north-starr
Agentic Development Workflow for AI Coding Tools
You don't improve productivity by going faster — you improve it by improving control.
Claude Code (marketplace) ·
VS Code Copilot (Homebrew)
agentlinter.com/r/w7Jo9X0qVOV8
Inspiration
north-starr is inspired by Idea Flow by Janelle Arty Starr — a framework for making invisible friction visible in software development.
The core insight: you don't improve productivity by going faster, you improve it by improving control. north-starr applies this to AI-assisted development — your AI partner learns your patterns, remembers your landmines, and works with control, not just speed.
The book · Talk · Podcast · "The most underrated book in software engineering management"
How it works
Every task goes through a complexity gate. Simple tasks flow fast. Complex tasks get automatic risk analysis and structured planning. The AI decides — you approve.
Task given
│
▼
Complexity Assessment
│
├─ Low ──────────► State files → Wait for approval → Code
│
├─ Medium/High ──► /invert (risk analysis)
│ │
│ ▼
│ layoutplan agent (separate thread)
│ │
│ ▼
│ Approval gate → Code
│
▼
RED (failing tests) → GREEN (implementation)
│
▼
Completion menu:
├─ /generate-commit
├─ /generate-pr
├─ /learn (capture patterns & landmines)
└─ Done
The complexity gate
Before any code change, the AI prints this assessment:
| # | Question | Answer |
|---|
| 0 | Is current behavior covered by tests? | Yes / No |
| 1 | How many files will this change? | 1-2 / 3+ |
| 2 | Am I creating new types or protocols? | No / Yes |
| 3 | Does this require cross-module integration? | No / Yes |
- All low → state the files and proceed
- Q0 = No → write tests for current behavior first
- Any medium/high → run
/invert automatically, then plan before coding
No configuration needed. This is built into every project north-starr bootstraps.
PRD decomposition
For full product specs, /decompose breaks the work into manageable pieces before the complexity gate kicks in:
PRD received
│
▼
/decompose → Scan & detect AI project
│
├─ Non-AI project ──► storymap agent → .plans/STORIES-<name>.md
│
├─ AI project ──────► chief-ai-po agent → .plans/STORIES-AI-<name>.md
│ (inverted stories, safety stories SA.1-SA.6,
│ human oversight checkpoints, graceful degradation)
│
▼
Optional: GitHub Issues script
│
▼
Pick a story → /invert → layoutplan → Implement
Install
Claude Code (marketplace):
/plugin marketplace add selcukyucel/north-starr
/plugin install north-starr
VS Code Copilot (Homebrew):
brew tap selcukyucel/north-starr https://github.com/selcukyucel/north-starr.git
brew install north-starr
cd your-project && north-starr init
Then run /bootstrap — north-starr explores your codebase and generates everything your AI needs.
What /bootstrap generates
All output is tool-native — the exact files each tool already reads:
| Artifact | Claude Code | VS Code Copilot |
|---|
| Project context | CLAUDE.md | AGENTS.md |
| Universal context | AGENTS.md | AGENTS.md |
| Pattern rules | .claude/rules/*.md | .github/instructions/*.instructions.md |
| Landmine rules | .claude/rules/*.md | .github/instructions/*.instructions.md |
| Agents | .claude/agents/layoutplan.md, storymap.md, chief-ai-po.md | .github/agents/layoutplan.agent.md, storymap.agent.md, chief-ai-po.agent.md |
| Module context | CLAUDE.md per module | — |
Pattern rules document how things are done in your codebase. Landmine rules document what to watch out for. Both are scoped by file path — they fire only when the AI touches matching files.
The learning loop
When your AI gets corrected, discovers a convention, or breaks something — /learn captures it as a pattern or landmine rule. These rules feed directly into how the AI operates next session.
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