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AC session-recovery

AI会话管理与优化系统。解决会话上下文限制问题，通过三级文档系统、自动化脚本和上下文压缩实现快速恢复。包含：STATUS.md快速索引、memory详细记录、自动更新脚本、上下文压缩策略、信息识别机制。适用于长期AI项目、多任务并行、需要高效会话恢复的场景。触发词：会话恢复、session recovery、上下文压缩、快速恢复、会话管理。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 12 files · 4 scripts body ≈ 2 374 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI会话管理与优化系统。解决会话上下文限制问题，通过三级文档系统、自动化脚本和上下文压缩实现快速恢复。包含：STATUS.md快速索引、memory详细记录、自动更新脚本、上下文压缩策略、信息识别机制。适用于长期AI项目、多任务并行、需要高效会话恢复的场景。触发词：会话恢复、session…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmenttype 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%
100
Quality 40%
78
Run on models
none yet
Process rating
C
53/100
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 · 0

✓ No critical or high findings

Files scanned: 12. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 106 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2374 tokens
  • 100Running it twice. No mutating operations
  • low 16 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
  • -280 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 171: enough signal without eating the budget
  • +4Structure: 76 headings
  • +3Step-by-step instructions: 106 items
  • +4Has examples (32 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 5 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.