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概要と使いどころ
This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Guides discovery of local skills and enforces minimal documentation style.
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。
Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection
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。
Discovers all Claude Code agents in the system including built-in, plugin, project, and user-level agents. Use when you need to find which agents are available, understand the agent ecosystem, or prepare agents for Actoris registration.
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 this skill to create or update the root AGENTS.md file to register AgenticDev skills for AI agent discovery. Triggers include "register AgenticDev", "update AGENTS.md", "setup agent guide", or initializing a new project.
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 whenever the user wants to find, rank, or shortlist directories and registries where they can submit or list an MCP server (Model Context Protocol server) — to get backlinks, referral traffic, and discovery by agent builders. Triggers on "where do I list my MCP server", "best MCP directories", "MCP registries to submit to", "get my MCP server discovered", or "pull submission details for these MCP-directory domains", even when described indirectly (we built an MCP server, where do we publish it). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-ai-directories for general AI-tool / AI-agent / agent-skill listings, and to find-product-directories for general SaaS/software launches. Skip building an MCP server or asking how MCP works (DIY), finding a firm to build one (use find-ai-consultancy / find-software-developer), and MCP link-building *services*.
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 whenever the user wants to find, rank, or shortlist directories and listing sites where they can submit an AI product — an AI tool, AI app, AI agent, or agent skill / plugin — to get backlinks, referral traffic, and discovery. Triggers on "where can I list my AI tool", "directories to submit my AI agent", "agent-skills directories", "best AI tool directories for backlinks", "where do I get my GPT/Claude app discovered", or "pull submission details for these AI-directory domains", even when described indirectly (we built an AI agent, where do we get it in front of people). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-mcp-directories for MCP-server listings specifically, and to find-product-directories for general SaaS/software/app launches with no AI angle. Skip finding an AI consultancy/agency to hire (use find-ai-consultancy), comparing AI products ("ChatGPT vs Claude"), building an AI tool (do-the-work), and AI link-building *services*.
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。
Maintain portal public signing-key discovery at .well-known/jwks.json, reusable admin API directive parsing, opt-in admin private-key export, KMS key serialization, asymmetric issuer selection, base-path routing, and JWT verification tests. Excludes upstream OAuth provider JWKS consumption.
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。
Maintain upstream OAuth identity-provider directive parsers, shared configuration dispatch, OAuth/OIDC discovery, GitHub identity claims, JWKS and static public PEM verification, EdDSA/Ed25519 token validation, key refresh, and real portal OAuth E2E tests. Excludes portal signing-key publication and gatekeeper-owned direct OAuth sessions.
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 you want a structured messaging and positioning workshop that captures discovery answers and builds the messaging document.
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。
Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
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 preparing for a sales meeting, discovery call, demo, or customer check-in. Triggers: 'prep me for a meeting with [company]', 'meeting brief for [company]', 'pre-call prep', 'I have a call with [contact] at [company]', 'meeting prep', 'call prep'.
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。
First-run onboarding for the marketing harness. Walks new users through product discovery, generates MARKETING.md, and recommends the first command to run. Use when a user has just installed Kai and types /kai-start, "get started with Kai", "set up Kai", "first time using Kai", or when MARKETING.md doesn't exist yet.
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。
[Deprecated] discovery merged. Use `cm-skill-index` instead.
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。
[Deprecated] discovery and meta-skill merged. Use `cm-skill-index` instead.
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。
Customer discovery framework using Jobs-To-Be-Done theory — uncover the functional, social, and emotional jobs customers hire products to do. Produces JTBD canvases with job statements, outcome metrics, and competing solutions. Use alongside cm-brainstorm-idea for evidence-based product decisions.
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 a skill isn't triggering as expected, before adding a new skill (to check for a name collision), during periodic cleanup of an accumulated skills directory, or when asked to review skill quality — checks frontmatter validity, description quality against Skill Discovery Optimization conventions, body size, and cross-scope name collisions.
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 ARIS to run Markdown-based agent skills for literature review, idea discovery, cross-model critique, experiment planning, and paper-writing support.
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。
Strategic sales leadership guidance for B2B SaaS and enterprise software companies. Covers sales strategy, team building, pipeline management, enterprise selling, discovery calls, demos, proposals, negotiations, and sales operations. Use when building sales teams, designing sales processes, improving win rates, or scaling revenue operations. Use for "sales strategy", "sales process", "pipeline review", "deal strategy", "sales hiring", "quota planning".
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。
Begin a CRISP product discovery session — Phase C (Clarify)
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。
CRISP session manager — detects project state and routes to the right entry point. Use /crisp to start or resume any CRISP engagement. Handles new projects (discovery), existing codebases (archaeology), active sprints (execute), and validation (prove). Reads docs/crisp-state.json to know where you are.
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。
The Mileva Method (CRISP) — Phase 3: Investigate. Process mapping for existing or greenfield processes, user journey mapping, UX discovery, project goals, system architecture (integration map + data flow). Use after outcome alignment. Triggers on "investigate", "phase 3", "map the process", "process mapping", "user journey", "as-is process", or after Phase R exit checklist is complete.
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。
The Mileva Method (CRISP) — Phase 1: Clarify. Problem definition, eliciting, painkiller test, buy vs build, AI justification gate, Go/No-Go. Use at the start of any AI implementation project. Triggers on "clarify", "phase 1", "start discovery", "define the problem", "what problem are we solving", or at the beginning of a new client engagement.
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。
Architect a feature with the Carmack Council before writing code. Use when explicitly asked to plan a feature, do a "council plan", "carmack plan", or invoke /council-plan. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipeline), Karri Saarinen (UI quality), Vitaly Friedman (UX quality). Interactive feature discovery followed by parallel subagent dispatch. Produces a sequenced, attributed implementation plan with no code. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.
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。
Scout the TouchDesigner community for what's HYPED right now — community showcases, recent tutorials, generative-AI bridges, hardware interaction trends, visual-aesthetic trends of 2025-2026 — then propose tdmcp tools that ride those trends AND are easy to build. Use whenever the user wants to brainstorm new feature ideas based on what's trending in TouchDesigner, asks for 'hype' or 'trending' features, asks 'what are people doing in TD right now / what's hot / what's hype', wants tools inspired by community trends, asks to scout TD trends/aesthetics/integrations, or says things like 'ideias hype', 'novas ideias', 'o que está em alta', 'criar ferramentas para o que está bombando', 'tendências do TouchDesigner'. Also for follow-ups: refresh, rescout one surface, re-rank under another profile, deepen a trend, or filter for buildable-easy items. This is an EXTERNAL trend ideation harness — complementary to tdmcp-feature-discovery (which is INTERNAL gap analysis). It produces `_workspace/hype-scout/HYPE_TOOL_BACKLOG.md` ranked by Hype × Build-Ease; it does NOT build. Once a feature is chosen from the backlog, hand it to tdmcp-pipeline.
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。