SKILLEMALL.ai

BD auto-hook

检查指定 SKILL 是否存在偷懒、跳步、简化执行等问题,并确保该 SKILL 末尾附有自审计钩子(autohook); 也可撤销已注入的钩子,将 SKILL 恢复原样。 支持 Claude Code、OpenAI Codex CLI、OpenClaw 三大智能体,自动适配 Linux / macOS / Windows 路径。 【注入触发词】:「检查 XX skill 有没有偷懒」「审计 XX skill」「给 XX skill 加钩子」「XX skill 有没有 hook」 「验证 XX skill 执行质量」「XX skill 是否规范」「帮我找 XX skill 在哪」。 【撤销触发词】:「停止检查 XX skill」「不用检查了」「取消 hook」「删除钩子」「移除 XX skill 的自审计」 「XX skill 去掉 autohook」「关掉审计」「撤销钩子」「还原 XX skill」。 只要用户提到上述任意触发词,立即触发本 skill。

ClawHub Agent Skills author: panhongwei v1.0.0 MIT-0 4 files body ≈ 1 717 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmentAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
D
46/100
Unfinished process
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 · 0

✓ No critical or high findings

Files scanned: 4. 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 46/100

  • 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
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1717 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)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 433: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (18 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This skill openly aims to add audit hooks, but it can persistently change how other skills behave and write local audit files without enough user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026