skills/polyphony/SKILL.md
Multi-agent orchestration with container-isolated workspaces — each agent session runs in its own Docker container with independent git branches
npx skillsauth add alinaqi/claude-bootstrap polyphonyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Container-isolated workspaces for parallel agent execution. Each agent gets its own Docker container with a full git clone on its own branch. No conflicts, independent tests, clean PRs.
gh api) or local SQLite queuegit clone --reference, branch checkout, cleanupDISCOVERED -> CLAIMED -> ROUTED -> PROVISIONED -> RUNNING -> VERIFYING -> LANDED
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v v
FAILED --> BLOCKED
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CLAIMED (retry)
Check:
command -v docker &>/dev/null || command -v orbctl &>/dev/null
All config lives in ~/.polyphony/:
| File | Purpose |
|------|---------|
| config.yaml | Workspace root, poll interval, max concurrency |
| identities.yaml | Named credential bundles with volume paths |
| agents.yaml | Agent profiles (CLI commands, strengths) |
| routing.yaml | Routing rules and fallback chains |
Initialize with: polyphony init
Rules are evaluated top-down; first match wins. Each rule has a match predicate and an agent target.
rules:
- match: { task_type: docs, risk: low }
agent: kimi
- match: { task_type: bugfix }
agent: codex
- match: { risk: high }
agent: claude
default:
agent: claude
fallback: [codex, kimi]
Each dimension scores 0-2. Total 0-10.
| Dimension | Source | |-----------|--------| | Cyclomatic depth | LOC + scope size | | Fan-out | Number of callers | | Security boundary | Auth/PII keywords | | Concurrency | Lock/transaction keywords | | Domain invariants | Risk level + task type |
Routing thresholds:
Each task gets:
polyphony-worker:latest/workspace (not a worktree)~/.claude:/home/worker/.claude:ro)polyphony init # Create ~/.polyphony/ with config templates
polyphony spawn "Fix auth bug" # Create and route a task
polyphony status # Show task states
polyphony cleanup # Remove completed workspaces
testing
Multi-model validation council — auto-validate plans, architecture changes, and PRs via validate-plan/review before executing
development
Mandatory code reviews via /code-review before commits and deploys
development
# Visual Validation — Autonomous Screenshot Verification ## Philosophy Every UI change should be visually verified before it ships. Peekaboo captures pixel-accurate screenshots. The system compares before/after and flags visual regressions. No manual "looks good to me" — the machine verifies what the machine built. ## Autonomous Flow ``` static/* files modified (detected by auto-review-hook or E2E testkit) ↓ peekaboo image --mode screen → ~/.maggy/visual-verify/after-{ts}.png ↓ Compa
tools
# Model Routing System ## How Routing Decisions Are Made Every user prompt goes through a 9-tier classification pipeline before any AI model processes it. The system answers three questions: 1. **Which model should handle this?** — 9-tier cost/complexity classification 2. **Is the classifier itself working?** — Cascading fallback (qwen3 → kimi → deepseek → cache) 3. **Can we verify the result?** — Tool-level fallback + auto-evaluation ### The Pipeline ``` User types prompt ↓ UserPromptS