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BD 智能体成长复盘

帮助用户复盘一段时间内的学习、实践、工作和人机协作变化，自动读取已有记忆、身份、Skill、对话记录、任务结果和知识资料，追问少量关键问题，生成'AI赋能XXX成长报告'。调用时机：用户说'智能体成长复盘''生成我的AI赋能成长报告''回顾这段时间我的成长''总结我和智能体最近的变化''复盘最近的人机协作''写一份阶段性学习思想报告''更新我的成长档案'。核心能力：分析用户变化、智能体变化、人机共同形成的能力，区分事实/感受/推断/归纳，未经确认不得写回Memory。

ClawHub Agent Skills author: jeasonhaitao v1.0.0 MIT-0 4 files body ≈ 2 337 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

帮助用户复盘一段时间内的学习、实践、工作和人机协作变化，自动读取已有记忆、身份、Skill、对话记录、任务结果和知识资料，追问少量关键问题，生成'AI赋能XXX成长报告'。调用时机：用户说'智能体成长复盘''生成我的AI赋能成长报告''回顾这段时间我的成长''总结我和智能体最近的变化''复盘最近的人机协作''写一份阶…

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
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
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 · 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 name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "knowledge_deps"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "trigger"

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 (智能体成长复盘) differs from the folder (agent-growth-review)
  • 100Tools and files. No external tools needed
  • 100Steps. 171 steps
  • 100Execution cost. Instruction body is 2337 tokens
  • 100Running it twice. No mutating operations
  • low 10 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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 236: enough signal without eating the budget
  • +4Structure: 44 headings
  • +3Step-by-step instructions: 171 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
The skill has a coherent growth-report purpose, but it can automatically gather broad personal history before the user has clearly approved the scope.
LLM: suspicious (high) · 5 Aug 2026