SKILLEMALL.ai
GitHub ↗ RU Sign in

AC skill-audit

Audits Claude skills from GitHub repositories for effectiveness, token usage, safety, and best-practice compliance, then automatically generates bilingual social media posts about the findings. Use when the user wants to audit a skill, review a skill from GitHub, analyze a SKILL.md, evaluate skill quality, or check a skill for safety and permission issues.

ClawHub Agent Skills author: Dr. Ren v1.0.0 MIT-0 7 files · 1 script body ≈ 1 153 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerGitHubAI and agentsMarketingData and analyticstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Consistency w 8
40
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: 7. 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

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (skill-audit) differs from the folder (fenz-skill-auditor)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 46 steps
    • 100Execution cost. Instruction body is 1153 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 358: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    The skill has a legitimate audit purpose, but it runs an unbundled sibling post-generator script and processes cloned repository contents with limited safety boundaries.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026