export default goap-agent

name	goap-agent
description	Invoke for complex multi-step tasks requiring intelligent planning and multi-agent coordination. Use when tasks need decomposition, dependency mapping, parallel/sequential/swarm/iterative execution strategies, or coordination of multiple specialized agents with quality gates and dynamic optimization.

GOAP Agent Skill: Goal-Oriented Action Planning

Enable intelligent planning and execution of complex multi-step tasks through systematic decomposition, dependency mapping, and coordinated multi-agent execution.

Always use the plans/ folder for all files.

Quick Reference

• Execution Strategies - Detailed guide on parallel, sequential, swarm, hybrid, and iterative execution patterns

When to Use This Skill

Use this skill when facing:

• Complex Multi-Step Tasks: Tasks requiring 5+ distinct steps or multiple specialized capabilities
• Cross-Domain Problems: Issues spanning multiple areas (storage, API, testing, documentation)
• Optimization Opportunities: Tasks that could benefit from parallel or hybrid execution
• Quality-Critical Work: Projects requiring validation checkpoints and quality gates
• Resource-Intensive Operations: Large refactors, migrations, or architectural changes
• Ambiguous Requirements: Tasks needing structured analysis before execution

Core GOAP Methodology

The GOAP Planning Cycle

1. ANALYZE → Understand goals, constraints, resources
2. DECOMPOSE → Break into atomic tasks with dependencies
3. STRATEGIZE → Choose execution pattern (parallel/sequential/swarm/hybrid/iterative)
4. COORDINATE → Assign tasks to specialized agents
5. EXECUTE → Run with monitoring and quality gates
6. SYNTHESIZE → Aggregate results and validate success

Phase 1: Task Analysis

Initial Assessment

## Task Analysis

**Primary Goal**: [Clear statement of what success looks like]

**Constraints**:
- Time: [Urgent / Normal / Flexible]
- Resources: [Available agents, tools, data]
- Dependencies: [External systems, prerequisites]

**Complexity Level**:
- Simple: Single agent, <3 steps
- Medium: 2-3 agents, some dependencies
- Complex: 4+ agents, mixed execution modes
- Very Complex: Multiple phases, many dependencies

**Quality Requirements**:
- Testing: [Unit / Integration / E2E]
- Standards: [AGENTS.md compliance, formatting, linting]
- Documentation: [API docs, examples, guides]
- Performance: [Speed, memory, scalability]

Context Gathering

1. Codebase Understanding: Use Explore agent to understand relevant code
2. Past Patterns: Check if similar tasks have been done before
3. Available Resources: Identify available agents and their capabilities
4. Current State: Understand starting conditions and existing implementations

Phase 2: Task Decomposition

Use the task-decomposition skill to break down the goal:

## Task Decomposition: [Task Name]

### Main Goal
[Clear statement of primary objective]

### Sub-Goals
1. [Component 1] - Priority: P0
   - Success Criteria: [How to verify]
   - Dependencies: [Prerequisites]
   - Complexity: [Low/Medium/High]

2. [Component 2] - Priority: P1
   - Success Criteria: [How to verify]
   - Dependencies: [Component 1]
   - Complexity: [Low/Medium/High]

### Atomic Tasks
**Component 1: [Name]**
- Task 1.1: [Action] (Agent: type, Deps: none)
- Task 1.2: [Action] (Agent: type, Deps: 1.1)

### Dependency Graph

Task 1.1 → Task 1.2 → Task 2.1 ↘ Task 1.3 (parallel) → Task 2.2

Key Decomposition Principles

• Atomic: Each task is indivisible and clear
• Testable: Can verify completion
• Independent where possible: Minimize dependencies
• Assigned: Each task maps to an agent capability

Phase 3: Strategy Selection

Choose execution strategy based on task characteristics. See execution-strategies.md for detailed guide.

