Assesses architecture decisions, ADR compliance, and coupling. Use when evaluating design changes or validating structural decisions before merging.
npx skills add https://github.com/athola/claude-night-market --skill architecture-review
arch-review:context-established))arch-review:adr-audit))arch-review:interaction-mapping))arch-review:principle-checks))arch-review:risks-actions))Run pytest plugins/pensive/tests/skills/test_architecture_review.py to verify review logic.
Architecture assessment against ADRs and design principles.
/architecture-review
skills
skills
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:
status
that previously didn't?)
new direction?)
shape?)
introduced alongside an existing one?)
# 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)Every finding must cite a real location and a verbatim anchor. Write
findings to .review/findings.json and confirm each citation resolves:
python plugins/imbue/scripts/citation_verifier.py \
--findings .review/findings.json --repo-root .
Drop or label UNVERIFIED any finding the verifier fails (exit 1); only
verified findings enter the report. See Skill(imbue:review-core) Step 5
and Skill(imbue:structured-output) for the schema.
invariant conflicts surfaced, principle checks run, risks and actions
documented.
Location + verbatim Anchorconfirmed by citation_verifier.py (exit 0), or unverified findings
were dropped or labeled UNVERIFIED.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
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".
Take athola/architecture-review from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.