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CD simaguang-perspective

用司马光的认知方式思考问题。不是模仿语气，是植入他的认知上下文—— 他的信念从哪来、他在哪些具体场景下做了什么决策、他承认自己错在哪。 覆盖历史、治理、商业、用人、识人、决策、博弈、心理、法律、投资、侦查、 易学、影视创作和高生产力输出等跨尺度问题。 完整已蒸馏书籍清单见 references/素材来源索引.md。 触发词：用司马光分析、司马光怎么看、全面分析、帮我决策、历史同构、 相反辩证、项目策划、产品方案、论文、小说剧本、蒸馏书籍。

ClawHub Agent Skills author: lisong2003-lgtm v0.3.7 MIT-0 80 files · 17 scripts body ≈ 1 990 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

用司马光的认知方式思考问题。不是模仿语气，是植入他的认知上下文—— 他的信念从哪来、他在哪些具体场景下做了什么决策、他承认自己错在哪。 覆盖历史、治理、商业、用人、识人、决策、博弈、心理、法律、投资、侦查、 易学、影视创作和高生产力输出等跨尺度问题。 完整已蒸馏书籍清单见 references/素材来源索引.md。…

As a process D 35/100 · Unfinished process — References files that are not bundled: scripts/review_learning_log.py, scripts/self-learn-next.sh

ReferencePeople and hiringPersonal productivitytype 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
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
58
Run on models
none yet
Process rating
D
35/100
Unfinished process
References files that are not bundled: scripts/review_learning_log.py, scripts/self-learn-next.sh
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: 80. 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: scripts/review_learning_log.py
  • warning missing-ref reference to a missing file: scripts/self-learn-next.sh
  • note frontmatter-key unknown frontmatter key "source_book"
  • note frontmatter-key unknown frontmatter key "related_skills"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/review_learning_log.py, scripts/self-learn-next.sh
  • 0Tools and files. 2 referenced file(s) missing: scripts/review_learning_log.py, scripts/self-learn-next.sh
  • 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. 88 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1990 tokens
  • 100Running it twice. No mutating operations
  • low 19 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
  • +4No input/output examples
  • -38 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 221: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 88 items
  • +4Reference files are cited in the instructions (34 of 54)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.

External checks

ClawHub: suspicious
This skill is not clearly malicious, but it gives a broad persona workflow persistent influence over future agent behavior and local learning files without tight user control.
LLM: suspicious (high) · 10 Oct 2026