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Live launchpad feed + AI hot topics for meme tokens. (1) meme-rush: real-time lifecycle feed on launchpads (Pump.fun, Four.meme) — brand-new launches, currently-finalizing / bonding-curve tokens, and just-migrated-to-DEX tokens; filter by dev behavior, age, market cap. (2) topic-rush: AI-detected hot market narratives with the associated tokens ranked by inflow. Use for: "new pump.fun launches", "what just migrated", "currently bonding", "hot narratives", "what topic is pumping right now", "live launchpad feed", "AI hot topics".

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Snapshot of a single wallet's token holdings on a specific chain — list of every token currently held with name, symbol, current price, 24h price change, and holding quantity. Use when the user provides an explicit wallet address (or says "my wallet") and wants the current portfolio: "what does 0x... hold", "wallet balance breakdown", "list positions for this address", "what tokens are in this wallet", "show me the holdings of <address>".

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Tailors one resume to one specific job posting. Builds a must-have/nice-to-have requirement table, an evidence map, gap questions instead of invention, a tailored copy (headline, summary, bullet order, skills), honest ATS guidance, and an optional cover letter for that posting. Use when the user says "tailor my resume to this job", "customize my CV for this posting", "match my resume to this job description", "what keywords am I missing", "make my resume ATS-friendly for this role", "will this get past the ATS", "the job asks for X and I don't have it", or pastes a job ad with a resume. Works from pasted text; uses Reactive Resume MCP tools when connected. Not for bullets with no target job (resume-bullet-writer), what belongs on a resume or a general audit (resume-content-guide), tracking or follow-ups (job-application-manager), or interview prep and practice (interview-prep, mock-interview).

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The baseline method for building ONE stage — a single classroom — with this runtime. Covers how the plan is settled in conversation, the order the generation tools are called in, how the classroom roster is written, when narration has to be re-synthesized, and what must be true before a stage is called done. Load this before generating or rebuilding any stage from scratch, whatever its subject; it is the floor the topic skills build on, not an alternative to them.

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The design law of a slide page — canvas geometry, the text height table, the type scale, contrast pairs, spacing rhythm, and which element type carries which content. Load it when patching slide elements one at a time, when a fix is about colour or contrast, when text has to grow or shrink inside a box, when a page feels crowded or empty, or when replacement content needs real rich-text structure. It is the standard `page-clone` and `pro-editing` edit against, not a procedure of its own — those two decide which pages to touch and in what order, this one decides what a good page looks like when you put it back.

日本語の概要は準備中です。原文の説明を表示しています。

Interactive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually integrating an API end-to-end (use integrate-context-matic instead).

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Write, append, or read desk journal entries. The journal is persistent memory — what survives session boundaries. A good entry has: what was done, current state, next step.

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Run a read-only documentation drift audit for a feature, PR, branch, release, API, configuration change, workflow, CLI, package, or repository area. Use when the user asks whether docs are stale, missing, inconsistent with code, or need updates after code changes. Checks README files, setup guides, API docs, env docs, changelogs, examples, comments, generated docs, and user-facing instructions. This is not a general code review; it compares what the docs claim against what the code does.

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Analyzes git diffs or staged changes and generates narrative commit messages that explain WHY a change was made, not just what changed — following Conventional Commits format. Use when asked to "write a commit message", "generate a commit", "describe my changes", "what should I commit this as", "commit this", "summarize my diff", or "help me commit". Works with git diff output, staged files, or plain descriptions of changes.

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Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

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Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.

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Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock. Triggers on "earnings preview", "what to watch for [company] earnings", "pre-earnings", "earnings setup", or "preview Q[X] for [company]".

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Work on one hard mathematics problem in rounds: each round a judge writes a few self-contained questions, independent workers answer them, and the judge keeps a ledger of what is proved, refuted and open, until the judge concludes or the rounds run out, and then a proof.md says plainly what is and is not proved. A run takes hours and dozens of worker runs. Usage: /math-proof:siege [NAME=value settings] <the problem, stated in full, or the path of a file holding it>.

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Strategy Discovery: evidence-gated facade over Alpha Zoo + the SDM strategy store — answers what strategies exist and what state they are in, with per-regime evidence instead of scenario tags; reports evidence freshness on every returned row and rebuilds the disposable evidence cache from local backtest runs.

