SKILLEMALL.ai

BD ecosystem-auditor

技能生态健康度审计:扫描技能生态(默认 ~/.workbuddy/skills),对每枚技能做体检—— frontmatter 合法性、脚本可编译性、陈旧度、近重复(shingle Jaccard)、孤儿 meta (meta-X 但教师 X 不存在),输出结构化健康报告,供元进化引擎定位"该修/该并/该弃"的技能。 这是让全栈超级智能体"能治理自身生态"的元能力,一线大模型不具备。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 280 tokens Open the sourceclawhub.ai analyzed 3 d ago

技能生态健康度审计:扫描技能生态(默认 ~/.workbuddy/skills),对每枚技能做体检—— frontmatter 合法性、脚本可编译性、陈旧度、近重复(shingle Jaccard)、孤儿 meta (meta-X 但教师 X…

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

AnalyzerSoftware developmentWriting and documentsAI and agentstype 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: 5. 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")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "visibility"

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 (python) that frontmatter does not declare
  • 100Steps. 15 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 280 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 193: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 15 items

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

External checks

ClawHub: suspicious
The main auditor is a coherent local skill-health scanner, but the package also includes an under-scoped learning module that can persist notes and preferences into arbitrary skill directories.
LLM: suspicious (high) · 14 Aug 2026