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AB code-review

Review a code change for correctness, security, maintainability, and test adequacy, and reach a defensible verdict. Use when reviewing a pull request, diff, patch, or AI-generated change; when scoping an oversized review into reviewable units; when writing or triaging review comments; when assigning reviewers or setting review policy; or when investigating why a defect escaped review. Covers change sizing and attention budgets, risk-tiered checklists, severity-labeled comments, verdict and residual-risk records, review metrics that resist gaming, and the measured limits of checklists and AI reviewers. Do not use for implementing the requested change, root-cause debugging of a failing test or incident (use systematic-debugging), security assessment of a whole system (use security-audit-methodology), designing new code securely (use secure-software-engineering), architecture review across systems (use software-architecture), or reverse-engineering an unfamiliar codebase (use software-architecture-analysis).

magnus919/agent-skills Agent Skills author: magnus919 21 files · 4 scripts body ≈ 3 609 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Review a code change for correctness, security, maintainability, and test adequacy, and reach a defensible verdict.

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

AnalyzerSoftware developmenttype 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
69/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 19. 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 69/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 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
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 37 steps
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3609 tokens
    • low No test case covers injection arriving through data

    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 1020: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)
    • +3All 4 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.