SKILLEMALL.ai
GitHub ↗ RU Sign in

CF content-distiller

从任何内容（电子书/链接/视频/文案）中蒸馏可复用方法论，并自动封装为体系化技能包。支持PDF/EPUB/TXT/MD电子书、小红书/抖音/B站/视频号等平台链接、本地视频、公众号文章、直接文案六种输入源。触发词：蒸馏、帮我蒸馏、提取方法论、把这本书变成技能、生成技能包、book to skills、从这门课程里提取、这个方法论归我了、帮我沉淀方法论。

Not recommendedcritical or high security findings
ClawHub Agent Skills author: 桂皮 v1.0.1 MIT-0 8 files body ≈ 2 819 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

从任何内容（电子书/链接/视频/文案）中蒸馏可复用方法论，并自动封装为体系化技能包。支持PDF/EPUB/TXT/MD电子书、小红书/抖音/B站/视频号等平台链接、本地视频、公众号文章、直接文案六种输入源。触发词：蒸馏、帮我蒸馏、提取方法论、把这本书变成技能、生成技能包、book to…

As a process F 35/100 · Will not run — References files that are not bundled: references/原始内容存档.md, references/理论原理.md, references/相关方法论对照.md

ProcedureAI and agentsPersonal productivitySoftware 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
74/100
safety, quality, tests
Safety 60%
82
Quality 40%
63
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/原始内容存档.md, references/理论原理.md, references/相关方法论对照.md
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. 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 · 1

  • high Concealment en-hide-from-user skill-card.md:22
    Instruction to hide actions from the user
    Risk: PDF and EPUB helper scripts may silently install Python packages. <br>

Files scanned: 8. 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: references/原始内容存档.md
  • warning missing-ref reference to a missing file: references/理论原理.md
  • warning missing-ref reference to a missing file: references/相关方法论对照.md
  • warning missing-ref reference to a missing file: references/局限性分析.md
  • warning missing-ref reference to a missing file: examples/实例-01-成功.md
  • warning missing-ref reference to a missing file: examples/实例-02-成功.md
  • warning missing-ref reference to a missing file: examples/实例-03-失败.md
  • warning missing-ref reference to a missing file: examples/实例-04-边界.md
  • warning missing-ref reference to a missing file: templates/模板-01.md
  • warning missing-ref reference to a missing file: templates/检查清单.md
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "xiaping_trigger"
  • note frontmatter-key unknown frontmatter key "xiaping_category"
  • note frontmatter-key unknown frontmatter key "xiaping_tags"
  • note frontmatter-key unknown frontmatter key "references"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/原始内容存档.md, references/理论原理.md, references/相关方法论对照.md
  • 0Tools and files. 10 referenced file(s) missing: references/原始内容存档.md, references/理论原理.md, references/相关方法论对照.md
  • 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
  • 100Steps. 77 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2819 tokens
  • 100Running it twice. No mutating operations
  • low 10 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
  • -214 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 177: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: suspicious
This skill mostly does what it says, but it can silently install Python packages and create persistent skill files in the user's WorkBuddy skills directory.
LLM: suspicious (high) · VirusTotal: · 22 Jun 2026