Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assesses architecture decisions, ADR compliance, and coupling. Use when evaluating design changes or validating structural decisions before merging.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Run pytest plugins/pensive/tests/skills/test_architecture_review.py to verify review logic.
Architecture assessment against ADRs and design principles.
/architecture-review
Load modules based on review scope:
modules/adr-audit.md (~400 tokens): ADR verification and documentation.modules/coupling-analysis.md (~450 tokens): Dependency analysis and boundary violations.modules/principle-checks.md (~500 tokens): Code quality, security, and performance.modules/fpf-methodology.md (~800 tokens): FPF (Functional, Practical, Foundation) multi-perspective review methodology.modules/ceremony-audit.md (~600 tokens): Manual lenses for mapping layers that never diverge. Passthrough mappers, twin types, speculative DTOs, single-implementation interfaces. Includes the IO boundary counter-signal.Load all modules for full reviews. For focused reviews, load only relevant modules.
arch-review:context-established: Repository, branch, motivation.arch-review:adr-audit: ADR verification and new ADR needs.arch-review:interaction-mapping: Module coupling analysis.arch-review:invariant-check: Invariant conflict detection and 3-option analysis.arch-review:principle-checks: LoD, security, performance.arch-review:ceremony-audit: Mapping layers that never diverge.arch-review:risks-actions: Recommendation and follow-ups.arch-review:findings-verifiedarch-review:context-established)Confirm repository and branch:
pwd
git status -sb
Document:
arch-review:adr-audit)Load: modules/adr-audit.md
arch-review:interaction-mapping)Load: modules/coupling-analysis.md
arch-review:invariant-check)Before checking principles, identify whether the changes conflict with existing design invariants. This is the highest-judgment step in architecture review: models get this wrong more often than any other call.
Identify existing invariants:
# Detect boundary crossings in changed files
git diff --name-only | while read f; do
head -20 "$f" 2>/dev/null | rg "^(import|from|use |require)" || true
done
When a conflict is detected:
Do NOT recommend a resolution. Present the three options and escalate to human judgment:
| Option | When Right | When Wrong |
|---|---|---|
| Preserve invariant (reject feature) | Invariant simplifies many things; feature is marginal | Feature is genuinely needed and invariant is stale |
| Layer on top (add inelegantly) | Feature is needed; invariant still valuable; imperfection is OK | Layering creates a maintenance trap that will compound |
| Revise invariant (change the design) | Genuine new learning invalidates the original reasoning | You're "cleaning up" a decision you don't fully understand |
Output format:
### Invariant Conflicts
[I1] **[Invariant name]** — [what decision it represents]
- **Location**: file.py:42
- **Anchor**: `verbatim source text at line 42`
- **Conflict**: [what change clashes]
- **Options**: Preserve / Layer / Revise
- **Recommendation**: ESCALATE TO HUMAN
- **Risk if wrong**: [what compounds]
Why this matters: Bad invariant decisions compound. After a few wrong calls the codebase becomes unsalvageable. This is a judgment problem rather than a context problem: the agent should surface it, not solve it.
arch-review:principle-checks)Load: modules/principle-checks.md
arch-review:ceremony-audit)Load: modules/ceremony-audit.md
Coupling analysis (Step 3) finds boundaries that leak. This step finds the opposite failure: boundaries that cost something and separate nothing.
Each finding must name the need the ceremony serves today. If no current need can be named, the ceremony is the finding.
Do not flag mappers at an IO boundary. They are load-bearing even when they look like passthroughs, because they stop future internal fields from escaping. See the counter-signal in the module.
arch-review:risks-actions)Summarize using imbue:diff-analysis/modules/risk-assessment-framework:
List follow-ups with owners and dates.
Provide recommendation:
arch-review:findings-verified)Write findings to .review/findings.json, run the citation verifier
(Skill(imbue:review-core) Step 5), and drop or label UNVERIFIED any
the verifier rejects.
Location + verbatim Anchor
confirmed by citation_verifier.py (exit 0), or unverified findings
were dropped or labeled UNVERIFIED.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.