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AC building-agents

【构建 AI Agent 实战指南】把 OpenAI《A Practical Guide to Building Agents》蒸馏成可执行的 Agent 设计方法论。覆盖：判断是否该建 Agent（vs 规则引擎）、Agent 三组件（Model/Tools/Instructions）、模型选型、工具设计、写 agent instructions、单/多智能体编排（Manager/Decentralized 模式）、Guardrails 与 human-in-the-loop。当用户说"帮我设计一个 agent""single agent 还是 multi-agent""怎么写 agent 指令""agent 工具怎么设计""agent 需要哪些 guardrails""该不该上 agent""agent 编排模式怎么选"时使用。

ClawHub Agent Skills author: jiaxinmmhh v1.0.0 MIT-0 3 files body ≈ 343 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

【构建 AI Agent 实战指南】把 OpenAI《A Practical Guide to Building Agents》蒸馏成可执行的 Agent 设计方法论。覆盖：判断是否该建 Agent（vs 规则引擎）、Agent 三组件（Model/Tools/Instructions）、模型选型、工具设计、写…

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

GeneratorPersonal productivitytype 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%
100
Quality 40%
74
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: 3. 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 "slug"
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "emoji"

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. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 343 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 371: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 22 items
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: clean
This is a documentation-only skill for designing AI agents and does not add code execution, credentials access, persistence, or hidden data flows.
LLM: benign (high) · VirusTotal: · 12 Aug 2026