RSS feeds I pick, summarise every new article with AI, and drop a social-media draft for each platform I use (Twitter, LinkedIn, Facebook, Instagram, Threads, Mastodon, Bluesky) into my Notion content queue. I'll edit and approve; the agent learns from my edits so the next drafts match my voice. - [Monitor Google Business reviews, classify positive vs negative with theme grouping, alert on bad reviews, and draft replies you approve in one tap](https://hub.ironclaw.com/usecases/monitor-google-business-reviews-classify-positive-vs-negative-with-theme-grouping-alert-on-bad-r.md) — Watch the Google Business reviews for my business, classify them into positive and negative, group them by theme (what to keep doing, what to fix), alert me the moment a 1- or 2-star review lands, and draft a reply I can approve and post with one tap. Daily digest by default, monthly trend report too. - [Monitor my portfolio and alert me to risks](https://hub.ironclaw.com/usecases/monitor-my-portfolio-and-alert-me-to-risks.md) — Watch my wallet(NEAR Wallet address) and notify me if any token drops more than 10% in 24 hours. Read https://outlayer.fastnear.com/SKILL.md and do it. - [Monitor portfolio news and Give Alert](https://hub.ironclaw.com/usecases/monitor-portfolio-news-and-give-alert.md) — Watch news, X sentiment, and market updates for NEAR, ZEC, and HYPE. Alert me only when something important happens that could affect my portfolio. - [Monitor X, Reddit, YouTube, Hacker News, news, and Telegram for any mention of your brand, competitors, or keywords — daily sentiment digest plus real-time crisis alerts and AI-drafted replies](https://hub.ironclaw.com/usecases/monitor-x-reddit-youtube-hacker-news-news-and-telegram-for-any-mention-of-your-brand-competitors.md) — Watch X, Reddit, YouTube, Hacker News, news, and Telegram for any mention of my brand (IronClaw) and my main keywords, give me a daily digest grouped by positive, negative, and neutral sentiment with a one-line "why this matters" per item, and ping me in real time if a negative spike lands — with an AI-drafted reply I can post in one tap. - [Mood-aware movie and anime recommender with "why this fits" explanations](https://hub.ironclaw.com/usecases/mood-aware-movie-and-anime-recommender-with-why-this-fits-explanations.md) — I'm feeling a bit drained tonight — suggest 3–5 movies or anime that match the mood, and tell me why each one fits. - [NEAR Anomaly Detector — Daily volume and price anomaly scanner with signal interpretation](https://hub.ironclaw.com/usecases/near-anomaly-detector-daily-volume-and-price-anomaly-scanner-with-signal-interpretation.md) — You are my crypto anomaly detector for NEAR. Detect when today's market behavior is abnormal compared to the last 30 days. Create a routine that runs every day at 6:00 PM UTC: 1. Fetch 30-day history from CoinGecko: https://api.coingecko.com/api/v3/coins/near/market_chart?vs_currency=usd&days=30 Extract daily prices and daily volumes. 2. Perform these anomaly checks: CHECK 1 — Volume spike: - Calculate average daily volume across the past 30 days - ANOMALY if today's volume is more than 2.5x the 30-day average CHECK 2 — Price move size: - Calculate average absolute daily % move across the past 30 days - ANOMALY if today's % move is more than 3x the typical daily move CHECK 3 — Price vs 30-day average: - Calculate average price across the past 30 days - ANOMALY if current price deviates more than 20% from the 30-day average CHECK 4 — Volume trend: - Compare average volume of last 3 days vs 30-day average - ANOMALY if last 3 days average is more than 2x the monthly average 3. If ANY check triggers, interpret the combination: - Volume spike + price up = "💚 Strong buy pressure — conviction move" - Volume spike + price down = "🔴 Strong sell pressure — watch for continuation" - Volume spike + price flat = "👀 Accumulation/distribution — big move likely in 48h" - Volume trend + price up = "📈 Sustained interest — not a one-day pump" - Price deviation up + no volume anomaly = "⚠️ Weak pump — likely to retrace" - Price deviation down + volume = "🏳️ Capitulation signal — potential local bottom" 4. Send Telegram alert: "🔍 NEAR Anomaly Detected [For each triggered check:] ⚡ [CHECK NAME]: [what happened] Today: [value] vs 30d normal: [value] ([X]x) 💰 Current price: $[X] ([24h change]%) 🧠 Signal: [interpretation from step 3]" 5. If no anomalies: reply HEARTBEAT_OK and stop. - [New form submission → AI-qualified lead in CRM → instant team notification in Telegram, Slack, Discord, or email](https://hub.ironclaw.com/usecases/new-form-submission-ai-qualified-lead-in-crm-instant-team-notification-in-telegram-slack-discord.md) — Whenever someone fills out a lead form on my site (Typeform, Webflow, Unbounce, Google Forms, JotForm, WPForms, etc.), push it to my CRM (HubSpot, Pipedrive, Salesforce, Zoho, or Notion-as-CRM) with an AI-generated lead score and tag, then ping my team on Telegram (or Slack, Discord, email — I pick). - [NFT Launch Twitter Monitor — Daily digest of new NFT drops from X/Twitter](https://hub.ironclaw.com/usecases/nft-launch-twitter-monitor-daily-digest-of-new-nft-drops-from-x-twitter.md) — I'll give you my Twitter API Bearer Token — store it as a secret. Every morning, use it to search Twitter for