Gstack Pro

Transform your AI assistant into a structured virtual software engineering team with 10 specialist roles — inspired by Garry Tan's GStack (YC CEO, 16K GitHub Stars) and adapted for OpenClaw's subagent architecture. Covers the full development lifecycle: product thinking → architecture → design → code review → QA → ship → retro. Generator-Evaluator pattern included. Health Score (0-100) for every sprint. Automated browser QA. Activate when: starting a new feature, preparing to ship, doing code review, running QA, or needing a product rethink. Works with: OpenClaw subagents (coder/tester/architect/writer/operator/designer/progress/requirer).

GStack Pro — 10-Role AI Engineering Team

Built on the philosophy of Garry Tan's GStack (YC CEO) · 16K GitHub Stars · MIT License
Adapted for OpenClaw subagent + session architecture

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What It Does

GStack Pro gives your AI 10 specialist roles — each with a clear mandate, a structured output format, and a measurable quality bar.

Instead of one generic AI doing everything badly, you get a team:

#	Role	Icon	Subagent	Best For
1	CEO / Product Thinker	🏛️	requirer	Rethink the problem before building
2	Architect / Tech Lead	🏗️	architect	Lock in data flow, failure modes, tests
3	Designer Review	🎨	designer	80-item design audit, AI slop detection
4	Paranoid Code Review	🔍	tester	N+1, race conditions, trust boundaries
5	Browser QA	🌐	browser tool	AI with eyes — login, click, screenshot, verify
6	Automated QA + Fix	🧪	tester + coder	Find → fix → re-verify with Health Score
7	QA Reporter	📊	tester	Report-only, clean handoff to team
8	One-Command Ship	🚀	operator	sync → test → push → PR
9	Engineering Retro	🔄	progress	Commit analysis, praise, growth areas
10	Release Docs	📝	writer	Sync docs to match what shipped

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The Development Cycle

text

User Request
     ↓
① CEO审视 (/plan-ceo)
   → Is this worth building? What's the 10-star product?
     ↓
② Architecture Lock (/plan-eng)
   → Data flow, state machine, failure modes, test matrix
     ↓
③ Design Review (/plan-design)
   → 80-item audit, design quality grades, AI slop detection
     ↓
④ Paranoid Code Review (/review)
   → N+1, race conditions, trust boundary violations
     ↓
⑤ Automated Browser QA (/qa)
   → AI drives browser, finds bugs, fixes them, re-verifies
   → Health Score 0-100 determines ship-readiness
     ↓
⑥ One-Command Ship (/ship)
   → sync main → run tests → push → open PR
     ↓
⑦ Engineering Retro (/retro)
   → Commit analysis, team performance, improvement plan
     ↓
⑧ Release Docs (/document)
   → Update README/ARCHITECTURE to match what shipped

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How to Activate a Role

Method 1: Direct Command (e.g., in conversation)

text

/plan-ceo: 为AICFO设计一个新功能：员工工资条自动生成PDF

/review: 审查deepfmt Sprint 3的代码改动

/qa: 对 https://xxx.space.minimaxi.com 运行标准QA测试

Method 2: Subagent (for background/parallel work)

javascript

sessions_spawn({
  agentId: "tester",  // QA + Review
  task: "Read skills/gstack-pro/roles/review.md then review the code at /workspace/projects/aicfo/aicfo-mvp/src/api/"
})

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Health Score System

After every /qa session, output a structured score:

json

{
  "healthScore": 85,
  "status": "🟡 Good",
  "breakdown": {
    "functional": { "passed": 8, "total": 10, "score": 24 },
    "edgeCases": { "covered": 4, "total": 5, "score": 20 },
    "consoleErrors": { "passed": true, "score": 25 },
    "designRegressions": { "passed": true, "score": 16 }
  },
  "shipRecommendation": "🟡 Fix 2 minor issues before ship"
}

Score	Status	Action
90-100	🟢 Excellent	Ready to ship immediately
70-89	🟡 Good	2-3 minor issues, fix before ship
50-69	🟠 Needs Work	Significant bugs, fix before next sprint
<50	🔴 Do Not Ship	Core functionality broken, redo required

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Quality Bars

Code Must Pass

• ✅ N+1 queries eliminated
• ✅ All external calls have timeouts
• ✅ Retries with exponential backoff
• ✅ Database transactions properly bounded
• ✅ Input validation on all untrusted data
• ✅ No trust boundary violations
• ✅ Structured logging (JSON, with trace IDs)

Design Must Pass

• ✅ Consistent visual hierarchy
• ✅ No AI slop patterns (copy-paste generic cards, overuse of gradients)
• ✅ Responsive at 375px / 768px / 1440px
• ✅ Accessible (color contrast, focus states)
• ✅ Meaningful empty states

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Anti-Patterns Detected

Pattern	Why It Fails	Detection
"Looks good!"	AI self-evaluation bias	Evaluator never reads generator code
Circular dependency	Unmaintainable architecture	Dependency graph analysis
AI slop	Generic, low-quality design	80-item designer audit
Magic numbers	Hard to maintain	no-magic-numbers lint rule
Forgotten edge cases	Silent production failures	Mandatory test matrix
No rollback plan	Can't safely deploy	/ship requires rollback plan

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OpenClaw Subagent Mapping

Role	Subagent ID	Type
CEO Product	requirer	demand analysis
Architect	architect	tech design
Designer	designer	UI/UX review
Code Review	tester	quality assurance
Browser QA	browser tool	automated testing
QA + Fix	tester + coder	test + implement
QA Reporter	tester	reporting
Ship	operator	release
Retro	progress	analysis
Docs	writer	documentation

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Key Insight: Generator vs Evaluator

GStack Pro separates creation from judgment.

text

Generator Agent  ──→  builds code  ──→  Evaluator Agent
  (creates)         (artifact)           (judges from SPEC + URL only)
                                              ↑
                                       Never reads generator's code

This eliminates cognitive commitment bias — the AI can't judge what it already committed to building.

Inspired by: Anthropic Engineering, "Harness Design for Long-Running Application Development" (2026)

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Files

File	Purpose
SKILL.md	This file — overview and usage
references/plan-ceo.md	CEO product thinking SOP
references/plan-eng.md	Architecture review SOP
references/review.md	Paranoid code review SOP
references/qa.md	Automated QA SOP + Health Score
references/ship.md	One-command ship SOP
references/retro.md	Engineering retro SOP

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Inspired by Garry Tan's GStack (https://gstacks.org) · MIT License For OpenClaw · Compatible with Claude Code GStack workflows