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AC structured-vector-memory

Structured Vector Memory (SVM) / 三层高效记忆存储法。整合 LanceDB Pro 向量引擎 + 三层结构化管理，涵盖：每日记忆整理（Micro Sync + Daily Wrapup）、记忆蒸馏压缩（Weekly Compound）、去重检测、scope 隔离、archive 机制。触发词：记忆系统、memory system、安装记忆、记忆管理、micro sync、daily wrapup、weekly compound、lancedb、整理记忆、记忆维护、记忆蒸馏、压缩上下文、精简记忆、context瘦身、consolidate memory。

ClawHub Agent Skills author: kylin19860916 v3.1.0 MIT-0 6 files · 3 scripts body ≈ 1 285 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype 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
C
51/100
Has gaps
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 SKILL.md:93
    Mentions editing / listing crontab
    crontab -e

Files scanned: 6. 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")

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1285 tokens
  • low 12 top-level sections: this looks like several domains in one skill
  • low The skill ranks results itself: that belongs to the system behind the tool, not the model

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 295: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 3 scripts are documented

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

External checks

ClawHub: suspicious
This is a disclosed memory-automation skill, but it can repeatedly review conversations and change persistent agent memory without clear per-action approval.
LLM: suspicious (high) · VirusTotal: · 29 May 2026