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面向 AI Agent 的自我进化引擎，直击"重复犯错、从沉默误学、记忆压缩丢失、进化无法衡量"四大痛点。通过自反思、纠错学习、模式晋升机制，让 Agent 从每次交互中积累可复用经验，而非每次会话从零开始。 核心能力包括自反思日志（任务后自动评估"是否达预期/如何改进/是否成模式"）、纠错学习信号（识别用户纠正并归类）、反污染防线（3 次确认才晋升、永不从沉默推断）、分层记忆压缩（合并而非删除，保留确认偏好）、进化指标度量（纠正频率/晋升率/复用率可量化）、命名空间隔离（项目/领域/全局三级）、心跳维护机制、冲突解决规则。 适用场景：AI 编程助手避免重复犯错、长期项目经验沉淀、多项目模式复用、希望 Agent 越用越好的用户、需要可衡量进化的团队。 差异化：相比简单记忆存储，本系统提供反污染防线（3 次确认+不从沉默推断）避免误学、压缩合并而非删除保留确认偏好、进化指标让"是否变好"可量化、命名空间隔离避免跨项目污染、心跳自动维护。所有记忆分层加载降低 token 消耗。 触发关键词：自我改进、反思、纠错、学习、进化、经验积累、避免重复犯错、self-improving、reflection

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 1 890 tokens Open the sourceclawhub.ai↗ analyzed 15 h ago

面向 AI Agent 的自我进化引擎，直击"重复犯错、从沉默误学、记忆压缩丢失、进化无法衡量"四大痛点。通过自反思、纠错学习、模式晋升机制，让 Agent 从每次交互中积累可复用经验，而非每次会话从零开始。…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
49/100
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 · 0

✓ No critical or high findings

Files scanned: 2. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (evolution-engine) differs from the folder (evolution-engine-v2)
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 26 steps
  • 100Execution cost. Instruction body is 1890 tokens
  • 100Running it twice. No mutating operations
  • low 16 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

  • +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
  • +5Description quotes 2 example trigger phrases
  • +3Description length 509: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (12 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill is mostly coherent, but it asks for command execution while saying it does not need it and it creates persistent local memory files.
LLM: suspicious (high) · 18 Jul 2026