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让 AI 代理根据对话内容自动选择最合适的模型。层2（多词非连续命中）+ 层1（单词兜底）并行决定模型池；层3（embedding）永远独立运行决定会话切换。B+ 连续漂移计数器（第3次警告，第4次强制 C-auto）。soft_keywords 泛用词降权机制。四池架构（高速/智能/人文/代理），五分支路由，全自动 Fallback 回路。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 19 files body ≈ 3 487 tokens Open the sourcegithub.com↗ analyzed 2 d ago

让 AI 代理根据对话内容自动选择最合适的模型。层2（多词非连续命中）+ 层1（单词兜底）并行决定模型池；层3（embedding）永远独立运行决定会话切换。B+ 连续漂移计数器（第3次警告，第4次强制 C-auto）。softkeywords 泛用词降权机制。四池架构（高速/智能/人文/代理），五分支路由，全自动…

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

IntegrationDiscordTelegramPeople and hiringtype 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
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 19. 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 "requires_approval"

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
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 56 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3487 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 16 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
  • -2localhost URLs: will not work for another user
  • -258 emoji in the instructions: noise for the model
  • -31 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 172: enough signal without eating the budget
  • +4Structure: 47 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (22 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.