Quick Strategy Guide

Strategy	When to Use	Speed	Complexity
Parallel	Independent tasks, time-critical	Nx	High
Sequential	Dependent tasks, order matters	1x	Low
Swarm	Many similar tasks	~Nx	Medium
Hybrid	Mixed requirements	2-4x	Very High
Iterative	Progressive refinement, convergence	Varies	Medium

Decision Tree

Needs iterative refinement?
  ├─ Yes (until criteria met or converged) → ITERATIVE
  └─ No → Is time critical?
      ├─ Yes → Can tasks run in parallel?
      │   ├─ Yes → PARALLEL
      │   └─ No → SEQUENTIAL (prioritize critical path)
      └─ No → Are tasks similar?
          ├─ Yes (many similar) → SWARM
          ├─ No (mixed) → HYBRID
          └─ Simple linear → SEQUENTIAL

Phase 4: Agent Assignment

Agent Capability Matrix

Agent Type	Capabilities	Best For
feature-implementer	Design, implement, test features	New functionality
debugger	Diagnose, fix runtime issues	Bug fixes, performance
test-runner	Execute tests, diagnose failures	Test validation
refactorer	Improve code quality, structure	Code improvements
code-reviewer	Review quality, compliance	Quality assurance
loop-agent	Iterative refinement, convergence detection	Progressive improvements

Assignment Principles

1. Match agent capabilities to task requirements
2. Balance workload across agents
3. Consider agent specialization
4. Plan for quality validation

Phase 5: Execution Planning

Create the Execution Plan

## Execution Plan: [Task Name]

### Overview
- Strategy: [Parallel/Sequential/Swarm/Hybrid/Iterative]
- Total Tasks: [N]
- Estimated Duration: [Time]
- Quality Gates: [N checkpoints]

### Phase 1: [Phase Name]
**Tasks**:
- Task 1: [Description] (Agent: type)
- Task 2: [Description] (Agent: type)

**Quality Gate**: [Validation criteria]

### Phase 2: [Phase Name]
**Tasks**:
- Task 3: [Description] (Agent: type)

**Quality Gate**: [Validation criteria]

### Overall Success Criteria
- [ ] All tasks complete
- [ ] Quality gates passed
- [ ] Tests passing
- [ ] Documentation updated

### Contingency Plans
- If Phase 1 fails → [Recovery plan]
- If tests fail → [Diagnostic approach]

Phase 6: Coordinated Execution

Parallel Execution

**Launching parallel agents:**
- Agent 1 (feature-implementer) → Task A
- Agent 2 (feature-implementer) → Task B
- Agent 3 (test-runner) → Task C

**Coordination**:
- All agents work simultaneously
- Monitor progress independently
- Aggregate results when all complete

Sequential Execution

**Launching sequential agents:**
Phase 1: Agent 1 (debugger) → Diagnose issue
  ↓ Quality Gate: Root cause identified
Phase 2: Agent 2 (refactorer) → Apply fix
  ↓ Quality Gate: Tests pass
Phase 3: Agent 3 (code-reviewer) → Validate

Monitoring During Execution

• Track agent progress
• Monitor for failures
• Validate intermediate results
• Adjust plan if needed

Phase 7: Result Synthesis

Aggregate Results

## Execution Summary: [Task Name]

### ✓ Completed Tasks
- [Task 1]: Success
- [Task 2]: Success

### 📦 Deliverables
- [File/Feature 1]
- [File/Feature 2]

### ✅ Quality Validation
- Tests: [Pass/Fail] ([coverage]%)
- Linting: [Pass/Fail]
- Standards: [Compliant]

### 📊 Performance Metrics
- Duration: [actual vs estimated]
- Efficiency: [parallel speedup if applicable]

### 💡 Recommendations
- [Improvement 1]
- [Improvement 2]

### 🎓 Lessons Learned
- [What worked well]
- [What to improve]

Common GOAP Patterns

Pattern 1: Research → Implement → Validate

Phase 1 (Sequential): Research
  - Explore agent → Understand codebase
  - Quality Gate: Architecture documented

Phase 2 (Parallel): Implement
  - feature-implementer (A) → Module 1
  - feature-implementer (B) → Module 2
  - Quality Gate: Implementations complete

Phase 3 (Sequential): Validate
  - test-runner → All tests
  - code-reviewer → Final review
  - Quality Gate: Ready for merge

Pattern 2: Investigate → Diagnose → Fix → Verify

Phase 1: Investigate
  - debugger → Reproduce issue
  - Quality Gate: Issue reproduced

Phase 2: Diagnose
  - debugger → Root cause analysis
  - Quality Gate: Root cause identified

