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BF memory-tree-universal

Memory Tree 记忆树架构 — 通用版，适用于任何 AI Agent 的长期记忆系统。包含三棵树设计、热冷路径管道、14 条打分规则、数据库 Schema、检索 API 和坑点总结。当用户需要记忆系统、长期记忆、AI Agent 记忆架构时加载此技能。

ClawHub Agent Skills author: Yoorle Hnggo v1.0.0 MIT-0 5 files body ≈ 1 894 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 35/100 · Will not run — References files that are not bundled: scripts/quick_test.py, references/debug-log.md

IntegrationAI 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
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/quick_test.py, references/debug-log.md
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/quick_test.py
  • warning missing-ref reference to a missing file: references/debug-log.md

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/quick_test.py, references/debug-log.md
  • 0Tools and files. 2 referenced file(s) missing: scripts/quick_test.py, references/debug-log.md
  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 100Steps. 33 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1894 tokens
  • 100Progress reporting. Reports progress
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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 130: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
This is a legitimate long-term memory design, but it stores and reuses conversation content too broadly, including credential-like data, without enough privacy controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026