SKILLEMALL.ai
GitHub ↗ RU Sign in

BF meta-skill-system

元技能系统，提供领域评估、工作流重构、领域负载物生成和通用任务执行的完整能力。核心能力：①领域消除评估（五步法：边界识别→存在理由分析→消除可行性评估→独立存在必要性判断→决策输出）②工作流重构（三步法：拆解→消除→重整，将复杂工作流重构为AI辅助一人简易完成）③领域负载物生成（从零创建完整的领域负载物技能，三层结构模板+28项接口校验，可选附加12维完整角色）④通用任务执行（三轴正交：执行轴6大元操作+管线编排、内容轴清单法+样本法、创新轴4种模式+10种元框架）。10域82种任务。触发词：元技能、领域评估、工作流重构、技能生成、任务执行、meta-skill、领域消除、三轴执行、创新框架、meta-skill-system。

ClawHub Agent Skills author: 波动几何 v1.0.13 MIT-0 15 files body ≈ 1 803 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

元技能系统，提供领域评估、工作流重构、领域负载物生成和通用任务执行的完整能力。核心能力：①领域消除评估（五步法：边界识别→存在理由分析→消除可行性评估→独立存在必要性判断→决策输出）②工作流重构（三步法：拆解→消除→重整，将复杂工作流重构为AI辅助一人简易完成）③领域负载物生成（从零创建完整的领域负载物技能，三层结构…

As a process F 35/100 · Will not run — References files that are not bundled: references/meta-skill-system-prompt.md

ProcedureAI 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/meta-skill-system-prompt.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: 15. 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/meta-skill-system-prompt.md

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/meta-skill-system-prompt.md
  • 0Tools and files. 1 referenced file(s) missing: references/meta-skill-system-prompt.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
  • 100Steps. 61 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1803 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 318: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 61 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

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

External checks

ClawHub: suspicious
The skill is a mostly transparent methodology and skill-generation framework, but it includes rules that can reduce user control and propagate its own identity into generated skills.
LLM: suspicious (high) · 12 Sept 2026