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概要と使いどころ
Unified code intelligence — Skeleton Index (zero-dep, <4s) + AST knowledge graph (CodeGraph) + architecture diagrams (Mermaid) + smart context builder. Pre-indexes code structure so AI agents understand any codebase instantly. 95% token compression for onboarding. 30% fewer tokens for deep analysis.
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。
Unified code intelligence — routes to Skeleton Index, CodeGraph, Architecture Diagram, or Smart Context Builder based on task shape. Loads deep refs on demand.
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。
Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.
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Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.
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。
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.
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。
Analyzes Bazel build dependency graphs to identify bottlenecks and optimize build times. Uses the Bazel Query Language (BQL), Action Graph API, and Build Event Protocol (BEP) for deep build analysis.
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。
audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.
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。
aerc is a terminal-based email client written in Go with first-class support for IMAP, SMTP, JMAP, Maildir, and Notmuch. It features Vim-like keybindings, email threading, HTML rendering, built-in patch management, and deep UNIX integration through pipes and commands.
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。
Aider is an open-source AI pair programming tool that runs in your terminal, letting you collaborate with LLMs to edit code across your entire codebase. With 42k+ GitHub stars and 15 billion tokens processed weekly, it features deep git integration, repo-wide code mapping, and support for 100+ programming languages.
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。
Expert product demonstration specialist for SaaS and B2B software. Use when preparing demos, structuring demo presentations, tailoring to stakeholders, handling objections during demos, managing live vs sandbox environments, or improving demo conversion rates. Covers remote and in-person demos, multi-stakeholder presentations, technical deep-dives, and executive briefings.
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。
Perform a rigorous Carmack Council code review. Use when explicitly asked to review code, do a "council review", "carmack review", or invoke /council-review. 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 pipelines), Karri Saarinen (UI quality), Vitaly Friedman (UX quality), Kent Beck (test quality). Uses parallel subagents for deep, independent review. Produces prioritised P1/P2/P3 findings. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.
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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline 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。
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。
Use for a standard, single-pass security audit of an entire repository or a scoped path, package, folder, or submodule with no diff to review. This is the default repository scan. Do not use for PR, commit, branch, or working-tree diffs, or for deep, multi-pass scans.
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 developing, reviewing, packaging, debugging, or answering questions about DeepSeek Harness (DSH) plugins — the plugin-based agent harness on vendored Cordis. Applies the official plugin-development constraints (plugin contract, cordis.yml layers, services/events/effects, tool DSL, bundles/profiles) backed by the dsh-plugin-guide knowledge base.
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Use when starting a new project. Genesis finds real production failures from similar GitHub repos, then builds a working MVP immediately - not just documents. Two modes: fast-mvp (5 min research cap, builds right after) and deep-research (full analysis). Success = user can run the project. Triggers on: "genesis init [vision]", "I want to build X", "scaffold", "new project", "set up project", "start building", "create a tool", "make a CLI", "bootstrap", "בנה פרויקט", "צור פרויקט", "התחל פרויקט".
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 SonarJS-derived rules in .NET repositories that ship JavaScript or TypeScript frontends and need deeper bug-risk, code-smell, or cognitive-complexity checks than a minimal ESLint baseline. Use when the repo wants `eslint-plugin-sonarjs` locally or already runs SonarQube or SonarCloud in CI.
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。
Add or update automated tests for a change using the repository’s verification rules in `AGENTS.md`. Use when implementing a feature, bugfix, refactor, or regression test; prefer stable integration/API/UI coverage and pull deeper test strategy from the bundled references.
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 writing a PR title/description, a commit message body, a code review comment, or an issue — in Hedgehog's own repo or any consuming project. Triggers on "open a PR", "write the PR description", "comment on this PR", "file an issue". Covers writing style (terse, info-dense, Simplified Technical English), the Why/What shape, folding deep reasoning under `<details>`, and the pre-open checklist (CI status, scope, verified claims only).
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。
Conversational design ideation agent that researches trends, explores visual directions, and refines ideas through adaptive questioning — then produces a rich PRD document and auto-generates Stitch screens. Your design buddy that thinks deeply before designing.
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Use for web/current lookups. Triggers: search, look up, latest, news, prices, docs, status, X/Twitter, deep research, 검색, 검색해, 찾아봐, 찾아줘, 알아봐, 웹검색, 딥리서치, 심층 조사.
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。
Synthesizes exploration findings into unified analysis with deep investigation capabilities (shell commands, git history, dependency analysis) and completeness evaluation (converted from agent)
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。