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Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity, flagging what to fix before submitting (blocking issues + dead code) vs what to leave for the actual review. Report-only — does not edit files.

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Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check dependencies", "what uses this", "show relationships", "find serializers", or when reading code and needing to understand what depends on a component before modifications.

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Draft a reply to your newest Gmail email that knows what you discussed in your Granola meetings, what you promised the sender, and how you write, using cognee memory. Use when someone asks to answer or reply to their latest email in their own tone. Runs locally; reads Gmail read-only; the draft is printed, never sent.

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Add WhatsApp channel via native Baileys adapter. Direct connection — no Chat SDK bridge. Uses QR code or pairing code for authentication.

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Add WhatsApp Business Cloud API channel via Chat SDK. Official Meta API.

日本語の概要は準備中です。原文の説明を表示しています。

Brain-augmented web research. Sends brain context about a topic to Perplexity, which searches the web with citations and returns what is NEW vs what the brain already knows. Use for entity enrichment, current-state checks, deal monitoring, and freshness deltas. NOT for simple URL fetches (use web_fetch) or brain-only queries (use gbrain query).

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Runs the owner's Monday morning briefing as a two-link chain — business-pulse for the cross-connector snapshot of cash, sales, and pipeline, and report-builder for any saved KPI reports the owner has already defined — then reads the week ahead straight from Calendar and mail and merges all three parts into a single one-page Monday document, delivered per the owner's stored output preference. Requires no connectors and degrades gracefully, building from whatever of the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), Calendar, DocuSign, Expensify, Gmail or M365, HubSpot, PayPal, Ramp, RingEx Chat, Shopify, Slack, Stripe, and TikTok Ads is connected, or from pasted and uploaded data when none are. Trigger on "Monday brief," "start my week," "what do I need to know this week," "weekly briefing," "catch me up before Monday," or when the owner schedules a recurring start-of-week update.

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Wins back customers who have quietly stopped buying — finds the ones whose gap has stretched past their own normal rhythm or who are showing churn signals, ranks them by what the relationship was worth, drafts a win-back message per customer in the owner's voice, and sends and logs only what the owner approves. Chains review-reputation, outreach-composer, and crm-autopilot, and keeps the rule that the first touch acknowledges the gap without pitching. Use this whenever the owner is worried about customers drifting away, including phrasings like "who hasn't ordered in a while," "win back my old customers," "which customers have gone quiet," "we lost some regulars," "reach out to the people who stopped calling," or "how do I get them back." Reach for it when the owner names a customer they have not heard from, as readily as for a full win-back campaign.

日本語の概要は準備中です。原文の説明を表示しています。

Watches what customers are saying in public and in private, then does something about it. Aggregates Google, Yelp, and Facebook reviews alongside disputes, tickets, and email into themes backed by verbatim quotes; drafts a reply to every review in the owner's own voice for approval; spots customers who have gone quiet or look likely to leave; and drafts win-back offers for the ones worth keeping. With Shopify connected, order and fulfillment patterns become a sentiment signal too. Works from pasted or exported reviews when nothing is connected. Use this whenever the owner asks about reviews, ratings, reputation, complaints, or repeat business — including "what are people saying about us," "did we get any bad reviews," "how do I respond to this one-star," "our rating dropped," "which customers have gone quiet," "who hasn't ordered in a while," "how do I win them back," or "are customers happy."

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Turns what is actually selling into a reorder decision and gets it all the way into the books — computes sales velocity and conservative stockout dates per item, sizes the reorder, drafts the purchase order and the vendor email, and then stages the matching entry so the bill matches against the PO when it arrives. Flags the money sitting still too, so dead stock gets cleared instead of reordered. Runs on Shopify, Square, or an uploaded sales and stock CSV, and deepens with QuickBooks, Gmail or M365, Calendar, and NetSuite. No PO is sent and no vendor is emailed without approval. Use it when the owner says "what do I need to reorder," "am I going to run out," "restock," "we keep running out of filters," "draft a PO for my supplier," or "how much cash is sitting in the warehouse."

日本語の概要は準備中です。原文の説明を表示しています。