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AF self-improving-five-layer-memory

Self-Evolving Five-Layer Memory System for AI Agents. Based on WorkBuddy Five-Layer Memory v4.1, provides L1-L5 layered architecture, evolution mechanism, and automated maintenance. Triggers when the user needs to (1) build a memory system, (2) optimize existing memory, (3) analyze memory architecture, or (4) implement memory automation. Built from Dingdang's production memory system (2026-05-03).

ClawHub Agent Skills author: zhdingdang v1.0.0 MIT-0 5 files body ≈ 707 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 39/100 · Will not run — References files that are not bundled: scripts/mempalace_consolidation.py

AnalyzerAI and agentsInfrastructuretype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: scripts/mempalace_consolidation.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/mempalace_consolidation.py

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: scripts/mempalace_consolidation.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/mempalace_consolidation.py
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 54 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 707 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 400: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 54 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: suspicious
This memory-system skill is not malware, but it asks the agent to persist sensitive personal, behavioral, environment, and API-key-related information with too little user control.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026