Phase 3: Fix
  - refactorer → Apply fix
  - Quality Gate: Fix implemented

Phase 4: Verify
  - test-runner → Regression tests
  - Quality Gate: Tests pass

Pattern 3: Audit → Improve → Validate

Phase 1: Audit
  - code-reviewer → Quality audit
  - Quality Gate: Issues identified

Phase 2 (Swarm): Improve
  - Multiple refactorer agents
  - Work queue: [issue list]
  - Quality Gate: All issues addressed

Phase 3: Validate
  - test-runner → Full test suite
  - code-reviewer → Final check
  - Quality Gate: Quality targets met

Error Handling & Recovery

Agent Failure Recovery

**If agent fails:**
1. Log failure reason
2. Check quality gate status
3. Options:
   - Retry same agent (transient error)
   - Assign to different agent (agent issue)
   - Modify task (requirements issue)
   - Escalate to user (blocking issue)

Quality Gate Failure

**If quality gate fails:**
1. Identify failing criteria
2. Diagnose root cause
3. Options:
   - Re-run previous phase with fixes
   - Adjust quality criteria (if appropriate)
   - Change strategy (e.g., parallel → sequential for debugging)

Blocked Dependencies

**If dependency blocks progress:**
1. Identify blocking task
2. Prioritize unblocking
3. Options:
   - Execute dependency first (re-order)
   - Remove dependency (refactor plan)
   - Parallel work on independent tasks

Best Practices

DO:

✓ Break tasks into atomic, testable units ✓ Define clear quality gates between phases ✓ Match agent capabilities to task requirements ✓ Monitor progress and validate incrementally ✓ Document decisions and rationale ✓ Learn from execution for future planning ✓ Use parallel execution where safe ✓ Validate dependencies before execution

DON'T:

✗ Create monolithic tasks (break them down) ✗ Skip quality gates (leads to cascading failures) ✗ Assume tasks are independent (verify carefully) ✗ Ignore agent failures (address immediately) ✗ Over-complicate simple tasks (use sequential) ✗ Under-estimate coordination overhead ✗ Forget to aggregate and synthesize results

Integration with Other Skills

• task-decomposition: Use for Phase 2 (breaking down complex goals)
• agent-coordination: Use for Phase 6 (coordinating multiple agents)
• parallel-execution: Use for parallel strategy implementation
• loop-agent: Use for iterative refinement strategy implementation
• All specialized agents (feature-implementer, debugger, test-runner, etc.)

Quick Example

Task: Implement authentication system

## GOAP Plan

### Phase 1: Analysis (Sequential)
- goap-agent → Define requirements
- Quality Gate: Requirements clear

### Phase 2: Implementation (Parallel)
- Agent A → User model + database
- Agent B → Auth middleware
- Agent C → API endpoints
- Quality Gate: All components implemented

### Phase 3: Integration (Sequential)
- feature-implementer → Wire components together
- test-runner → Integration tests
- Quality Gate: Tests pass

### Phase 4: Validation (Sequential)
- code-reviewer → Security review
- Quality Gate: Approved for deployment

Success Metrics

Planning Quality

• Clear decomposition with measurable tasks
• Realistic time estimates
• Appropriate strategy selection
• Well-defined quality gates

Execution Quality

• Tasks completed as planned
• Quality gates passed
• Minimal re-work required
• Efficient resource utilization

Learning

• Document what worked well
• Identify improvement areas
• Update patterns for future use
• Share knowledge with team

Advanced Topics

Dynamic Re-Planning

If during execution:

• Dependencies change
• Requirements clarified
• Blockers discovered
• Performance issues found

Then:

1. Pause execution
2. Re-analyze with new information
3. Adjust plan (tasks, dependencies, strategy)
4. Resume with updated plan

Optimization Techniques

• Critical path optimization: Parallelize non-critical-path tasks
• Resource pooling: Share agents across similar tasks
• Incremental delivery: Complete and validate phases incrementally
• Adaptive strategy: Switch strategies based on progress

Summary

GOAP enables systematic planning and execution of complex tasks through:

1. Structured Analysis: Understand goals, constraints, and context
2. Intelligent Decomposition: Break into atomic, testable tasks
3. Strategic Execution: Choose optimal execution pattern
4. Quality Assurance: Validate at checkpoints
5. Coordinated Agents: Leverage specialized capabilities
6. Continuous Learning: Improve from each execution

Use GOAP when facing complex, multi-step challenges requiring coordination, optimization, and quality assurance.