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概要と使いどころ
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。
Decide what to borrow, adapt, or write before building something. Use when the user describes an app, tool, CLI, or feature they want to build and needs to know which parts already exist as open-source GitHub repos (BORROW), which are worth reading and adapting (KITBASH), and which to just write (WRITE). Every repository named is checked against the live GitHub API, so invented ones never reach the user.
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 SEO content strategy for SaaS and B2B sites. Use when doing keyword research, planning content clusters, creating pillar pages, optimizing on-page SEO, fixing technical SEO issues, building links, refreshing content, implementing programmatic SEO, mapping search intent, optimizing for SERP features, or measuring content performance. Use for SEO audits, content gap analysis, and organic growth strategy.
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 website copywriting for SaaS and B2B companies. Use when writing homepage copy, feature pages, about pages, pricing pages, comparison pages, product tours, integration pages, use case pages, demo/contact pages, or navigation microcopy. Use for website messaging hierarchy, value proposition development, and page-level conversion 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。
Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, behavioral analytics, and impact measurement.
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 competitive intelligence and strategy guidance for B2B/SaaS companies. Use when conducting competitive research, creating battlecards, analyzing win/loss data, building market landscape maps, positioning against alternatives, handling competitive objections, writing comparison pages, or developing sales enablement for competitive situations. Use for competitive monitoring, feature comparisons, and market intelligence gathering.
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 specification and documentation writer. Use when creating PRDs, user stories, acceptance criteria, technical specifications, API documentation, edge case analysis, design handoff docs, feature flag plans, or success metrics. Covers the full spectrum from high-level requirements to implementation-ready specifications.
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。
Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
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。
Release tdmcp: write the Keep-a-Changelog entry, bump the SemVer version across every manifest, then (autonomously, per project policy) commit, tag, and push — gated on a td-qa PASS, with hard git safety rails. Use when releasing/shipping/cutting a tdmcp version, writing the CHANGELOG, bumping the version, or tagging a release.
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 implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK can be answered directly.
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。
How to author one new tdmcp MCP tool the house way — the file shape (XImpl + registerX), the Python-bridge pattern (buildPayloadScript/parsePythonReport over executePythonScript), the result helpers (errorResult/guardTd/jsonResult/structuredResult), fail-forward error handling, ESM/.js + Biome style, and the offline msw unit test that proves it. Load this whenever building, extending, reviewing, or testing a tdmcp tool file under src/tools/** (and its test under tests/unit/**). Use it for every Phase-13 / v0.5.0 feature-build tool and any re-run after a gate failure.
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 wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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 guided setup for a fresh gAIOS clone. Interviews the user (identity, role, priorities, voice, connections, team, pain, guardrails) and fills the templates — CLAUDE.md, context/, references/voice.md, connections.md — then introduces the WAT / wiki / workflows / experiments features. Trigger on "/setup", "set me up", "get started", "onboard me", or a fresh clone where CLAUDE.md still has {{placeholders}}.
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 to conduct STRIDE threat modeling for a system or feature design.
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 to evaluate a service or feature against Downe's 15 principles of good 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 to verify a feature/story meets all Definition of Done criteria before marking 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。
Use to assess Privacy by Design compliance and GDPR/data protection alignment for a feature or system.
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。
Read GitHub Actions CI for the current branch or open PR, triage failing checks, reproduce locally, and fix scoped issues until green. Use whenever the user mentions CI failures, red checks, failed pipeline, GitHub Actions, merge blocked, fix the build, babysit CI, gh run failed, ruff/ty errors, failing pytest, example run failed, or asks to check CI on this branch — even if they only paste a log snippet. Also use after implementing a feature when tests, lint, or typecheck might have broken.
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。
Write modern, version-aware C# for .NET repositories. Use when choosing language features across C# versions, especially C# 13 and C# 14, while staying compatible with the repo's target framework and `LangVersion`.
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。
Apply MCAF non-functional-requirements guidance to capture or refine explicit quality attributes such as accessibility, reliability, scalability, maintainability, performance, and compliance. Use when a feature or architecture change needs explicit quality attributes and trade-offs.
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。
Apply MCAF ML/AI delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Use when the repo includes model training, inference, data science workflows, or ML-specific delivery 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。
Turn a raw product idea into build-ready docs — one doc per EPIC with locked decisions, exact API contracts, a story-by-story priority plan, and an execute.md handoff Claude can build from across sessions. Gets the clarity by interrogating the user through a roster of expert personas that grill, counter, and refuse to flatter. Use to ideate, validate, scope, architect, or plan a product, feature, startup, or app before any code is written. Invoke by persona name or acronym to summon one expert, or with no argument to run the full lifecycle end to end.
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。
Designs implementation blueprints for features using exploration findings and architectural best practices (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。