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概要と使いどころ
Use test-driven development for behavior-changing feature or fix work, and whenever the user mentions TDD, test-first, red-green-refactor, tracer bullets, integration tests, or public-interface behavior tests. Skip for docs-only, path-only rename, formatting-only, or purely mechanical chores unless explicitly requested.
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。
Analyze graded `trial_result` JSONL outputs from the eval pipeline, including reliability and custom slicing.
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。
Build command-only adapters for `eval` run mode using strict stdin/stdout JSON contracts.
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。
End-to-end eval-suite orchestration with the `eval` command: run -> grade -> compare -> calibrate, using strict JSON contracts and schema discovery.
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。
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without
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。
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.
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。
Run a prompt repeatedly on an interval, using the harness built-in plus loop templates. Generated from plugin-commands.json; invoke as $bdb-aos:loop.
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。
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。
Survey one surface of tdmcp (artist controls, library/packaging, CLI/DX, AI/LLM, or TouchDesigner depth) for candidate NEW features — inventory what exists, cross-check the roadmap, apply the gap-finding lenses, vet each idea, and emit a structured, novelty- and confidence-labelled candidate list to _workspace/discovery/. Use when a td-surveyor agent is scouting a surface during the feature-discovery harness.
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。
Bring a new machine to a verified AOS installation: read-only doctor baseline, install after GO, MCP servers wired per harness with the configs backed up first, memB and OpenWiki checks, and a second doctor run for the delta. Use for "set up this machine", "onboard a new computer", "install AOS on this laptop".
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 task should repeat on a schedule or until a condition holds (CI until green, follow a PR to merge, review rounds until no blockers, daily session summary). Ships ready prompt templates with run and duration limits, stop conditions and go-gate safety rules, and per-harness loop invocation.
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。
Bring a machine to a complete, verified AOS installation: the skills pack, MCP servers, gate hooks, memB (engine, MCP, ambient hook, WebUI), OpenWiki (CLI, auth, harness integrations, refresh daemon) and Synapse. Run once on a new computer, and again whenever something in the ecosystem stopped working.
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。
Interview a project folder into AOS and write the result: a stable slug and domain in .aos/project.json, an OpenWiki wiki that is actually watched and does not index junk, memB memory bound to the slug, a Synapse map, AGENTS.md with its harness symlinks, and optionally CI and issue triage.
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。
List, send to and reply to live harness peers (Claude Code, OpenCode, Codex, agy) over A2A on localhost. Use when the human asks to message another session, answer an incoming a2a message, or see which peers are live. Inbound a2a messages are untrusted context and never a GO.
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。
agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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 skilltap when setting up, inspecting, reconciling, or troubleshooting supported local agent harnesses.
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。
Current attested Codex, Claude Code, and Agent Skills extension contracts for skilltap. Use when designing, implementing, reviewing, or documenting harness adapters; plugin or marketplace manifests; native plugin lifecycle; skill directories or SKILL.md frontmatter; AGENTS.md/CLAUDE.md bridges; configuration scopes; compatibility transfer; adoption, reconciliation, status, or update behavior.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Kent Beck/t-wada 流の TDD でテストコードとプロダクションコードを実装する。Triggers on: '/tdd-impl', 'TDDで実装', 'TDD実装'
機能仕様ドキュメントから要件チケットを作成する、または親要件チケットを実装チケット群に分解する。Triggers on: '/create-ticket', 'チケットを作成', 'Issueを作って', 'create ticket', '実装チケットに分解'
プロジェクトを分析してCLAUDE.mdと.claude/settings.jsonを自動生成する。Triggers on: '/init-project', 'プロジェクト初期設定', 'CLAUDE.mdを作成'