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AC openclaw-contributor

Contribute to the OpenClaw core repository using the repo's own CONTRIBUTING.md rules. Use when working in `openclaw/openclaw` or a fork to triage issues, plan a focused fix, choose the right validation commands, prepare AI-assisted PRs, route changes to the right subsystem maintainers, or avoid breaking OpenClaw contribution norms.

ClawHub Agent Skills author: manjaroblack v0.1.1 MIT-0 9 files body ≈ 874 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

ProcedureSoftware 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
90
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token README.md:5
      High-entropy token-like string (may be an id, hash or a credential)
      [![GitHub](https://img.shields.io/badge/GitHub-manjaroblack%2Fop…717?logo=…&logoColor=white)](https://github.com/manjaroblack/openclaw-contributor-skill)

    Files scanned: 9. 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 46 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 874 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 334: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 46 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 3)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This skill is a coherent OpenClaw contribution helper with expected local repo inspection and PR-prep scripts, and no evidence of hidden persistence, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026