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cyanluna-git/cyanluna.skills

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cyanluna.skills AI-powered kanban pipeline for Claude Code and Codex — seven autonomous agents, one board.

개요

cyanluna.skills AI-powered kanban pipeline for Claude Code and Codex — seven autonomous agents, one board.

README

cyanluna.skills

AI-powered kanban pipeline for Claude Code and Codex — seven autonomous agents, one board.


Click to watch demo (25s)

Quick Start

1. Clone and install skills

git clone https://github.com/cyanluna/cyanluna.skills.git
REPO="$PWD/cyanluna.skills"

# Claude install
mkdir -p ~/.claude/skills
cp -R "$REPO"/kanban         ~/.claude/skills/
cp -R "$REPO"/kanban-run     ~/.claude/skills/
cp -R "$REPO"/kanban-refine  ~/.claude/skills/
cp -R "$REPO"/kanban-init    ~/.claude/skills/
cp -R "$REPO"/kanban-explore ~/.claude/skills/
cp -R "$REPO"/kanban-board   ~/.claude/kanban-board

# Codex install (recommended: symlink to shared source)
mkdir -p ~/.codex/skills
for s in kanban kanban-run kanban-refine kanban-init kanban-explore; do
  ln -sfn "$REPO/$s" "$HOME/.codex/skills/$s"
done
ln -sfn "$HOME/.claude/kanban-board" "$HOME/.codex/kanban-board"

2. Set up environment

cd ~/.codex/kanban-board 2>/dev/null || cd ~/.claude/kanban-board
cp .env.example .env   # fill in DATABASE_URL and optionally Cloudflare R2 vars
pnpm install

Create a free Neon database at neon.tech and paste the connection string as DATABASE_URL. For image attachments, set up a Cloudflare R2 bucket and fill in the CLOUDFLARE_R2_* variables. Image uploads are disabled if these are omitted.

3. Initialize a project (inside any project directory)

/kanban-init

This creates .claude/kanban.json, .codex/kanban.json, and a kanban-board/start.sh launcher. Project data is stored in Neon under a project column — no local DB files needed.

4. Start the board and add tasks

./kanban-board/start.sh        # opens http://localhost:5173
/kanban add Implement user authentication
/kanban-run 1                  # runs the full AI pipeline

The Pipeline

Every task flows through a 7-column board. AI agents handle each stage automatically.

Req → Plan → Review Plan → Impl → Review Impl → Test → Done
Column Agent Model (Claude / Codex) What happens
Requirements User You describe what needs to be done
Plan Planner opus / gpt-5.4 Reads requirements, writes plan + decision log + done-when checklist
Review Plan Critic sonnet / gpt-5.4 Scores plan on 3 dimensions, approves or requests changes
Implement Builder + Shield opus+sonnet / gpt-5.3-codex Builder implements; Shield writes TDD tests
Review Impl Inspector sonnet / gpt-5.4 Scores code on 7 dimensions, approves or rejects
Test Ranger sonnet / gpt-5.3-codex Runs lint, build, and test suite
Done Auto-commits with [kanban #ID] tag

Model routing is provider-aware via kanban/models.json. Under Codex, kanban-run and kanban-batch-run intentionally resolve to the higher-capability Codex route for the pipeline agents.

Pipeline Levels

Not every task needs the full pipeline. Set the level at creation time:

Level Path Use Case
L1 Quick Req → Impl → Done File cleanup, config changes, typo fixes
L2 Standard Req → Plan → Impl → Review → Done Feature edits, bug fixes, refactoring
L3 Full Req → Plan → Plan Rev → Impl → Impl Rev → Test → Done New features, architecture changes

The AI Team

Each agent has a fixed nickname used as a signature in every field and log entry. The task card becomes a complete work record — you can always see who wrote what and when.

Nickname Role Model (Claude / Codex) Reads Writes
Planner Plan Agent opus / gpt-5.4 description plan, decision_log, done_when
Critic Plan Review sonnet / gpt-5.4 description, plan, decision_log, done_when plan_review_comments
Builder Worker opus / gpt-5.3-codex description, plan, done_when, review comments implementation_notes
Shield TDD Tester sonnet / gpt-5.3-codex description, implementation_notes implementation_notes (append)
Inspector Code Review sonnet / gpt-5.4 description, plan, done_when, implementation_notes review_comments
Ranger Test Runner sonnet / gpt-5.3-codex implementation_notes test_results
Refiner Requirements Refinement opus / gpt-5.2 title, description description (rewrite)

Signature rule — every agent prepends a header to its output:

> **Planner** `opus` · 2026-02-24T10:00:00Z

Scoring Rubrics

Review agents use structured scoring (1–5 per dimension) instead of plain approve/reject.

