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概要と使いどころ
Generates optimized meta descriptions using OpenAI Chat Completions API with token-aware truncation. Integrates with Yoast SEO REST API fields and Google SERP Preview validation for CTR optimization.
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。
Use Yao when an operator needs to package an AI agent workflow, hooks, MCP tools, memory scopes, and a reviewable web or chat interface into one deployable runtime.
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。
host chinese call of cthulhu and similar investigative tabletop rpg sessions as kp/keeper. use when the user asks to start, continue, simulate, or play a coc scenario, wants chatgpt to be kp, asks for preset investigator cards, npc teammates, dice checks, horror investigation pacing, solo/co-op tabletop roleplaying, or persistent pre-session prep/project folders for a campaign. ask only the minimum setup questions: whether the user wants a custom investigator persona and how many npc teammates; otherwise handle scenario selection, character card creation, npc teammates, dice, pacing, narration, and campaign notes automatically.
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。
Use when the user brings any fuzzy, half-formed, or interrupt-driven input — an order from a boss, a forwarded email thread, a ticket, a chat message, a "can you look into X", a raw idea — and it needs framing before work starts. Turns the input into a structured brief (Context · Desired outcome · Goal · Owner · Next step · Decision needed). This is the AIOS's front door; run most things through it first. Trigger on "/structure", "frame this", "what's the actual ask here", "structure this", or any vague task that lacks a defined outcome.
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。
Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured data".
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。
Use when the user says 'make my store visible to chatgpt', 'set up product feeds', or 'make my store agent-ready', or asks about Google Merchant Center, a store llms.txt, or cart links for AI agents. Sets up product feeds in six formats and registers the catalog with the major platforms.
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。
Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"
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。
Use for past-session context recovery. Triggers: recall, last time, previous session, chat search, memory search, 그때, 지난번, 저번 세션, 예전에 했던, 기억나?, 리콜, 지난 세션, 이전 작업, 뭐였지, 어떻게 했었지.
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。
Use when adding support for a new model architecture to imp, porting a model family, or debugging a model that loads but produces wrong output - "add support for <model>", "new arch", loader detection, chat template, tokenizer parity, RoPE variant, "outputs garbage", "prompt-blind", "digits scrambled", "NaN logits", "describes a different picture", "does it fit in VRAM". Do NOT use for kernel performance (sm120-cuda-expert) or quant-format questions (quant-formats).
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。
Use when working on or testing imp-server and its HTTP APIs - OpenAI/Anthropic endpoints, /v1/chat/completions, /v1/messages, /v1/responses, SSE streaming, tool calling, json_schema/regex/GBNF constrained decoding, thinking/reasoning_content, cache_control/prefix cache, model loading and swapping, request priority, X-Request-Id, OTLP tracing / traceparent / Jaeger, /metrics, container env (IMP_SET), "server returns 404/garbage", API compliance. Do NOT use for CLI-only inference (imp-cli), kernel work (sm120-cuda-expert), or pure output-quality checks (check-degeneration).
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。
Simulates a realistic VC Investment Committee: three partner archetypes debate a startup's merits, concerns, and deal terms, scored across 28 dimensions. Use when the user asks to simulate an IC, or how partners would debate their startup, and gives a deck, a company description, or a specific fund. A full run takes about 15–20 minutes, so for a passing question such as 'would a VC fund us?' or 'are we fundable?', answer briefly in chat and offer the simulation instead of starting it. When it runs, partners' views come from the scored simulation, never improvised.
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。
Maps a startup's competitive landscape, scores moat strength across 6+ dimensions, and writes an investor-ready competition narrative with a positioning map. Use when the user asks for a competitive landscape, moat analysis, or positioning review and gives enough to work from: a deck, a list of competitors, or a clearly named startup. A full run takes about 30–40 minutes and searches the web, so for a passing question such as 'who are my competitors?' or 'is that a real moat?', answer briefly in chat and offer the analysis instead of starting it. When it runs, the competitor set and moat scores are checked adversarially, never recalled from memory.
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。
Scores and strengthens startup pitch decks (pre-seed through Series A) against 35 investor-grade criteria grounded in Sequoia, DocSend, YC, a16z, and Carta data. Use when the user has shared a pitch deck (PDF, PPTX, markdown, pasted slide text, or a link to one) and asks for a review, score, feedback, or critique. A full review takes about 15–25 minutes, 40 or more for a long investor deck, so for a general pitch question with no deck, answer briefly in chat and offer the review instead of starting it. When it runs, score with the rubric, never with deck advice from memory.
