Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Open warmly with one or two examples from
../../references/examples-bank.md, then interview
the founder into a build sheet. No API key needed in this phase — the output
is a plan.
| Slot | Question | Maps to |
|---|---|---|
| Job | "What one job should this agent do end-to-end?" | agent.system + outcome description |
| Trigger | "What kicks it off — you ask it, an event, or a schedule?" | on-demand / event / cron |
| Inputs | "What does it read?" (files, repo, memory, gmail/slack/github, web) | resources / MCP servers / memory |
| Actions | "What does it do?" (draft, write, call APIs, run code) | agent toolset / custom tools / MCP |
| Done | "How would you grade a good run?" | outcome rubric (required) |
| Recurrence | "Once, on request, or on a cadence?" | single-pass / grade-loop / cron-loop |
See ../../references/interview-to-config.md
for the full mapping.
python3 scripts/interview_planner.py \
--job "Triage overnight support email" --trigger schedule \
--inputs "gmail,memory" --actions "label,reply" \
--dod "one label per email, grounded reason, no invented facts" \
--recurrence daily --out ./my-agent/plan.json
always_ask.python3 scripts/build_sheet_builder.py --plan ./my-agent/plan.json --out-dir ./my-agent
python3 scripts/primitives_validator.py --sheet ./my-agent/build-sheet.json
goal_state.py set --phase stage-launch --artifact build_sheet=./my-agent/build-sheet.json, then advance.scripts/interview_planner.py — answers → primitives skeleton + deferrals.scripts/build_sheet_builder.py — assemble/normalize build-sheet.json.scripts/primitives_validator.py — validate vs CMA limits (PASS/WARN/FAIL).まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
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。
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
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。
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
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。
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
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。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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。
Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
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。