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BC self-evolving-memory

为 AI Agent 部署分层自进化记忆系统的 mentor skill。核心状态保存在本地文件；语义检索和定时触发是可选 Host 能力。覆盖三层存储（即时 ≤20KB / 近中期 / 长期检索）、事务化巩固与回滚、多因子晋升、事件因果图谱、主题索引、回忆规划器和证据账本。当用户要初始化或修复 Agent 记忆、轻量记录、跑巩固、评估晋升、回滚校验、建索引或规划检索时触发。

ClawHub Agent Skills author: 大痴小乙 v0.1.1 MIT-0 37 files body ≈ 1 236 tokens Open the sourceclawhub.ai↗ analyzed 10 h ago

为 AI Agent 部署分层自进化记忆系统的 mentor skill。核心状态保存在本地文件；语义检索和定时触发是可选 Host 能力。覆盖三层存储（即时 ≤20KB / 近中期 / 长期检索）、事务化巩固与回滚、多因子晋升、事件因果图谱、主题索引、回忆规划器和证据账本。当用户要初始化或修复 Agent…

As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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
  • 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 Secrets in code secret-high-entropy-token references/03-consolidation-guard.md:37
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "run_id": "2026…3d4",
    quoted

Files scanned: 35. 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 "archetype"

Process rating: all ten parameters 59/100

  • 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
  • 30Running it twice. 4 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 34 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1236 tokens
  • low No test case covers injection arriving through data

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)
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 189: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 34 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (18 of 18)

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

External checks

ClawHub: clean
This is a local-first AI memory management skill whose file access, persistence, secret-handling helpers, and optional retrieval/scheduling are disclosed and mostly bounded to the stated memory-system purpose.
LLM: benign (high) · VirusTotal: · 8 Aug 2026