SKILLEMALL.ai
GitHub ↗ RU Sign in

BC opc-triz-innovation

基于TRIZ（发明问题解决理论）的技术创新方法论Skill，面向OPC创业者和制造业从业者，提供40个发明原理、39×39矛盾矩阵、物场分析等核心工具，解决技术矛盾和物理矛盾。

ClawHub Agent Skills author: golngod v1.0.0 MIT-0 9 files body ≈ 7 018 tokens Open the sourceclawhub.ai↗ analyzed 28 h ago

基于TRIZ（发明问题解决理论）的技术创新方法论Skill，面向OPC创业者和制造业从业者，提供40个发明原理、39×39矛盾矩阵、物场分析等核心工具，解决技术矛盾和物理矛盾。

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
52/100
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 9. 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 body-long SKILL.md body ≈ 7018 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 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
  • 70Execution cost. Instruction body is 7018 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 104 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 14 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)
  • +3Description length 88: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -216 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +4Structure: 64 headings
  • +3Step-by-step instructions: 104 items
  • +4Has examples (29 code blocks)
  • +4Reference files are cited in the instructions (4 of 6)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
This skill is a TRIZ innovation guidance package with disclosed web and reference-file research workflows and no hidden executable behavior found.
LLM: benign (high) · VirusTotal: · 28 May 2026