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AD meihua-yishu

梅花易数占卜工具，属于 fortune-telling-skills 运势测算套件。基于邵雍《梅花易数》体系， 支持时间起卦、数字起卦、文字起卦等多种起卦方式，通过体用五行生克关系判断吉凶。 起卦为确定性计算 + LLM 断卦解读，无外部 API 依赖。 触发词：梅花易数、起卦、占卜、卦象、体用、八卦占卜、帮我起一卦、算一卦。 不适用于：星座运势、塔罗占卜、八字命理 → 建议使用 fortune-hub。

ClawHub Agent Skills author: eamanc v1.0.0 MIT-0 4 files body ≈ 1 018 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsInfrastructuretype 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%
74
Run on models
none yet
Process rating
D
49/100
Unfinished process
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: 4. 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 "clawhub-slug"
  • note frontmatter-key unknown frontmatter key "clawhub-owner"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (meihua-yishu) differs from the folder (meihua-yishu-divination)
  • 100Tools and files. No external tools needed
  • 100Steps. 29 steps
  • 100Execution cost. Instruction body is 1018 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

  • +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
  • +2Single-language instructions
  • +3Description length 204: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: clean
This appears to be a low-risk divination skill with broad but purpose-aligned trigger phrases and no evidence of hidden execution, data theft, persistence, or destructive behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026