SKILLEMALL.ai
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AC code-analysis

This skill produces a DESCRIPTIVE Git-history reflection report. It is intended ONLY for: (a) a developer running it on their own repository for self-reflection, or (b) an opt-in, consent-based team retrospective on a shared repository. The skill MUST NOT be used to evaluate, rank, compare, discipline, or surveil individual workers, or to support performance reviews, compensation, promotion, or any HR decision. Trigger only on explicit, intent-clear requests such as: "generate a Git-history reflection report on my own repo", "self-reflection on my commit patterns", "team retrospective with everyone's consent", "对我自己的仓库做一个 Git 历史自查报告", "团队复盘（已获每个人同意）". Do NOT trigger on broad phrases like "analyze code", "代码分析", "developer evaluation", or "compare developers" — those are too vague and risk activating people-data analysis without consent. When the request is ambiguous, ask the user to clarify intent and confirm they have informed consent from every developer whose history will be read.

ClawHub Agent Skills author: enoyao v1.1.1 MIT-0 36 files · 25 scripts body ≈ 4 076 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

This skill produces a DESCRIPTIVE Git-history reflection report.

As a process C 55/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerGitHubSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
55/100
Has gaps
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: 33. 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 55/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 28 mutating operations with no state check
    • 40Consistency. Frontmatter name (code-analysis) differs from the folder (code-analysis-skills)
    • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4076 tokens
    • 85Steps. 72 steps, 1 vague phrases
    • low 11 top-level sections: this looks like several domains in one skill

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 998: 120–800 characters recommended
    • -219 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 72 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    In the sandbox The scripts kept to themselves

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 12 скриптов, ещё 5 не поместились в лимит; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    Из них 10 не дошли до работы, и об их поведении мы ничего не узнали.

    publish.shне запустился: ❌ 'clawhub' CLI not found in PATH. Install it first:
    src/main.pyне запустился: ModuleNotFoundError: No module named 'click'
    src/scanner.pyне запустился: ModuleNotFoundError: No module named 'git'
    src/analyzers/base_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/cadence_signal_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/code_quality_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/code_style_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/commit_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/efficiency_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/analyzers/work_habit_analyzer.pyне запустился: ModuleNotFoundError: No module named 'pydriller'
    src/narrator/reflection_narrator.pyничего за пределами своей папки
    src/reporters/base_reporter.pyничего за пределами своей папки

    21 Sept 2026

    External checks

    ClawHub: suspicious
    The skill is a disclosed local Git-history reflection tool, but its handling of personal developer data has consent-scoping and report-rendering risks that need review.
    LLM: suspicious (high) · VirusTotal: · 11 Sept 2026