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当智能体涉及"memory"与"Context"的操作时触发skill；智能体底层记忆基础设施，完整实现Context Engineering五大核心能力：选择（噪声过滤+相关性筛选）、压缩（因果结构提取+工具结果压缩）、检索（结果重排序+多样性保证）、状态（任务进度追踪+目标对齐）、记忆（冲突检测+跨会话关联）；认知模型层支持认知模型构建、因果链提取、知识缺口识别、检索时机决策、质量评估、状态一致性校验、状态推理、跨会话关联、遗忘机制；作为元技能强制常驻运行

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 57 files body ≈ 3 977 tokens Open the sourcegithub.com↗ analyzed 2 d ago

当智能体涉及"memory"与"Context"的操作时触发skill；智能体底层记忆基础设施，完整实现Context…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
D
49/100
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-password-literal references/encryption_guide.md:157
    Hard-coded password / key literal (may be an example)
    password = "user…123"

Files scanned: 57. 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 "always"
  • note frontmatter-key unknown frontmatter key "dependency"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (agent-memory) differs from the folder (memory-and-context-engineering)
  • 100Tools and files. No external tools needed
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 3977 tokens
  • 100Running it twice. No mutating operations

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -32 of 41 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 232: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (26 code blocks)
  • +4Reference files are cited in the instructions (13 of 13)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.