SKILLEMALL.ai

AC debug-root-cause

系统化根因分析:用 20 种 RCA 方法替代随机试错。 当工具返回 error 或不符合预期的结果、同一操作反复失败、 或用户说"排查"、"为什么"、"还是不对"时,必须使用此 Skill。 关键词:报错、对不上、排查、根因、still broken、why、重复失败、不符合预期。 即使用户没有明确说"根因分析",只要工具返回了非预期结果或用户要求排查,都应触发。

ClawHub Agent Skills author: MaoChen1980 v1.0.2 MIT-0 3 files body ≈ 1 353 tokens Open the sourceclawhub.ai analyzed 31 h ago

系统化根因分析:用 20 种 RCA 方法替代随机试错。 当工具返回 error 或不符合预期的结果、同一操作反复失败、 或用户说"排查"、"为什么"、"还是不对"时,必须使用此 Skill。 关键词:报错、对不上、排查、根因、still broken、why、重复失败、不符合预期。…

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 3. 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 63/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 23 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1353 tokens
  • 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 184: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This looks like a debugging aid, but it should go through Review because it can activate broadly, write local notes, and points agents toward self-modifying workflows.
LLM: suspicious (medium) · VirusTotal: · 12 Jun 2026