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CF Moltbook Skill Auditor - PoW Provenance\n\nScans ClawdHub/Moltbook skills for threats (env reads, webhook exfil). Genera

molt security auditor

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 145 tokens Open the sourcegithub.com↗ analyzed 2 d ago

molt security auditor

As a process F 24/100 · Will not run — weak spots: steps, result and completion, when it triggers

AnalyzerAI 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
C
72/100
safety, quality, tests
Safety 60%
100
Quality 40%
30
Run on models
none yet
Process rating
F
24/100
Will not run
Steps w 15
0
Result and completion w 14
0
When it triggers w 12
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.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-long name is longer than 64 chars
  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 24/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (Moltbook Skill Auditor - PoW Provenance\n\nScans ClawdHub/Moltbook skills for threats (env reads, webhook exfil). Generates BTC-style hash chain for audits.\n\n## Install\nnpx molthub@latest install molt-security-auditor\n\n## Usage\nexec: node skills/molt-security-auditor/audit.js <skill_url_or_path>\n\n## Patterns\n- ENV_READ: .env|process.env\n- EXFIL: curl|fetch.*webhook\n- FS_ABUSE: readFileSync.*secret\n\n## PoW Chain\nSHA256(skill) + nonce grind → verifiable hash.\n\nExample:\n$ audit.js https://clawdhub.com/skills/weather/SKILL.md\nThreats: 0 | Chain: 0000abcd...) differs from the folder (molt-security-auditor)
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Execution cost. Instruction body is 145 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Description length 21: 120–800 characters recommended
  • +4Structure: 1 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions

Quality base 70; lint remarks subtract, signals add up to 100. Result: 30.