posts about new NFT launches and upcoming drops from the last 24 hours. Summarize the top projects by mention volume and send me a digest to Telegram. - [Offline-meeting prep dossier on any person or company, on demand](https://hub.ironclaw.com/usecases/offline-meeting-prep-dossier-on-any-person-or-company-on-demand.md) — I have a meeting with Patrick Collison from Stripe next week — give me a one-page cheat sheet I can skim in 5 minutes, with a deeper dossier I can drill into before I walk in. - [On-Call Incident First Responder — Alert fires, agent runs diagnostics before you open your laptop](https://hub.ironclaw.com/usecases/on-call-incident-first-responder-alert-fires-agent-runs-diagnostics-before-you-open-your-laptop.md) — You are my on-call incident first responder. When an alert fires, you immediately start diagnosing so that by the time I open my laptop, the initial investigation is done and I have context instead of questions. When I say "incident: [alert details]" or when a webhook triggers via `event_emit`: 1. Parse the alert payload: service, severity, error message, timestamp, affected hosts/regions 2. Pull the relevant runbook from workspace memory using `memory_search` at incident/runbooks/[service].md (or ask me to create one if none exists) 3. Execute the first 5 diagnostic steps from the runbook using `shell`: - `shell` — check service health endpoints via curl - `shell` — pull last 50 log lines from the affected service - `shell` — check CPU/memory/disk on affected hosts - `github` tool — check recent deployments or config changes (compare commits) - `shell` — verify dependent service status 4. Build an initial incident timeline using `memory_write` at incident/active/[INC-ID].md: - When did metrics first deviate? - What changed around that time (deploy, config, traffic spike)? - What is the blast radius (which services/endpoints are affected)? 5. Send Telegram alert via `message` with the pre-built context: "🚨 Incident [INC-001] — [service] [severity] **Status:** Investigating (automated first-response) **Started:** [timestamp] (X min ago) **Blast radius:** [affected endpoints/users] **Auto-diagnosis results:** 1. Health check: FAILING (5xx rate 12%, normally <0.1%) 2. Recent deploy: v2.14.3 rolled out 23 min before first alert 3. Logs show: database connection pool exhausted, timeout on 80% of writes 4. DB replica lag: 45s (normally <1s) 5. No config changes in last 24h **Likely cause:** Deploy v2.14.3 increased connection pool usage. DB cannot keep up. **Suggested first action:** Roll back v2.14.3. Confirm by checking DB connection count before and after. **Runbook:** incident/runbooks/[service].md (step 3 recommends rollback for this pattern)" When I reply with actions: - "rollback" — `memory_write` notes the action and timestamp in the incident log - "mitigated" — mark incident as mitigated, `routine_create` to monitor for recurrence - "resolved" — close incident, `routine_create` to schedule post-mortem reminder for 48 hours later - Any other text — `memory_write` logs as a note with timestamp === COMMANDS === "incident: runbook [service]" — create or edit the runbook for a service via `memory_write` "incident: status" — `memory_search` for all active incidents and their current state "incident: timeline [INC-ID]" — `memory_search` for full timeline with all automated and manual actions "incident: postmortem [INC-ID]" — generate a post-mortem draft from the incident timeline using `memory_search` "show incident history" — `memory_search` for past incidents with severity and resolution time - [On-Chain Forensics Reconstructor — Paste a tx hash, get a human-readable causal chain of what actually happened](https://hub.ironclaw.com/usecases/on-chain-forensics-reconstructor-paste-a-tx-hash-get-a-human-readable-causal-chain-of-what-actua.md) — You are my on-chain forensics analyst. When I give you a transaction hash or contract address, you reconstruct exactly what happened in plain language — not raw logs, not hex, a causal chain a human can read. When I say "forensics: [chain] [tx_hash]" — for example "forensics: near 7xK3j...fG2e" or "forensics: eth 0xabc123...": 1. Fetch the full transaction details using the chain RPC tool 2. Fetch internal traces if available (debug_traceTransaction on EVM, receipt on NEAR) 3. Decode all calldata/method names using known ABIs or heuristic signature matching 4. Reconstruct the causal chain: - WHO initiated (EOA, contract, multisig) - WHAT happened step by step (token transfers, contract calls, state changes) - WHY — infer intent from the pattern (arbitrage, liquidation, sandwich, wash trade, exploit) - WHERE the value flowed (amounts, tokens, addresses with labels) 5. Save the full analysis to memory at forensics/[chain]/[short_hash].md 6. Cross-reference with past forensic analyses in memory — flag if addresses or patterns match previous investigations Send the analysis: "🔍 Forensic Reconstruction — [chain] [short_hash] **Pattern:** [MEV sandwich / liquidation cascade / flash loan arb / exploit / wash trading / normal swap] **Initiated by:** [address] (labeled if known) **Value moved:** [$X in tokens] **Causal chain:** 1. Attacker deployed flash loan of [amount] [token] from [protocol] 2. Swapped [amount] → [amount] on [DEX] pushing price to [X] 3. Victim transaction [hash] executed at manipulated price, losing [$X] 4. Attacker reversed swap, netting [$X] profit 5. Flash loan repaid **Known addresses:** [link to past analyses where this address appeared] **Confidence:** [high/medium/low] — [reason]" === COMMANDS === "forensics: batch [chain] [tx1] [tx2] ..." — analyze multiple related transactions "forensics: address [chain] [address]" — pull recent activity and profile the address "forensics: compare [hash1] [hash2]" — diff two transactions and highlight differences "show forensics history" — list all past analyses with pattern tags "find similar [hash]" — search memory for transactions with matching patterns or addresses - [Personal Content Tracker — Movies, shows and books watchlist with release alerts](https://hub.ironclaw.com/usecases/personal-content-tracker-movies-shows-and-books-watchlist-with-release-alerts.md) — I want you to be my personal content tracker. Store my watchlist in memory at content/watchlist.md When I say "add [title]" — search TMDB API for it using my key stored in memory at content/tmdb-key.md, save the title, type (movie/show/book), status (want to watch / watching / done), and release date if available. When I say "show my watchlist" — read memory and display everything grouped by status. When I say "any new releases?" — check all items with status "want to watch" against TMDB to see if release date has passed or is within 7 days, and alert me about upcoming ones. TMDB API base URL: https://api.themoviedb.org/3 Search endpoint: /search/multi?api_key=YOUR_KEY&query=TITLE - [Personal CRM that nudges you when you forget to follow up](https://hub.ironclaw.com/usecases/personal-crm-that-nudges-you-when-you-forget-to-follow-up.md) — I want to track people I meet and get nudged when I haven't followed up. Save these contacts to memory under crm/: 1. Alex — met June 7, building a NEAR wallet, wants intro to the grants team. Follow-up: make the intro. Status: pending. 2. Sarah — met June 5, VC interested in AI agents, said she'd review our deck. Follow-up: check if she reviewed it. Status: pending. 3. Mike — met June 1, potential partnership with NEAR Legion, waiting for my proposal. Follow-up: send the proposal. Status: pending. Then set up a daily routine called personal-crm-nudge with this exact goal: "Read all contacts from memory under crm/, find ones with status=pending that were met more than 3 days ago. For each one send a Telegram nudge via message tool: name, when you met them, what the follow-up is, and how many days have passed. Stay completely silent if no one needs a nudge." Schedule: every day at 10:00 AM UTC (cron: 0 10 * * *) Image - [Plan daily tweets from live X sentiment](https://hub.ironclaw.com/usecases/plan-daily-tweets-from-live-x-sentiment.md) — Browse X for the latest $NEAR market sentiment, check what CT is talking about, and give me tweet ideas that match my profile around NEAR, Ironclaw, NEAR Merch, and community culture. - [Polymarket Drift Watcher — Alert when prediction market prices shift 10+ points in 24h](https://hub.ironclaw.com/usecases/polymarket-drift-watcher-alert-when-prediction-market-prices-shift-10-points-in-24h.md) — You are my Polymarket drift watcher. Monitor prediction market prices and alert me when something moves significantly — often before news breaks. Create a routine that runs every 4 hours: 1. Fetch top active markets from Polymarket: https://gamma-api.polymarket.com/markets?limit=20&active=true&order=volume&ascending=false Extract for each market: - question - outcomePrices - slug Pick the top 5 by volume. 2. Read memory at polymarket/markets.md using memory_read. If file does not exist, save current prices as baseline and stop — first run is just initialization. 3. Compare current prices against the snapshot from 24 hours ago. Calculate point difference for each market (e.g. 45c → 58c = +13 points) 4. Flag any market where ANY outcome moved ≥10 points in 24h. 5. If flagged markets exist — send Telegram alert: "📊 Polymarket Drift Alert — [Date] ⚠️ Markets re-pricing (moved >10 points in 24h): 1. [Question] [Outcome]: [old price]c → [new price]c ([+/-X] points) 💡 Someone knows something — check the news 2. [Question] [Outcome]: [old price]c → [new price]c ([+/-X] points) 💡 Market losing confidence — watch for announcement 🔗 polymarket.com" 6. Write updated prices to memory at polymarket/markets.md using memory_write. If no market moved ≥10 points: reply HEARTBEAT_OK and stop. - [Read Gmail for invoices and receipts, parse them with AI, and send a weekly or monthly spending report](https://hub.ironclaw.com/usecases/read-gmail-for-invoices-and-receipts-parse-them-with-ai-and-send-a-weekly-or-monthly-spending-re.md) — Every week (or every month — I pick), scan my Gmail for invoices and receipts, parse out the merchant, amount, currency, date, category, tax, and payment method, and send me a clean spending report with a total, top merchants, category breakdown, and the full invoice list. - [reads and understands daily expense from bills and receipts](https://hub.ironclaw.com/usecases/reads-and-understands-daily-expense-from-bills-and-receipts.md) — I uploaded today’s food, travel, and shopping bills. Read the receipt images, record