BEADS Multi-Agent Orchestration Skill

This skill coordinates a swarm of specialized AI agents to autonomously handle GitHub Issues from creation to merged PR.

Quick Start

Start Work on a GitHub Issue

# User triggers via any of:
@beads start #123
bd start 123
/project:beads-start 123

Check BEADS Status

bd ready          # Show tasks ready to work
bd list           # Show all tasks
bd stats          # Show project statistics
bd doctor         # Check system health

Agent Roster

See agents/ directory for detailed agent definitions.

Design Review Gate (NEW)

For complex features created via brainstorming, an automatic Design Review Gate ensures quality before implementation:

Design Document Created
        │
        ▼
┌─────────────────────────────────────────────────┐
│           DESIGN REVIEW GATE                     │
│                                                  │
│  Spawns in PARALLEL:                            │
│  • Architect Agent (technical architecture)     │
│  • Designer Agent (UX/API design)               │
│  • CTO Agent (TDD readiness)                    │
│                                                  │
│  ALL THREE must approve to proceed              │
└─────────────────────────────────────────────────┘
        │
        ├── Any NEEDS_REVISION? → Iterate on design (max 3x)
        │
       ALL APPROVED
        │
        ▼
   Create BEADS Epic → Begin Implementation

Triggering the Design Review Gate

The gate is automatically triggered when:

• superpowers:brainstorming completes and commits a design doc
• User runs /project:review-design <path-to-design.md>

Review Criteria by Agent

Iteration Protocol

• Max 3 iterations before human escalation
• Each iteration: revise design → re-run all reviewers
• Escalation options: Override / Defer / Cancel

See goodtogo:design-review-gate skill for full details.

Workflow Overview

GitHub Issue #123 (agent-ready label)
        │
        ▼
┌─────────────────────────────────────┐
│       Issue Orchestrator             │
│  Creates BEADS epic, delegates work  │
└─────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       Research Phase                 │
│  Researcher Agent explores codebase  │
└─────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       Planning Phase                 │
│  Architect Agent creates plan        │
└─────────────────────────────────────┘
        │
        ▼
┌───────────────────────────────────────────────────────────────┐
│              DESIGN REVIEW GATE (PARALLEL)                     │
│                                                                │
│  ┌─────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌───────┐ │
│  │   PM    │ │ Architect│ │ Designer │ │ Security │ │  CTO  │ │
│  │(users)  │ │  (tech)  │ │ (UX/API) │ │ (threats)│ │ (TDD) │ │
│  └─────────┘ └──────────┘ └──────────┘ └──────────┘ └───────┘ │
│                                                                │
│  ALL FIVE must approve (max 3 iterations)                      │
└───────────────────────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       Implementation Phase           │
│  Coder Agent implements with TDD     │
└─────────────────────────────────────┘
        │
        ├──────────────────────────────┐
        ▼                              ▼
┌───────────────────┐    ┌───────────────────┐
│   Code Review     │    │  Security Audit   │
│   Agent           │    │  Agent            │
└───────────────────┘    └───────────────────┘
        │                              │
        └──────────────┬───────────────┘
                       ▼
┌─────────────────────────────────────┐
│   PR Creation (Auto-Shepherd)        │
│   bin/create-pr-with-shepherd.sh     │
│   → Auto-invokes pr-shepherd skill   │
└─────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       PR Shepherd (Automatic)        │
│  Monitors CI, handles reviews,       │
│  resolves threads automatically      │
└─────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       Human Approval & Merge         │
└─────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────┐
│       Close Epic & Extract Learnings │
└─────────────────────────────────────┘

Automatic PR Review Cycles

When a PR is created via bin/create-pr-with-shepherd.sh, the script outputs instructions to start monitoring:

1. Creates the PR with proper title/body
2. Start pr-shepherd using /project:pr-shepherd <pr-number>
3. PR Shepherd monitors: CI status, review comments, thread resolution
4. Auto-fixes: Lint, type errors, test failures in your code
5. Reports when ready: All CI green, all threads resolved

For manually-created PRs, invoke /project:pr-shepherd <pr-number> to start the monitoring cycle.

