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CF knowledge-health-checker

知识库健康检查器 - 超越市面上水平的知识库质量诊断工具。检测空壳文件、断链、内容密度、知识网络完整性，生成可视化健康报告卡片，提供自动修复建议。支持Obsidian/Notion/Logseq/Markdown多格式。触发词：知识库检查、wiki健康、断链检测、空壳文件、知识库质量、lint wiki、knowledge health、check knowledge。

ClawHub Agent Skills author: xb19960921 v1.0.0 MIT-0 11 files · 3 scripts body ≈ 1 923 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

知识库健康检查器 - 超越市面上水平的知识库质量诊断工具。检测空壳文件、断链、内容密度、知识网络完整性，生成可视化健康报告卡片，提供自动修复建议。支持Obsidian/Notion/Logseq/Markdown多格式。触发词：知识库检查、wiki健康、断链检测、空壳文件、知识库质量、lint…

As a process F 31/100 · No process to follow — References files that are not bundled: url

AnalyzerObsidianNotionData and analyticstype 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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
F
31/100
No process to follow
References files that are not bundled: url
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 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")
  • warning missing-ref reference to a missing file: url
  • note edit-residue the text marks something as outdated (lines 136, 251): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: url
  • 0Tools and files. 1 referenced file(s) missing: url
  • 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
  • 40Consistency. Frontmatter name (knowledge-health-checker) differs from the folder (publish-knowledge-health-checker)
  • 100Steps. 85 steps
  • 100Execution cost. Instruction body is 1923 tokens
  • 100Running it twice. No mutating operations
  • low 12 top-level sections: this looks like several domains in one skill

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
  • -5TODO / placeholder text left in the skill
  • -219 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 186: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 85 items
  • +4Has examples (17 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This knowledge-base checker is mostly purpose-aligned, but its repair and scheduling features can delete or rewrite files and include unsafe generated scripts that require careful review.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026