BD axiomata-skill-evaluator-zh
Axiomata 技能评估系统 — OpenClaw 代理通用技能质量评估工具。 双评估系统：(1) Axioma 5维框架（结构、清晰度、完整性、一致性、功能性），(2) ISO 25010结构框架（13项自动检查，目标90%+）。 自包含：捆绑 evaluator.py 和 eval-skill.py。 适用于：发布前评估技能、根据评估结果改进技能、使用自动分析检查技能质量、进行技能审计、验证技能是否达到生产标准。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 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
- 50Steps. 2 steps
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1916 tokens
- 100Running it twice. No mutating operations
- low 13 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 214: enough signal without eating the budget
- +4Structure: 32 headings
- +4Has examples (16 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: clean
This skill is a self-contained skill-quality evaluator that reads local skill files and prints reports, with no evidence of network access, credential use, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026