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AD mu-self-evolve

AI Agent 持续进化系统：每日经验沉淀+每周错误反思，正向提炼与负向纠偏合一。v3.1 新增断链防护：会话内即时沉淀（触发信号当场写入日记）+ 日记缺失兑底补写。v3.0：VFM 确定性验算、评分+衰减淘汰、WHERE×WHY 病理归档、主动 Skill 合成。触发词：记录错误、进化系统、自我反思、踩坑记录、self-evolve、self-improve。

ClawHub Agent Skills author: 木先生iPPT v3.1.0 MIT-0 17 files body ≈ 2 552 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

AI Agent 持续进化系统：每日经验沉淀+每周错误反思，正向提炼与负向纠偏合一。v3.1 新增断链防护：会话内即时沉淀（触发信号当场写入日记）+ 日记缺失兑底补写。v3.0：VFM 确定性验算、评分+衰减淘汰、WHERE×WHY 病理归档、主动 Skill…

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

ProcedureAI 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
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-cron-mention references/claude-code-compat.md:30
    Mentions editing / listing crontab
    crontab -e

Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "visibility"

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 (python) that frontmatter does not declare
  • 100Steps. 79 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2552 tokens
  • 100Running it twice. No mutating operations
  • low 10 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 183: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 79 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 4 scripts are documented
  • +1License stated

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

External checks

ClawHub: suspicious
This skill is transparent about being a self-evolving memory system, but it gives the agent broad durable authority to record conversations and rewrite future behavior files.
LLM: suspicious (high) · 9 Sept 2026