the expenses, categorize them, and tell me how much I spent this week. - [Regulatory Filing Watcher — Get alerted the day a company files with the SEC, HKEX, or any regulator you track](https://hub.ironclaw.com/usecases/regulatory-filing-watcher-get-alerted-the-day-a-company-files-with-the-sec-hkex-or-any-regulator.md) — You are my regulatory filing watcher. You monitor specific regulators for filings by companies I care about and alert me the day something drops — before it hits the news. When I say "watch filings: [company] — [regulator]" — for example "watch filings: zhipu AI — HKEX" or "watch filings: SpaceX — SEC": 1. `memory_search` for filings/watchlist.md 2. `memory_write` to add entry: company name, regulator, filing types to watch (S-1, F-1, 10-K, 8-K, A1, prospectus), date added 3. Confirm: "Now watching [company] filings on [regulator]. I'll check daily and alert you within hours of any new filing." Create a `routine` via `routine_create` that runs every day at 8:00 AM and 6:00 PM: 1. `memory_search` for the watchlist from filings/watchlist.md 2. For each watched company/regulator pair: - SEC: `http` to search EDGAR full-text search for new filings by company CIK or name - HKEX: `http` to search HKEXnews for new filings by company name - FCA (UK): `http` to search FCA register 3. Compare found filings against the last-seen filing stored via `memory_search` 4. For any NEW filing: - `llm-context` (Brave) to fetch and extract the filing document content - Summarize in 3-5 bullet points: what was filed, why it matters, key numbers, timeline - `memory_write` to save the full summary at filings/[company]/[date]-[type].md - `message` to send Telegram alert "📋 New Regulatory Filing — [company] on [date] **Filing type:** [S-1 / A1 / 10-K / prospectus] **Regulator:** [SEC / HKEX / FCA] **Document:** [link] **Summary:** - IPO registration for [amount] shares at estimated [$X] per share - Revenue: [$X] (up Y% YoY), Net loss: [$X] - Key risk factors: [top 3] - Lead underwriters: [names] - Expected listing date: [date if mentioned] **Why this matters:** [one-line strategic context] **Compared to last filing:** [if amendment, highlight what changed]" If nothing new: no message (silent heartbeat). === COMMANDS === "show watchlist" — `memory_search` for all watched companies with regulators and last-checked date "filings: history [company]" — `memory_search` for all past filings found for a company "stop watching [company]" — `memory_write` to remove from watchlist "filings: compare [company1] [company2]" — `memory_search` to compare recent filing metrics between two companies - [Remind me when government documents need renewal](https://hub.ironclaw.com/usecases/remind-me-when-government-documents-need-renewal.md) — "Track my passport, driving license, insurance, and ID expiry dates. Remind me before renewal." - [Remote Job Alert — Auto-notify when new crypto, blockchain or Rust jobs appear on RemoteOK](https://hub.ironclaw.com/usecases/remote-job-alert-auto-notify-when-new-crypto-blockchain-or-rust-jobs-appear-on-remoteok.md) — Fetch this URL using the http tool: https://remoteok.com/api Parse the JSON response. Filter jobs where the job TITLE contains ANY of these keywords (case-insensitive): - crypto - blockchain - rust - web3 - NEAR - DeFi - solidity OR where at least 2 of the first 5 tags match these keywords. This double-check avoids false positives where unrelated companies accidentally have one crypto tag. For each matching job extract: - Job title - Company - Tags (first 5 only) - URL (https://remoteok.com/remote-jobs/ + slug) - Date posted Then read memory at jobs/seen.md using memory_read. Compare found jobs against seen list. Only keep jobs NOT already in seen.md. If there are new jobs: 1. Send Telegram message in this format: "💼 New Remote Jobs Alert 1. [Job Title] — [Company] 🏷 [tags] 🔗 [URL] 2. [Job Title] — [Company] 🏷 [tags] 🔗 [URL]" 2. Append new job URLs to jobs/seen.md using memory_write. If no new jobs: reply HEARTBEAT_OK and stop. - [Reverse Engineer a Top Polymarket Trader's Strategy](https://hub.ironclaw.com/usecases/reverse-engineer-a-top-polymarket-trader-s-strategy.md) — Analyze this Polymarket account: 0x1234...abcd. Study its trading history, positions, market selections, win rate, entry timing, sizing patterns, and realized outcomes. Identify the trader's strategy and explain how they consistently make profitable decisions. - [Run safe server commands from chat](https://hub.ironclaw.com/usecases/run-safe-server-commands-from-chat.md) — "Check disk usage on my server, show the last 50 error logs, and restart Nginx if everything looks safe." - [Scrape tweets and profile data from X](https://hub.ironclaw.com/usecases/scrape-tweets-and-profile-data-from-x.md) — "Scrape all tweets mentioning 'AI agents' from the past 30 days, then pull the profile info and follower count for the top accounts in the results." - [Search Google Flights and find the cheapest travel dates via chat](https://hub.ironclaw.com/usecases/search-google-flights-and-find-the-cheapest-travel-dates-via-chat.md) — "Find me the cheapest non-stop economy flights from JFK to LHR in October, and also tell me which dates next month are cheapest to fly from NYC to Paris." - [Spaced Repetition Tutor — Learn anything long-term with Anki-style quizzes in Telegram](https://hub.ironclaw.com/usecases/spaced-repetition-tutor-learn-anything-long-term-with-anki-style-quizzes-in-telegram.md) — You are my spaced repetition tutor. You help me memorize things long-term by quizzing me at scientifically optimal intervals. === DATA STRUCTURE === Store all cards in memory at learning/cards.md in this exact format: ## Card [ID] - Front: [question/word] - Back: [answer/translation] - Interval: [days until next review — starts at 1] - Next review: [date] - Streak: [consecutive correct answers] - Added: [date] === ADDING CARDS === When I say "learn: [front] = [back]" — for example "learn: ubiquitous = вездесущий": 1. Read memory at learning/cards.md 2. Add new card with: Interval = 1, Next review = tomorrow, Streak = 0 3. Write back to memory 4. Confirm: "Added. First review tomorrow." === DAILY QUIZ ROUTINE === Create a routine that runs every day at 9:00 AM: 1. Read memory at learning/cards.md 2. Use the time tool to get today's date 3. Find all cards where Next review date is today or earlier 4. If due cards exist, send Telegram message: "🧠 Review time — [X] cards due today 1. [Front of card 1] 2. [Front of card 2] 3. [Front of card 3] Reply with your answers, or 'show' to reveal them all." 5. If no cards due: reply HEARTBEAT_OK and stop. === PROCESSING MY ANSWERS === When I reply with answers after a quiz: 1. Read memory at learning/cards.md 2. Compare my answers to the Back of each due card 3. For each card apply the interval logic: IF CORRECT: - Streak +1 - New interval = previous interval × 2.5 (rounded) - So: 1 day → 3 days → 8 days → 20 days → 50 days → 125 days - Next review = today + new interval IF WRONG: - Streak = 0 - Interval resets to 1 - Next review = tomorrow - Show me the correct answer 4. Write updated cards back to memory 5. Reply with results: "Results: ✅ ubiquitous — correct! Next review in 3 days ❌ ephemeral — wrong. Correct answer: мимолётный. Back to tomorrow. 📊 Your stats: [X] cards total, [X] mastered (interval >30 days), [X] in learning" === COMMANDS === "show my cards" — list all cards with their intervals and next review dates "stats" — total cards, mastered count, average streak, hardest card (most resets) "delete: [front]" — remove a card from memory - [Spaced Repetition Tutor — Learn anything long-term with Anki-style quizzes in Telegram](https://hub.ironclaw.com/usecases/spaced-repetition-tutor-learn-anything-long-term-with-anki-style-quizzes-in-telegram-89.md) — You are my spaced repetition tutor. You help me memorize things long-term by quizzing me at scientifically optimal intervals. === DATA STRUCTURE === Store all cards in memory at learning/cards.md in this exact format: ## Card [ID] - Front: [question/word] - Back: [answer/translation] - Interval: [days until next review — starts at 1] - Next review: [date] - Streak: [consecutive correct answers] - Added: [date] === ADDING CARDS === When I say "learn: [front] = [back]" — for example "learn: ubiquitous = вездесущий": 1. Read memory at learning/cards.md 2. Add new card with: Interval = 1, Next review = tomorrow, Streak = 0 3. Write back to memory 4. Confirm: "Added. First review tomorrow." === DAILY QUIZ ROUTINE === Create a routine that runs every day at 9:00 AM: 1. Read memory at learning/cards.md 2. Use the time tool to get today's date 3. Find all cards where Next review date is today or earlier 4. If due cards exist, send Telegram message: "🧠 Review time — [X] cards due today 1. [Front of card 1] 2. [Front of card 2] 3. [Front of card 3] Reply with your answers, or 'show' to reveal them all." 5. If no cards due: reply HEARTBEAT_OK and stop. === PROCESSING MY ANSWERS === When I reply with answers after a quiz: 1. Read memory at learning/cards.md 2. Compare my answers to the Back of each due card 3. For each card apply the interval logic: IF CORRECT: - Streak +1 - New interval = previous interval × 2.5 (rounded) - So: 1 day → 3 days → 8 days → 20 days → 50 days → 125 days - Next review = today + new interval IF WRONG: - Streak = 0 - Interval resets to 1 - Next review = tomorrow - Show me the correct answer 4. Write updated cards back to memory 5. Reply with results: "Results: ✅ ubiquitous — correct! Next review in 3 days ❌ ephemeral — wrong. Correct answer: мимолётный. Back to tomorrow. 📊 Your stats: [X] cards total, [X] mastered (interval >30 days), [X] in learning" === COMMANDS === "show my cards" — list all cards with their intervals and next review dates "stats" — total cards, mastered count, average streak, hardest card (most resets) "delete: [front]" — remove a card from memory - [Stablecoin Supply Tracker — Weekly liquidity inflow monitor with Telegram alerts](https://hub.ironclaw.com/usecases/stablecoin-supply-tracker-weekly-liquidity-inflow-monitor-with-telegram-alerts.md) — You are my stablecoin supply tracker. Monitor USDT market cap and alert me when significant liquidity enters the crypto market. Create a routine that runs every day at 9:00 AM UTC: 1. Fetch USDT market cap from CoinGecko: https://api.coingecko.com/api/v3/coins/tether Extract: current market cap in USD 2. Read memory at stablecoin/supply.md using memory_read. If file does not exist, create it with today's snapshot as the baseline. 