SKILLEMALL.ai

AB multi-skill-eval

集成化多方法技能评估系统。整合静态分析(skill-assessment)、Rubric质量打分(skill-evaluator)和自主基准测试(skill-eval)。用于全面评估、对比、审计或改进OpenClaw技能。覆盖文档完整性、代码质量、25项Rubric打分、多模型基准测试。 触发词(中文): 评估技能、技能评分、技能审计、对比技能、批量评估、技能质量检查、静态分析、基准测试、触发词检测、幽灵工具检测 触发词(English): evaluate skill, compare skills, audit skill, benchmark skill, static analysis, skill quality, skill assessment Use when you need to evaluate, audit, benchmark, or improve OpenClaw skills.

ClawHub Agent Skills author: wangzairong v1.0.2 MIT-0 16 files body ≈ 2 892 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 10 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 85 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2892 tokens

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 412: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 85 items
    • +3Output format is stated explicitly
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 5 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    This appears to be a real skill-evaluation toolkit, but it needs Review because some broad benchmark/rewrite workflows and generated HTML outputs are not safely bounded.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026