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Gstack式AI工程团队开发模式。基于Y Combinator CEO Garry Tan开源的gstack方法论， 将单一AI助手转化为虚拟工程团队。适用场景：新项目启动、功能开发、代码重构、Bug修复、 技术方案评审、部署上线、团队Sprint规划、代码审查、E2E测试。 触发词：gstack、用gstack模式、工程团队模式、Sprint开发、角色化开发、虚拟团队、 office-hours、CEO review、QA测试、安全审计、canary发布、Sprint回顾、 用团队模式开发、多角色协作开发、专业分工开发

ClawHub Agent Skills author: wangbotaochn v1.0.1 MIT-0 15 files body ≈ 1 370 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerOperations and projectsInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
51/100
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: 15. 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 51/100

  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 77 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1370 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 264: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This skill is mostly a disclosed engineering workflow, but it can guide agents into broad code changes, real-environment testing, external model sharing, and deployment without enough explicit consent boundaries.
LLM: suspicious (high) · VirusTotal: · 29 May 2026