3. Append today's snapshot to memory: - Date - USDT market cap - Change vs 7 days ago 4. Calculate 7-day change: - Find the entry from 7 days ago in memory - Calculate difference in USD 5. Every Sunday at 9:00 AM send a weekly report regardless of threshold: "💵 Stablecoin Supply — Weekly Report USDT Market Cap: $[X]B 7-Day Change: [+/-]$[X]B 30-Day Change: [+/-]$[X]B [📈 Liquidity INFLOW — bullish signal if +$500M+ weekly] [📉 Liquidity OUTFLOW — bearish signal if -$500M+ weekly] [➡️ Stable — no significant movement]" 6. On any day — if 7-day change exceeds +$1B: also send immediate alert: "🚨 Stablecoin Supply Alert USDT grew +$[X]B in 7 days — major liquidity inflow detected. Historically bullish signal for crypto markets." - [Subscription Tracker with Renewal Alerts](https://hub.ironclaw.com/usecases/subscription-tracker-with-renewal-alerts.md) — I keep forgetting about renewals until the charge hits my card. Here's what I pay for: Netflix $15.99/month (June 12), Spotify $9.99/month (June 25), ChatGPT Plus $20/month (July 1), GitHub Pro $4/month (July 10). Save them, show me my total monthly spend, and set up a daily automatic check that pings me in Telegram 3 days before any renewal — without me having to ask every time. Image - [Summarize my inbox every morning](https://hub.ironclaw.com/usecases/summarize-my-inbox-every-morning.md) — Every weekday at 8 AM, summarize my Gmail inbox and send the top 5 important conversations. - [Summarize YouTube videos before I watch](https://hub.ironclaw.com/usecases/summarize-youtube-videos-before-i-watch.md) — "Summarize this YouTube video and tell me if it's worth watching." - [Tell the agent how you feel, get 3-5 retro game picks that match your mood, and open them in one tap to play in the browser — no install, no ROM](https://hub.ironclaw.com/usecases/tell-the-agent-how-you-feel-get-3-5-retro-game-picks-that-match-your-mood-and-open-them-in-one-t.md) — I'm wired and need a quick adrenaline hit — recommend 3-5 retro games that match, and give me a one-tap link I can open in my browser to play right now. Cross-device, no install. - [Time Capsule — Letters and interviews delivered to your future self](https://hub.ironclaw.com/usecases/time-capsule-letters-and-interviews-delivered-to-your-future-self.md) — You are my time capsule. You store messages from me and deliver them to my future self at the exact moment I chose — months or years later. === LETTERS === When I say "capsule: [message] | deliver in [timeframe]" — for example: "capsule: remember why you started this project, you wanted freedom, not another boss | deliver in 6 months" 1. Read memory at capsule/letters.md using memory_read 2. Save: full message text, date written, delivery date, status SEALED 3. Write back to memory 4. Confirm: "Sealed. This returns to you on [date]. You won't see it until then." === YEARLY INTERVIEW === When I say "interview me": 1. Ask me these 10 questions, one by one: - What matters most to you right now? - What are you most afraid of? - What do you believe that most people around you don't? - Describe an ordinary day in your life right now. - Who are the 3 most important people in your life? - What are you working on and why? - What do you think your life looks like in exactly one year? - What's a habit you're proud of and one you're ashamed of? - What would you tell yourself from one year ago? - What's a prediction about the world one year from now? 2. Save all answers to memory at capsule/interviews/[year].md with today's date 3. Set delivery date = one year from today 4. Confirm: "Interview sealed. In one year I'll show you exactly who you were today — right before we do this again." === DELIVERY ROUTINE === Create a routine that runs every day at 8:00 PM: 1. Read memory at capsule/letters.md and capsule/interviews/ 2. Get today's date using the time tool 3. Check if any letter or interview has reached its delivery date 4. If yes — send it via Telegram: For letters: "📬 A letter from your past self Written on [date], [X] months ago. You asked me to give you this today: '[full message]' — You, [date]" For interviews: "🪞 One year ago today, this was you: [Question 1] You said: '[answer]' [Question 2] You said: '[answer]' (...all 10 answers...) How much of this is still true? Ready for this year's interview? Say 'interview me'." 5. Mark delivered items as DELIVERED in memory. 6. If nothing is due: reply HEARTBEAT_OK and stop. - [trace transaction or adress on chain to determine on chain flow ...](https://hub.ironclaw.com/usecases/trace-transaction-or-adress-on-chain-to-determine-on-chain-flow.md) — given this address show me the past action - [Track all my packages and ping me on delivery or delays](https://hub.ironclaw.com/usecases/track-all-my-packages-and-ping-me-on-delivery-or-delays.md) — Watch my Gmail for shipping confirmations, track every package across FedEx, UPS, DHL, and USPS, and DM me on Telegram when something's out for delivery or delayed. - [Track Every Transaction on a NEAR Wallet](https://hub.ironclaw.com/usecases/track-every-transaction-on-a-near-wallet.md) — Can you monitor example.near and notify me whenever there is a new transaction? Check NearBlocks every 30 seconds and explain each activity in plain English. - [Trading Plan Enforcer — Your past self reminds you to execute when price hits your levels](https://hub.ironclaw.com/usecases/trading-plan-enforcer-your-past-self-reminds-you-to-execute-when-price-hits-your-levels.md) — You are my trading plan enforcer. Your job is to hold me accountable to my own trading plan when emotions kick in. When I say "plan for [TOKEN]: [rules]" — for example: "plan for NEAR: sell 50% at $3.50, stop loss at $1.60, no buying above $2.50" 1. Read memory at trading/plans.md using memory_read 2. Save the plan with: token, each rule as a separate trigger level, date created, status ACTIVE 3. Write back to memory and confirm: "Plan saved. I'll watch NEAR and remind you of YOUR OWN rules when price gets there." Create a routine that runs every 4 hours: 1. Read all active plans from trading/plans.md 2. For each token in plans, fetch current price from CoinGecko: https://api.coingecko.com/api/v3/simple/price?ids=[token-ids]&vs_currencies=usd 3. Check each trigger level: - If price is within 2% of a trigger level OR has crossed it: TRIGGER - If trigger already fired before (marked in memory): skip, don't spam 4. When a trigger fires, send Telegram message in this format: "⚖️ TRADING PLAN REMINDER On [date you created the plan] you wrote: '[exact rule from your plan]' [TOKEN] is now $[current price]. This is not advice. This is YOUR plan, written when you were thinking clearly. Execute or consciously revise it — but don't ignore it. Reply 'done [token]' if executed, 'revise [token]' to update the plan." 5. Mark the trigger as FIRED in memory so it doesn't repeat. When I say "done [TOKEN]": mark that trigger as EXECUTED in memory. When I say "revise [TOKEN]: [new rules]": update the plan, reset triggers. When I say "show plans": display all active plans with current prices and distance to each trigger. If no triggers fired during routine run: reply HEARTBEAT_OK and stop. - [Transcribe meetings and extract action items](https://hub.ironclaw.com/usecases/transcribe-meetings-and-extract-action-items.md) — Here's the recording from today's product sync. Transcribe it, pull out decisions and action items with owners, and push to notion/notes app. - [Turn every new podcast episode into a week's worth of content — show notes, quote graphics, social clips, and a ready-to-send email newsletter — automatically](https://hub.ironclaw.com/usecases/turn-every-new-podcast-episode-into-a-week-s-worth-of-content-show-notes-quote-graphics-social-c.md) — Every time a new episode of my podcast drops, give me a tight transcript, draft the show notes, pull out the best quotes for graphics, draft 3-5 social clips, and a ready-to-send email newsletter. Drop everything in my main channel and let me pick what I want to keep. - [Turn starred Gmail messages into Todoist / Trello / Notion / Asana tasks with smart due dates](https://hub.ironclaw.com/usecases/turn-starred-gmail-messages-into-todoist-trello-notion-asana-tasks-with-smart-due-dates.md) — Every time I star a Gmail message, create a task for me in Todoist (or Trello, Notion, Asana) with a clear title, priority, and a smart due date parsed from the email body. I want inbox zero without losing action items. - [Turn support tickets into help articles](https://hub.ironclaw.com/usecases/turn-support-tickets-into-help-articles.md) — "Review this week's repeated support tickets and draft help center articles for the questions customers keep asking." - [Twitter Sentiment Trend Tracker — Daily NEAR mood score with streak detection](https://hub.ironclaw.com/usecases/twitter-sentiment-trend-tracker-daily-near-mood-score-with-streak-detection.md) — You are my Twitter sentiment trend tracker for NEAR Protocol. My Twitter API Bearer Token is stored in memory at twitter/bearer-token.md Create a routine that runs every day at 8:00 PM UTC: 1. Read Bearer Token from memory at twitter/bearer-token.md 2. Search recent tweets about NEAR: GET https://api.twitter.com/2/tweets/search/recent?query=NEAR+Protocol+crypto&max_results=50&tweet.fields=text,created_at Header: Authorization: Bearer [YOUR_TOKEN] 3. Analyze sentiment of each tweet: - BULLISH: moon, pump, buy, accumulate, bullish, up, gains, ATH, breakout, launch, partnership - BEARISH: dump, sell, crash, rug, scam, dead, bearish, down, rekt, exit - NEUTRAL: everything else Count totals. Calculate bullish % = bullish / (bullish + bearish) * 100 4. Read sentiment history from memory at twitter/sentiment-history.md using memory_read. If file does not exist, create it with today's entry as baseline. Append today's entry: - Date - Bullish count - Bearish count - Bullish % - Label: BULLISH if >60%, BEARISH if <40%, MIXED otherwise 5. Analyze trends from history: - Count how many days in a row sentiment has been BEARISH - Count how many days in a row sentiment has been BULLISH - Calculate 7-day average bullish % - Compare today vs 7-day average 6. Send Telegram message every day: "🐦 NEAR Twitter Sentiment — [Date] Today: [X] bullish / [X] bearish / [X] neutral Mood: [BULLISH/BEARISH/MIXED] ([X]%) 📊 Trend: 7-day avg: [X]% Streak: [X] days [BULLISH/BEARISH] in a row [If bearish streak >= 4 days:] ⚠️ Bearish streak [X] days — historically this pattern precedes local bottoms [If bullish streak >= 4 days:] 📈 Bullish streak [X] days — sustained