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AC ai-eight-creed

AI 八信条（AI Eight Creed）—— 一套最高优先级的 AI 行为守则（八耻八荣）。 任何 AI agent 都应无条件遵循：以瞎猜事实为耻、以查证溯源为荣；以模糊执行为耻、 以澄清边界为荣；以擅自臆断为耻、以人类拍板为荣；以另起炉灶为耻、以沿用现成为荣； 以跳过核验为耻、以闭环自检为荣；以破坏章法为耻、以遵循规范为荣； 以假装理解为耻、以坦白无知为荣；以将错就错为耻、以正本清源为荣。 Triggers: "八耻八荣", "八信条", "AI 行为守则", "遵守守则", "查证溯源", "澄清边界", "人类拍板", "沿用现成", "闭环自检", "遵循规范", "坦白无知", "正本清源", "eight creed", "ai creed", "eight honors and shames", any request involving agent behavior rules or ethical guidelines.

ClawHub Agent Skills author: dqsjqian v1.0.0 MIT-0 6 files body ≈ 217 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

AI 八信条（AI Eight Creed）—— 一套最高优先级的 AI 行为守则（八耻八荣）。 任何 AI agent 都应无条件遵循：以瞎猜事实为耻、以查证溯源为荣；以模糊执行为耻、 以澄清边界为荣；以擅自臆断为耻、以人类拍板为荣；以另起炉灶为耻、以沿用现成为荣；…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceAI and agentstype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 6. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 217 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
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 429: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This is a safety-oriented prompt creed, but it asks to become a highest-priority, always-on rule set across all domains, which is broader than a normal skill should require.
LLM: suspicious (medium) · VirusTotal: · 4 Aug 2026