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ClawHub Agent Skills author: SxLiuYu v1.0.0 MIT-0 13 files · 3 scripts body ≈ 546 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

自动分类 GitHub Issue，AI 打标签、分配负责人、检测重复、回复 FAQ

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationGitHubSlackAI and agentsSoftware 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
C
84/100
safety, quality, tests
Safety 60%
98
Quality 40%
64
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
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.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv config.example.json:14
    Reads a .env file (code comment; test fixture / example file)
    # 3. 运行：source .env && python3 scripts/triage.py --dry-run
    commentfixture
  • low Dangerous commands cmd-cron-mention TEST-REPORT.md:162
    Mentions editing / listing crontab (test fixture / example file)
    crontab -e
    fixture

Files scanned: 11. 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")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 34 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 546 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 42: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
The skill is mostly coherent, but it should be reviewed because it can change GitHub issues, send issue content to DashScope, and asks for broader GitHub token access than issue triage needs.
LLM: suspicious (high) · VirusTotal: · 12 Sept 2026