Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
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。
5-phase planning: PRD analysis, design review, technical decisions, dynamic research team, metrics. Produces a complete implementation plan + ADRs before any code is written.
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。
Analyze the request and produce a complete implementation plan through structured phases. No code is written. Every significant decision is recorded. Run /clear after this command before running /plan-pipeline:validate.
Skip if no PRD exists (refactor, infra change, bug fix).
Read all PRD files and docs/INFORMATION_ARCHITECTURE.md if present. Scan the codebase to understand current implementation status.
Surface findings in 3 buckets:
Missing requirements: acceptance criteria that are absent or incomplete Ambiguous requirements: items with multiple valid interpretations Compliance concerns: security, data privacy, API contract implications
For each finding: present options with concrete pros/cons. Discuss with user. Record every decision in the plan file under a ## Decisions section before moving on. Do not proceed past unresolved ambiguities.
Skip if no UI changes are in scope.
Read: DESIGN_SYSTEM.md, existing UX ADRs, CLAUDE.md UX rules.
Produce specs for:
prefers-reduced-motion fallbacksCreate Design ADRs for significant UX decisions (choice of interaction pattern, new animation convention, platform divergence). Record minor layout choices directly in the plan file.
Spawn 1-2 Explore agents for targeted codebase research. Run them in the background via Task tool.
While agents run, check:
docs/adr/: if 3+ ADRs confirm a decision → auto-resolve without askingWhen agents return: present architecture decisions with 2-3 options each, concrete pros/cons, and a recommendation. Ask for user input on each unresolved decision.
For each significant decision:
docs/adr/ADR-XXXX.md using standard Nygard format (Context / Decision / Status / Consequences)docs/adr/PATTERNS.md with the new observationApply trigger rules to determine which research agents are needed. Present the proposed team with justification for each inclusion.
Research agent pool:
| Agent | Trigger | Model |
|---|---|---|
code-explorer | Always | Sonnet |
arch-researcher | Changes touch 2+ architectural layers | Sonnet |
database-analyst | Any DB schema change | Sonnet |
security-analyst | Auth, payments, PII, RBAC, rate limiting | Opus |
test-analyzer | Non-trivial feature (not just a bug fix) | Sonnet |
cross-platform-specialist | Web + mobile parity required | Sonnet |
native-app-specialist | Tasks touch mobile/native UI package | Sonnet |
design-system-researcher | UI changes in scope | Sonnet |
dependency-researcher | New packages being added | Sonnet |
devops-specialist | Docker, env vars, CI/CD changes | Sonnet |
integration-researcher | New services, libraries, OTEL config | Opus |
planning-coordinator | Always, when 2+ agents selected | Opus |
Tier labels (descriptive, not prescriptive):
Tell the user: "I recommend a [Tier N - Label] team: [agent list with one-line justification each]. Want to add or remove any agents?"
Wait for approval before Phase 4.
Tier 0: Conduct inline research. Write plan directly without spawning agents.
Tier 1+: Spawn approved agents in parallel using Task tool (run_in_background: true). For each agent, provide:
Monitor agents by reading their output files directly (TaskOutput was removed in v2.1.278; use Read on the output path reported by the agent instead). Report progress: "3/6 agents complete..."
When all agents return: if planning-coordinator was spawned, send it all agent reports and have it synthesize the final plan. Otherwise, synthesize directly.
Plan file structure (docs/plans/plan-{name}.md):
# Plan: {feature-name}
Created: {date} | Branch: {branch-name} | Tier: {N}
## Summary
One paragraph: what this implements and why.
## Decisions
Decisions recorded during Phase 1 (PRD analysis).
## Architecture
ADRs created, patterns applied, architectural choices made.
## Tasks
Ordered task list with layers (1 = foundation, 2 = depends on 1, etc.)
### Layer 1
- [ ] Task A: description, files affected, acceptance criteria
- [ ] Task B: description, files affected, acceptance criteria
### Layer 2
- [ ] Task C (depends on A): description, files affected, acceptance criteria
## Test Plan
How each task will be verified. TDD tasks marked explicitly.
## Integration Verification
Smoke test commands to run post-execution (if backend/services in scope).
## Out of Scope
What this plan explicitly does not address.
Commit: plan file + ADR files + agent report manifests.
Record timestamps, phase durations, agent counts, and cost estimates in docs/plans/metrics/{name}.json. Commit.
If Phase 1 produced no unresolved ambiguities and Phase 2 produced no unresolved decisions: auto-start /plan-pipeline:validate without asking.
If any human discussion occurred: ask "Ready to validate this plan?" before proceeding.
/plan-pipeline:start
Provide the feature description or point to a PRD file when prompted. The command handles the rest interactively.
Use for any non-trivial feature: anything touching more than 2 files, involving architecture decisions, or where a planning mistake would be expensive to undo.
For simple changes (typos, trivial refactors): use /plan mode instead.
/plan-pipeline:ceo-review → product direction locked
/plan-pipeline:eng-review → architecture locked
/plan-pipeline:start → produce implementation plan ← you are here
/plan-pipeline:validate → validate before execution
/plan-pipeline:execute → execute to merged PR
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
Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
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。
Codebase health audit scoring 7 categories with progression 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。
Autonomous improvement loop: scan codebase metrics, scaffold experiment files, run agent-driven iterations until metric improves
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。
Generate bounded independent candidates, score them against a frozen rubric, and verify the selected result with a proof log.
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。
Post-deploy monitoring: watch production after a deploy and alert on regressions
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。
Restore context after /clear by summarizing recent work and project state
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。