SKILLEMALL.ai

AC improvement-evaluator

当需要验证 Skill 改进是否真正提升了 AI 执行效果时使用。通过预定义任务集(YAML)运行 AI 任务,判定 pass/fail,输出 execution_pass_rate。不用于文档结构评分(用 improvement-learner)或候选打分(用 improvement-discriminator)。

ClawHub Agent Skills author: _silhouette v1.0.0 MIT-0 17 files body ≈ 1 099 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: improvement-evaluator (ClawHub)

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"

    Process rating: all ten parameters 50/100

    • 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
    • 40Consistency. Frontmatter name (improvement-evaluator) differs from the folder (auto-improvement-evaluator)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 14 steps
    • 100Execution cost. Instruction body is 1099 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)
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 159: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 14 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This is a disclosed skill-evaluation tool, but real runs can send skill content, prompts, rubrics, and outputs through the local Claude CLI.
    LLM: benign (high) · VirusTotal: · 29 May 2026