SKILLEMALL.ai
GitHub ↗ RU Sign in

AC skill-forge

当需要为已有 Skill 自动生成 task_suite.yaml 测试任务集、从 skill_spec.yaml 生成完整 SKILL.md + task_suite、或一键走完「生成 → 评估 → 改进」全链路时使用。 读取 SKILL.md 中的 frontmatter/When to Use/example/anti-example/Output 五类来源， 自动推导 5-10 个 test task 并选择 ContainsJudge / LLMRubricJudge / PytestJudge。 不用于手动编写 SKILL.md（用 skill-creator）、单独评估已有 task_suite（用 improvement-evaluator）、 或驱动改进循环（用 improvement-orchestrator）。

ClawHub Agent Skills author: _silhouette v1.0.1 MIT-0 16 files body ≈ 2 516 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerAI 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 40Consistency. Frontmatter name (skill-forge) differs from the folder (auto-skill-forge)
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 29 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 2516 tokens
    • low The response is described with custom markup (8 tags): a typed call is more reliable

    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)
    • -33 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 371: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 29 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a local skill and test-suite generator with disclosed file writes and optional local-tool execution, and I found no hidden data access, persistence, or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026