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AI对话记忆增强系统（v6）。解决AI跨对话失忆和同对话上下文丢失问题。三个核心命令：!记忆热身（启动检索历史）→ !记忆压缩（保存状态快照）→ !找记忆（语义搜索历史）。v6新增 !forge 命令，支持一键生成行业定制记忆Skill。技术栈：Python + chromadb向量数据库 + sentence-transformers语义搜索。

ClawHub Agent Skills author: mikeliang2000 v6.0.3 MIT-0 10 files · 1 script body ≈ 3 034 tokens Open the sourceclawhub.ai↗ analyzed 10 h ago

AI对话记忆增强系统（v6）。解决AI跨对话失忆和同对话上下文丢失问题。三个核心命令：!记忆热身（启动检索历史）→ !记忆压缩（保存状态快照）→ !找记忆（语义搜索历史）。v6新增 !forge 命令，支持一键生成行业定制记忆Skill。技术栈：Python + chromadb向量数据库 +…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorData and analyticsPeople and hiringAI 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
43/100
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: 10. 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 "display_name"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 43/100

  • 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
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 56 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3034 tokens
  • low 14 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

  • +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
  • -221 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 174: enough signal without eating the budget
  • +4Structure: 53 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (25 code blocks)
  • +3All 5 scripts are documented

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

External checks

ClawHub: suspicious
This memory skill is mostly coherent, but it asks for broad access to past conversations and local memory files, can automatically surface that history, and includes analytics tracking without enough user control.
LLM: suspicious (high) · VirusTotal: · 1 Jun 2026