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。
Builds credible TAM/SAM/SOM analysis with external validation and sensitivity testing for startup fundraising: top-down, bottom-up, or both. Use when the user asks to size a market or check market claims and gives something to size: a product or service description, a market segment, or a deck with TAM/SAM/SOM claims. A full run takes about 15–20 minutes and searches the web, 30 or more with a large data room or a revision round, so for a passing question about market size, answer briefly in chat and offer the analysis instead of starting it. When it runs, every figure is externally sourced and stress-tested, never estimated from memory.
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。
Use for any cap-table number, mechanic, or date before a founder signs, from a single SAFE, note or warrant described in chat or a quick 'is this dilution reasonable?' check to any draft or signed SAFE, note, term sheet, option plan, AoA, Carta XLSX, or spreadsheet. A single question gets a short cited answer in about 2–3 minutes; the full report, about 5–10 minutes, runs only when the founder shares documents or asks for it. Source-cited deterministic math (YC, NVCA, Cooley GO) for SAFE/note conversion, priced-round dilution, anti-dilution, option pools, warrants, MFN chains, dual-class voting, QSBS and Israeli §102 dates, and Israeli ↔ Delaware flips. NOT for waterfall modeling, cumulative dividends, RSUs, 83(b), 409A, SPAC, warrant repricing, or pure term definitions.
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。
Telegram CLI for reading/searching messages, syncing archives, and sending or downloading files. Use when the user asks about Telegram chats, messages, contacts, groups, or files.
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。
Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.
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。
Rewrite text so it reads like a real person wrote it, not an AI. Auto-detects Russian vs English and applies the matching rule set. Use when the user asks to humanize text, remove the "AI smell", de-AI, or make AI output sound natural - especially for Russian. Russian requests count too - очеловечь, перепиши как человек, оживи текст, убери запах ИИ, сделай живее, звучит как робот, убери канцелярит. Also use when asked whether a text was written by AI - нейросеть писала?, похоже на ChatGPT?, видно ли, что это ИИ, человек или бот писал, did AI write this? - then check and report findings without rewriting. While installed, also keep your own Russian and English writing free of the quick-list stock phrases, silently; never edit the user's text unless asked.
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。
Audits websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.
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。
Slack workspace integration: 9 on-demand scripts (post, read, delete, search, stream, react, upload, channels, users) + dual-token support + RTS API search + chat streaming + AI Block Kit + Session Bridge + Claudicle unified launcher (Claude Agent SDK, soul engine, three-tier memory).
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。
Create text effects impossible with CSS alone — kinetic typography, flowing text around animated obstacles, calligrams, shrinkwrap chat bubbles, typographic ASCII art, glyph path animation, variable font waves, glyph morphing, illuminated manuscripts. Single-file HTML output, zero build step. Powered by @chenglou/pretext + opentype.js. Triggers on text effects, kinetic typography, text animation, calligram, glyph art, typography art.
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。
Research UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI patterns.
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。
Plan and record evidence-backed LLM and AI-search citation checks across approved assistant/search surfaces with timestamped queries, locales, modes, cited URLs, snippets, screenshots or transcripts, and caveats. Use this skill when the user asks whether ChatGPT, Perplexity, Claude, Gemini, Copilot, or another assistant cites a target site, but do not claim citation without direct evidence.
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。
Produces structured markdown session continuation documents for multi-session workflows. Captures active work streams, decisions with rationale, knowledge-file deltas learned during the session, the exact files to load next conversation, ingested content, artifacts, and open items into an ingestion-optimized handoff. Carries pending KF updates and unresolved items forward across handoff chains so context never drops. Generates a closeout checklist and echoes the starter prompt to chat. Use when approaching context limits, wrapping up a session, or managing work across conversations. Trigger on: "create a handoff," "session closeout," "wrap up this session," "build session handoff," "BSH," "continue next session," "we're running out of context," "context is getting long." Do NOT use for Memory optimization, context budget analysis, project audits, deciding whether work is ready for autonomous/Claude Code execution (use rootnode-handoff-trigger-check), or summaries with no continuation intent.
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。