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AI-powered code review assistant for OpenClaw agent developers. Automatically analyzes code quality, detects security vulnerabilities, performance issues, and provides actionable improvement suggestions. Built specifically for AI agent code with a focus on safety, best practices, and maintainability.

ClawHub Agent Skills author: supermario11 v1.0.0 MIT-0 7 files body ≈ 1 796 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process F 36/100 · Will not run — References files that are not bundled: CONTRIBUTING.md, LICENSE, docs/README.md

AnalyzerSoftware developmentAI and agentstype 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
B
79/100
safety, quality, tests
Safety 60%
94
Quality 40%
56
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: CONTRIBUTING.md, LICENSE, docs/README.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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 · 6

✓ No critical or high findings

Medium and low: 6
  • low Secrets in code secret-labelled-token code-review-report.md:28
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder
  • low Secrets in code secret-password-literal code-review-report.md:28
    Hard-coded password / key literal (may be an example) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder
  • low Secrets in code secret-labelled-token code-review-report.md:50
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder
  • low Secrets in code secret-password-literal code-review-report.md:50
    Hard-coded password / key literal (may be an example) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder
  • low Secrets in code secret-labelled-token test-bad-code.js:4
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder
  • low Secrets in code secret-password-literal test-bad-code.js:4
    Hard-coded password / key literal (may be an example) (placeholder value)
    const API_KEY = "sk-1…def";
    placeholder

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: CONTRIBUTING.md
  • warning missing-ref reference to a missing file: LICENSE
  • warning missing-ref reference to a missing file: docs/README.md

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: CONTRIBUTING.md, LICENSE, docs/README.md
  • 0Tools and files. 3 referenced file(s) missing: CONTRIBUTING.md, LICENSE, docs/README.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (code-reviewer) differs from the folder (cuihua-code-reviewer)
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 57 steps
  • 100Execution cost. Instruction body is 1796 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 14 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • -5TODO / placeholder text left in the skill
  • -242 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 303: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 57 items
  • +3Output format is stated explicitly
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.

External checks

ClawHub: clean
This is a local code-review skill whose risky-looking code is mostly detection patterns, examples, and intentionally vulnerable fixtures, but users should treat reports as sensitive before sharing them.
LLM: benign (high) · VirusTotal: · 29 May 2026