BEADS Commands Reference

Issue Management

# Create epic for GitHub Issue
bd create "Feature: User Auth" --type epic --issue 123

# Create task under epic
bd create "Research auth patterns" --type task --parent bd-abc123

# Add dependency
bd dep add <blocked-task> <blocking-task>

# Update status
bd update <task-id> --status open|in_progress|blocked|closed

# Close with reason
bd close <task-id> --reason "Completed successfully"

Task Discovery

# Show ready (unblocked) tasks
bd ready --json

# List all tasks under epic
bd list --parent <epic-id>

# Show blocked tasks
bd blocked

# Show task details
bd show <task-id> --json

Labels for Custom States

# Waiting for human input
bd label add <task-id> waiting:human

# Waiting for CI
bd label add <task-id> waiting:ci

# Agent failed, needs intervention
bd label add <task-id> agent:failed

# Review iteration tracking
bd label add <task-id> review:iteration-1

Sync Operations

# Check sync status
bd sync --status

# Pull updates from main
bd sync --from-main

# Export to JSONL
bd export

Starting Work on an Issue

Step 1: Verify Issue is Ready

# Check Issue has agent-ready label
gh issue view 123 --json labels | jq '.labels[].name' | grep agent-ready

Step 2: Create BEADS Epic

# Get Issue details
ISSUE=$(gh issue view 123 --json title,body,number)

# Create epic linked to Issue
bd create "$(echo $ISSUE | jq -r .title)" --type epic --issue 123 --json

Step 3: Post Acknowledgment

gh issue comment 123 --body "🤖 Agent claiming this issue. BEADS epic created."

Step 4: Spawn Issue Orchestrator

Use the Task tool to spawn the Issue Orchestrator agent:

Task({
  subagent_type: "general-purpose",
  description: "Issue Orchestrator for #123",
  prompt: `You are the ISSUE ORCHESTRATOR agent.

Read the agent definition at:
.claude/plugins/goodtogo/skills/beads/agents/issue-orchestrator.md

Your task:
- Epic ID: <epic-id>
- GitHub Issue: #123
- Begin the orchestration workflow

Follow the workflow phases exactly as specified.`,
});

Human Escalation Protocol

When to Escalate

1. Ambiguous Requirements: Issue lacks clarity
2. Conflicting Constraints: Can't satisfy all requirements
3. Risk Decision: Security or data concerns
4. Blocked > 1 Hour: External dependency needed
5. 3 Failed Iterations: Agent can't resolve issue

How to Escalate

# Mark task as waiting
bd update <task-id> --status blocked
bd label add <task-id> waiting:human

# Post to GitHub Issue
gh issue comment <number> --body "$(cat <<'EOF'
## 🤖 Agent Request: <type>

**Task**: <task-id>
**Question**: <clear question>

### Options
1. **Option A**: <description>
2. **Option B**: <description>

### Agent Recommendation
<recommendation>

---
Reply: `@beads approve <task-id>` or `@beads respond <task-id> <option>`
EOF
)"

Human Response Patterns

# Approve a blocked task
@beads approve bd-abc123

# Respond with choice
@beads respond bd-abc123 "Use option A"

# Request changes
@beads request-changes bd-abc123 "Need more error handling"

# Defer to later
@beads defer bd-abc123 "Discuss in Monday standup"

Agent Spawning Patterns

Sequential Spawning

// Spawn Researcher first
const researchResult = await Task({
  subagent_type: "general-purpose",
  description: "Research for issue #123",
  prompt: researcherPrompt,
});

// Then spawn Architect with research output
const planResult = await Task({
  subagent_type: "general-purpose",
  description: "Planning for issue #123",
  prompt: architectPrompt + researchResult,
});

Parallel Spawning

// Spawn Code Review and Security Audit in parallel
const [reviewResult, securityResult] = await Promise.all([
  Task({
    subagent_type: "general-purpose",
    description: "Code review for #123",
    prompt: codeReviewPrompt,
  }),
  Task({
    subagent_type: "general-purpose",
    description: "Security audit for #123",
    prompt: securityAuditPrompt,
  }),
]);

Knowledge Integration

CRITICAL: Prime Before Starting Work

ALL agents MUST prime their context before starting ANY work. This prevents bad assumptions and ensures alignment with established patterns.

