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AC skill-evolver

Skill evolution system. Analyzes agent execution traces to generate Evolution Units (EUs — exploit/explore subtypes under the adaptive type), deploys them to SKILL.md via evidence-based scoring, making agents learn from their own experience. Trigger: 'run skill-evolver' / 'run skill evolution' / 'evolver status' / 'skill patch'

ClawHub Agent Skills author: Shawn v2.4.13 MIT-0 26 files · 2 scripts body ≈ 2 818 tokens Open the sourceclawhub.ai↗ analyzed 18 h ago

Skill evolution system.

As a process C 50/100 · Has gaps — weak spots: result and completion, 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "display_name"
    • note frontmatter-key unknown frontmatter key "slogan"
    • note frontmatter-key unknown frontmatter key "keywords"

    Process rating: all ten parameters 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (skill-evolver) differs from the folder (adaptive-skill-evolver)
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 39 steps
    • 100Execution cost. Instruction body is 2818 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (3 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)
    • +3Output format is not stated: the model decides each time
    • -32 of 12 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 329: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 39 items
    • +4Has examples (14 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: suspicious
    The skill is broadly coherent, but it needs Review because it can automatically read agent session logs, use LLM credentials, send trace/unit content to model endpoints, persist derived history, and edit other skills' SKILL.md files.
    LLM: suspicious (high) · 22 Jun 2026