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🚀 超级开发规划师 —— 对话式需求收集 + Agent 团队并行开发 + MCP 工具全程调用。 触发时机：用户说"帮我开发"、"我想做一个项目/网站/APP/系统/工具/脚本/Bot"、"plan模式"、 "对话式开发"、"生成开发文档"、"我有个想法想实现"，或任何描述了软件/工具/系统需求的请求。 即使需求模糊如"我想做个东西"也要触发。核心能力：(1) 引导小白用户通过选项对话完善需求， (2) 生成企业级超详细开发文档，(3) 编排 Agent 团队+MCP 工具并行开发，(4) 全程文档驱动、节点测试、自动提交。

ClawHub Agent Skills author: wangxiaofei860208-source v1.0.0 MIT-0 6 files body ≈ 2 126 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — 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
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv references/dev-doc-template.md:275
    Reads a .env file
    cp .env.example .env

Files scanned: 6. 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. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2126 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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 268: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (19 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

External checks

ClawHub: suspicious
This skill is a legitimate development-planning assistant, but it can move into broad automatic file, GitHub, database, deployment, and notification actions with weak scoping.
LLM: suspicious (high) · VirusTotal: · 29 May 2026