SKILLEMALL.ai
RU Sign in

AD pi-evox-loop

Give your coding agent an 'experience inheritance' runtime: recall validated fixes from an Evolver gene store at task start, register hits when a fix is actually used, and deposit newly-learned fixes after repairing a non-obvious failure. Optionally run controlled closed-loop experiments (R1 trap → distill → inject → R2) to measure inheritance gains. Use at the START of non-trivial tasks, after fixing a non-obvious failure, or when you want to measure agent self-evolution. Trigger words: 经验召回, 错题本, 经验继承, 自进化, evolver, 避坑, distill.

ClawHub Agent Skills author: Wing v0.10.0 MIT-0 29 files · 1 script body ≈ 1 827 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Give your coding agent an 'experience inheritance' runtime: recall validated fixes from an Evolver gene store at task start, register hits when a fix is…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsSoftware developmentInfrastructuretype 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%
96
Quality 40%
86
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token package-lock.json:1450
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…I5B+f3Oo…egQ==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:1514
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…4y7+TlJM…cMg==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:1530
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…2vj+04TZnew+uSJ9…bp3/iw3L…ZSw==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:1546
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha512-/PGqr…sIQ+7Mdr…o3F++To2W…uNQ==",
      detector

    Files scanned: 29. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "description_zh"
    • note frontmatter-key unknown frontmatter key "description_en"
    • note frontmatter-key unknown frontmatter key "homepage"

    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
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1827 tokens
    • 100Running it twice. No mutating operations
    • low 12 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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 536: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (1 of 5)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    This skill is mostly transparent about its experience-sharing purpose, but it can persistently alter future agent prompts from a global store with weak relevance controls.
    LLM: suspicious (high) · 11 Sept 2026