# General prime (loads critical rules + gotchas)
npx tsx scripts/beads-prime.ts

# Prime for specific files you'll modify
npx tsx scripts/beads-prime.ts --files "src/lib/services/*.ts" "src/app/api/*.ts"

# Prime for specific topic
npx tsx scripts/beads-prime.ts --keywords "authentication" "jwt"

# Prime for work type
npx tsx scripts/beads-prime.ts --work-type planning     # Before planning
npx tsx scripts/beads-prime.ts --work-type implementation  # Before coding
npx tsx scripts/beads-prime.ts --work-type review       # Before reviewing
npx tsx scripts/beads-prime.ts --work-type research     # Before exploring

# Combined (most thorough)
npx tsx scripts/beads-prime.ts --files "<files>" --keywords "<topic>" --work-type <type>

The prime command outputs relevant facts categorized as:

• MUST FOLLOW: Critical rules (NEVER/ALWAYS/MUST statements)
• GOTCHAS: Common pitfalls to avoid
• PATTERNS: Best practices for this codebase
• DECISIONS: Architectural choices

After Completing Work

Run self-reflection to extract learnings:

# Fetch recent PR comments
GITHUB_TOKEN=$(gh auth token) npx tsx scripts/beads-fetch-pr-comments.ts --days 7

# Use self-reflect skill to evaluate and add learnings
/project:self-reflect

Or spawn Knowledge Curator agent:

Task({
  subagent_type: "general-purpose",
  description: "Extract learnings from epic",
  prompt: `Review completed epic <epic-id> and extract learnings.

FIRST: Run \`npx tsx scripts/beads-prime.ts --work-type review\` to load context.

Then analyze:
- What patterns were used?
- What gotchas were discovered?
- What should future agents know?

Use the knowledge capture service to store learnings.`,
});

Success Criteria Checklist

Before closing an epic, verify ALL:

• All BEADS tasks under epic are closed
• PR is created and linked to GitHub Issue
• gtg returns READY status (deterministic PR readiness gate)

  gtg "$PR_NUMBER" --repo "$OWNER/$REPO" --format json > /tmp/gtg.json
  STATUS=$(jq -r '.status' /tmp/gtg.json)
  # Must return READY status before proceeding
  

• Human has approved merge
• PR is merged to main
• GitHub Issue is closed
• Learnings extracted to knowledge base

gtg as the Deterministic Gate

The gtg command replaces manual verification of:

• All CI checks passing
• All PR comments addressed
• All PR threads resolved

A PR is NOT ready until gtg returns READY status.

# Check PR readiness deterministically (recommended: parse JSON status)
gtg 123 --repo owner/repo --format json > /tmp/gtg.json
STATUS=$(jq -r '.status' /tmp/gtg.json)
echo "Status: $STATUS"  # Must be READY

# Alternative: use semantic exit codes for shell scripts
gtg 123 --repo owner/repo -q  # Exit code 0 = READY

# For details on what's blocking:
gtg 123 --repo owner/repo --format json | jq '.action_items'

Troubleshooting

Task Stuck in Progress

# Check task status
bd show <task-id> --json

# Check for orphaned agent
# If agent failed, reset and retry
bd update <task-id> --status open
bd label remove <task-id> agent:failed

Circular Dependencies

# Run doctor to detect
bd doctor

# If found, restructure dependencies
bd dep remove <task1> <task2>

BEADS Sync Issues

# Check sync status
bd sync --status

# Force export
bd export

# Pull from main
bd sync --from-main

Directory Structure

.claude/plugins/goodtogo/skills/beads/
├── SKILL.md                    # This file
└── agents/
    ├── issue-orchestrator.md   # Main coordinator
    ├── researcher-agent.md     # Codebase exploration
    ├── architect-agent.md      # Implementation planning
    ├── product-manager-agent.md # Use case & user benefit review (NEW)
    ├── designer-agent.md       # UX/API design review (NEW)
    ├── security-design-agent.md # Security threat modeling (NEW)
    ├── cto-agent.md            # TDD readiness review
    ├── coder-agent.md          # TDD implementation
    ├── code-review-agent.md    # Internal code review
    ├── security-auditor-agent.md # Security review (implementation)
    └── pr-shepherd-agent.md    # PR lifecycle management

.claude/plugins/goodtogo/skills/design-review-gate/
└── SKILL.md                    # Design review gate orchestrator (NEW)

.claude/plugins/goodtogo/skills/brainstorming-extension/
└── SKILL.md                    # Hooks brainstorming to review gate (NEW)

.claude/commands/
└── review-design.md            # /project:review-design command (NEW)

.claude/rubrics/
├── plan-review-rubric.md       # Used by CTO Agent
└── code-review-rubric.md       # Used by Code Review Agent

.beads/
├── beads.db                    # SQLite database
├── issues.jsonl                # Issue/task data
└── knowledge/                  # Curated learnings
    ├── codebase-facts.jsonl
    ├── patterns.jsonl
    └── anti-patterns.jsonl