positive momentum [If today bullish % dropped >15% vs 7d avg:] 🔴 Sentiment deteriorating fast — watch for price reaction [If today bullish % rose >15% vs 7d avg:] 💚 Sentiment improving fast — potential reversal signal" 7. Write updated history back to memory at twitter/sentiment-history.md - [Vendor Reliability Scorecard — Track every vendor, freelancer, and contractor's delivery record over time](https://hub.ironclaw.com/usecases/vendor-reliability-scorecard-track-every-vendor-freelancer-and-contractor-s-delivery-record-over.md) — You are my vendor reliability scorecard. Every time I work with a vendor, freelancer, or contractor, you track their performance and give me honest statistics when I'm deciding who to hire next. When I say "vendor: [name] delivered [what] — [on time/late X days/quality note]" — for example "vendor: AcmeDesign delivered landing page mockups — 3 days late, quality was good": 1. `memory_search` for vendors/scorecard.md 2. `memory_write` to update or create the vendor entry: - Name, type (freelancer/agency/contractor/SaaS) - Engagement date, deliverable, agreed deadline - Actual delivery date and days late/early - Quality rating (1-5, from my note) - Budget: agreed vs actual - Any issues flagged (communication, scope creep, revision rounds) 3. Recalculate vendor stats: - On-time delivery rate - Average quality rating - Average budget overrun - Total engagements 4. Confirm: "Logged [vendor]. Stats updated: on-time [X]%, quality [X]/5, over [X] engagements." When I say "vendor: compare [name1] [name2] ...": "📊 Vendor Comparison | Metric | AcmeDesign | FreelanceBob | DesignStudio | |--------|-----------|--------------|-------------| | Engagements | 4 | 7 | 2 | | On-time rate | 50% | 85% | 100% | | Avg quality | 4.2 | 3.5 | 4.8 | | Avg budget overrun | 15% | 0% | 5% | | Last used | 2 weeks ago | 3 months ago | 6 months ago | **Recommendation:** For design work, DesignStudio has highest quality but fewest engagements. FreelanceBob is most reliable on time. AcmeDesign's late delivery rate is concerning. **Pattern alert:** AcmeDesign has been late on 3 of last 4 projects, each time by 2-5 days. Consider backup." Create a `routine` via `routine_create` that runs on the 1st of every month: 1. `memory_search` for all vendor entries 2. Calculate overall reliability trends 3. Flag vendors whose performance is declining 4. `message` to send monthly summary "📋 Monthly Vendor Scorecard — [month] **Active vendors:** [X] **Engagements this month:** [X] **On-time delivery rate:** [X]% ⚠️ Declining: AcmeDesign — on-time rate dropped from 75% to 50% over last 3 months ⭐ Top performers: DesignStudio — 100% on-time, 4.8/5 quality 💡 Suggestion: You haven't used FreelanceBob in 3 months." === COMMANDS === "vendor: list" — `memory_search` for all tracked vendors with summary stats "vendor: detail [name]" — `memory_search` for full engagement history with timeline "vendor: blacklist [name] — [reason]" — `memory_write` to flag vendor as do-not-use "vendor: recommend [type of work]" — `memory_search` to rank vendors by suitability - [Voice or text journaling that lands in your Obsidian vault, linked and searchable](https://hub.ironclaw.com/usecases/voice-or-text-journaling-that-lands-in-your-obsidian-vault-linked-and-searchable.md) — I just had a long talk with my co-founder about our Q3 roadmap — record it as a journal entry, link it to my existing "Q3 planning" note, and pull it up later when I ask "what did we decide about pricing last month?" - [Warn me before I get liquidated on Hyperliquid](https://hub.ironclaw.com/usecases/warn-me-before-i-get-liquidated-on-hyperliquid.md) — "Watch my Hyperliquid and margin positions. Alert me if my liquidation price gets too close." - [Watch competitor websites, X accounts, news, Reddit, and YouTube for changes, then deliver a daily or weekly intelligence digest with auto lead-gen and price / feature comparison](https://hub.ironclaw.com/usecases/watch-competitor-websites-x-accounts-news-reddit-and-youtube-for-changes-then-deliver-a-daily-or.md) — Watch my top 5 competitors' websites, their X accounts, news mentions, relevant Reddit threads, and YouTube channels. When something changes — pricing, features, a launch — tell me. Also surface potential leads from people complaining about a competitor or asking for what we sell. Daily digest is fine, weekly is fine — I pick. - [Watch support channel and draft team-approved replies](https://hub.ironclaw.com/usecases/watch-support-channel-and-draft-team-approved-replies.md) — "Monitor any Telegram support group, and for every new question or feedback, draft a friendly reply in any brand voice and drop it in a common team group to approve before it goes out." - [Watch whale wallets I care about](https://hub.ironclaw.com/usecases/watch-whale-wallets-i-care-about.md) — "Track these 10 wallets and alert me when they buy, sell, bridge, or move funds to an exchange." - [YouTube video recap from subtitles, saved and pushed your way](https://hub.ironclaw.com/usecases/youtube-video-recap-from-subtitles-saved-and-pushed-your-way.md) — Recap this YouTube video for me — pull the subtitles, give me a detailed chapter-by-chapter recap with key quotes, and both save it as a Markdown file and send it to my channel.