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CF huashu-nuwa

女娲造人：输入人名/主题/甚至只是模糊需求，自动深度调研→思维框架提炼→生成可运行的人物Skill。 两种入口：(1)明确人名→直接蒸馏 (2)模糊需求→诊断推荐→再蒸馏。 触发词：「造skill」「蒸馏XX」「女娲」「造人」「XX的思维方式」「做个XX视角」「更新XX的skill」。 模糊需求也触发：「我想提升决策质量」「有没有一种思维方式能帮我...」「我需要一个思维顾问」。 English triggers: "distill [person]", "nuwa", "create a [person] perspective skill", "how does [person] think", "I need a thinking advisor".

alchaincyf/nuwa-skill Agent Skills author: alchaincyf MIT 23 files · 5 scripts body ≈ 4 593 tokens Open the sourcegithub.com↗ analyzed 5 d ago

女娲造人：输入人名/主题/甚至只是模糊需求，自动深度调研→思维框架提炼→生成可运行的人物Skill。 两种入口：(1)明确人名→直接蒸馏 (2)模糊需求→诊断推荐→再蒸馏。 触发词：「造skill」「蒸馏XX」「女娲」「造人」「XX的思维方式」「做个XX视角」「更新XX的skill」。…

As a process F 29/100 · No process to follow — References files that are not bundled: references/research/, references/research/0X-xxx.md, references/research/01-06.md

GeneratorGitHubAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
F
29/100
No process to follow
References files that are not bundled: references/research/, references/research/0X-xxx.md, references/research/01-06.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

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: 22. 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/research/
  • warning missing-ref reference to a missing file: references/research/0X-xxx.md
  • warning missing-ref reference to a missing file: references/research/01-06.md

Process rating: all ten parameters 29/100

Will not run. References files that are not bundled: references/research/, references/research/0X-xxx.md, references/research/01-06.md
  • 0Tools and files. 3 referenced file(s) missing: references/research/, references/research/0X-xxx.md, references/research/01-06.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
  • 40Consistency. Frontmatter name (huashu-nuwa) differs from the folder (nuwa-skill)
  • 70Execution cost. Instruction body is 4593 tokens
  • 100Steps. 108 steps
  • 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
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 332: enough signal without eating the budget
  • +4Structure: 53 headings
  • +3Step-by-step instructions: 108 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +3All 4 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.