Critic scores plans on 3 dimensions:

Dimension What it measures
Clarity Is the plan unambiguous and actionable?
Done-When Quality Are completion criteria verifiable?
Reversibility Can changes be safely rolled back?

Average >= 4.0 → approved. Any score = 1 or average < 3.0 → changes requested.

Inspector scores implementations on 7 dimensions:

Dimension What it measures
Code Quality Clean, readable, follows conventions
Error Handling Graceful failures, no silent swallows
Type Safety Proper types, no any leaks
Security No injection, no leaked secrets
Performance No unnecessary allocations or loops
Test Coverage Critical paths covered
Completion All done_when criteria met

Completion = 1, Security = 1, or Type Safety = 1 → hard reject.

Done-When Verification Chain

The done_when field connects agents into a verification loop:

  1. Planner writes a done_when checklist with verifiable completion criteria
  2. Critic reviews done_when quality — low score triggers /kanban-refine recommendation
  3. Builder must verify every done_when item before finishing
  4. Inspector checks that all done_when criteria are actually met

Web Board Features

Three views accessible from the top tab bar:

Board

  • 7-column kanban with real-time task counts
  • Drag-and-drop between columns (enforces valid status transitions)
  • Card detail modal with lifecycle progress bar, editable requirements, level selector

List

  • Inline editing of status, level, and priority without opening the modal

Chronicle (연대기)

  • Timeline view of every lifecycle event across all tasks, grouped by ISO week
  • 6 event types: Created · Started · Plan ready · Reviewed · Tested · Completed
  • Agent Activity toggle — expands agent_log entries into the timeline
  • Click any event row to open the full task detail modal

Common

  • Search by title, description, tags, or #ID
  • Sort by creation date, completion date, or default rank (persisted in localStorage)
  • Hide old Done toggle (3d+ threshold, persisted in localStorage)
  • Multi-project support — all projects on one board, or filter by project (persisted in localStorage)
  • Copy card reference — click to copy #ID Title to clipboard
  • Notes with markdown support
  • Image attachments with drag-and-drop upload (stored in Cloudflare R2)
  • Markdown rendering in plan, implementation notes, and reviews
  • Mermaid diagrams rendered inline
  • Agent log viewer — full chronological history of all agents per task
  • 10s auto-refresh (pauses when modal is open or dragging)
  • Dark theme by default

Commands Reference


Architecture

~/.claude/ and ~/.codex/
├── skills/
│   ├── kanban/              # CRUD & board (SKILL.md + shared context + schema + templates)
│   │   ├── SKILL.md
│   │   ├── shared.md        # Shared context (pipeline, API endpoints, error handling)
│   │   ├── schema.md        # PostgreSQL schema & JSON field formats
│   │   └── templates/       # Agent prompt templates
│   │       ├── plan-agent.md
│   │       ├── review-agent.md
│   │       ├── worker-agent.md
│   │       ├── tdd-tester.md
│   │       ├── code-review-agent.md
│   │       └── test-runner.md
│   ├── kanban-run/          # Pipeline orchestration
│   │   └── SKILL.md
│   ├── kanban-refine/       # Requirements refinement interview
│   │   └── SKILL.md
│   ├── kanban-explore/      # Codebase exploration & task seeding
│   │   └── SKILL.md
│   └── kanban-init/         # Project registration skill
│       └── SKILL.md
└── kanban-board/            # Central web board (Vite + TypeScript → Neon PostgreSQL)
    ├── plugins/kanban-api.ts
    ├── .env.example         # Template — copy to .env and fill in credentials
    └── .env                 # DATABASE_URL + CLOUDFLARE_R2_* (gitignored)

Neon PostgreSQL              # Centralized DB — all projects, all PCs
└── tasks table              # `project` column isolates per-project data

/
├── .claude/kanban.json      # Project config {"project": "my-project"}
├── .codex/kanban.json       # Same project config for Codex
└── kanban-board/start.sh    # Launcher: ~/.codex/kanban-board 우선, 없으면 ~/.claude/kanban-board

All task data lives in Neon — accessible from any machine without file sync.


Cross-PC Sync

Task data is stored in Neon PostgreSQL — sync across PCs is built-in. Any machine with the DATABASE_URL and the kanban-board running sees the same data instantly.

No OneDrive, no symlinks, no WAL conflicts.


Other Skills

This repo also includes utility skills:

Skill Description
model-router Routes Task tool subagents to optimal Claude model (Haiku/Sonnet/Opus) based on task complexity
gemini-claude-loop Dual-AI engineering loop — Claude plans and implements, Gemini validates and reviews

Install: cp -R ~/.claude/skills/ or ~/.codex/skills/ (or symlink from repo)


License

MIT

View this README on GitHub

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