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BB review-architecture

Review a written architecture artifact — an ADR (docs/adr/), a broader architecture doc, the architecture section of a design doc, or a domain-reference kit (glossary + traps pages, reviewed as one composite artifact) — against the system it claims to describe. Verifies the EXISTENCE and TOPOLOGY of declared mechanisms (structural boundaries, data ownership, NFR mechanisms, failure isolation, state consistency, evolution/versioning, domain binding) plus decision soundness, reversibility, and provenance (self-certification of reverse-engineered claims). Use after an ADR, architecture doc, domain glossary, or domain kit is written, before or during implementation. NOT for behavioral/runtime correctness (that is $kk:review-code and $kk:review-spec). Security architecture is out of scope — delegate threat modeling to the PAL secaudit tool (mcp__pal__secaudit).

serpro69/claude-toolbox Agent Skills author: serpro69 NOASSERTION 69 files body ≈ 1 446 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Review a written architecture artifact — an ADR (docs/adr/), a broader architecture doc, the architecture section of a design doc, or a domain-reference kit…

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerSoftware developmentSecuritytype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1446 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Description length 868: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.