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AF learning-review

学用结合的回顾机制。包含五个回顾环节：学后复盘（每次学完）、周内化（每周一次）、应用检查（每两周一次）、压缩归档（每月一次）、知识落地（每周一次）。将学习成果转化为 Agent 实际工作能力，防止"学完就忘"。 触发词："回顾", "复盘", "内化", "学习回顾", "learning review", "retrospective", "知识落地", "压缩归档"。 Not for: 首次学习新知识（用 daily-learning）、非学习类的定期回顾。

ClawHub Agent Skills author: mayf3 v1.0.1 MIT-0 4 files body ≈ 704 tokens Open the sourceclawhub.ai↗ analyzed 26 h ago

学用结合的回顾机制。包含五个回顾环节：学后复盘（每次学完）、周内化（每周一次）、应用检查（每两周一次）、压缩归档（每月一次）、知识落地（每周一次）。将学习成果转化为 Agent 实际工作能力，防止"学完就忘"。 触发词："回顾", "复盘", "内化", "学习回顾", "learning review"…

As a process F 39/100 · Will not run — References files that are not bundled: references/<topic>.md

AnalyzerAI and agentsOperations and projectstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: references/<topic>.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: 4. 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: references/<topic>.md

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: references/<topic>.md
  • 0Tools and files. 1 referenced file(s) missing: references/<topic>.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
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 704 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

  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 233: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

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

External checks

ClawHub: suspicious
The skill is purpose-aligned, but it can silently rewrite important agent instruction, memory, and skill files on a schedule without clear approval or rollback controls.
LLM: suspicious (high) · 9 Jul 2026