Claude-Code-Workflow/.codex/skills/workflow-plan-execute/phases/04-execution.md

Phase 4: Execution (Conditional)

Execute implementation tasks using agent orchestration with lazy loading, progress tracking, and optional auto-commit. This phase is triggered conditionally after Phase 3 completes.

Trigger Conditions

• User Selection: User chooses "Start Execution" in Phase 3 User Decision
• Auto Mode (--yes): Automatically enters Phase 4 after Phase 3 completes

Auto Mode

When --yes or -y:

• Completion Choice: Automatically completes session (runs workflow:session:complete --yes)

When --with-commit:

• Auto-Commit: After each agent task completes, commit changes based on summary document
• Commit Principle: Minimal commits - only commit files modified by the completed task
• Commit Message: Generated from task summary with format: "feat/fix/refactor: {task-title} - {summary}"

Key Design Principles

1. No Redundant Discovery: Session ID and planning docs already available from Phase 1-3
2. Autonomous Execution: Complete entire workflow without user interruption
3. Lazy Loading: Task JSONs read on-demand during execution, not upfront
4. ONE AGENT = ONE TASK JSON: Each agent instance executes exactly one task JSON file
5. IMPL_PLAN-Driven Strategy: Execution model derived from planning document
6. Continuous Progress Tracking: TodoWrite updates throughout entire workflow
7. Subagent Lifecycle: Explicit lifecycle management with spawn_agent → wait → close_agent

Execution Flow

Step 1: TodoWrite Generation
   ├─ Update session status to "active"
   ├─ Parse TODO_LIST.md for task statuses
   ├─ Generate TodoWrite for entire workflow
   └─ Prepare session context paths

Step 2: Execution Strategy Parsing
   ├─ Parse IMPL_PLAN.md Section 4 (Execution Model)
   └─ Fallback: Analyze task structure for smart defaults

Step 3: Task Execution Loop
   ├─ Get next in_progress task from TodoWrite
   ├─ Lazy load task JSON
   ├─ Launch agent (spawn_agent → wait → close_agent)
   ├─ Mark task completed (update IMPL-*.json status)
   ├─ [with-commit] Auto-commit changes
   └─ Advance to next task

Step 4: Completion
   ├─ Synchronize all statuses
   ├─ Generate summaries
   └─ ASK_USER: Review or Complete Session

Step 1: TodoWrite Generation

Step 1.0: Update Session Status to Active

Before generating TodoWrite, update session status from "planning" to "active":

# Update session status (idempotent - safe to run if already active)
jq '.status = "active" | .execution_started_at = (.execution_started_at // now | todate)' \
  ${projectRoot}/.workflow/active/${sessionId}/workflow-session.json > tmp.json && \
  mv tmp.json ${projectRoot}/.workflow/active/${sessionId}/workflow-session.json

This ensures the dashboard shows the session as "ACTIVE" during execution.

Step 1.1: Parse TODO_LIST.md

1. Create TodoWrite List: Generate task list from TODO_LIST.md (not from task JSONs)
   - Parse TODO_LIST.md to extract all tasks with current statuses
   - Identify first pending task with met dependencies
   - Generate comprehensive TodoWrite covering entire workflow
2. Prepare Session Context: Inject workflow paths for agent use
3. Validate Prerequisites: Ensure IMPL_PLAN.md and TODO_LIST.md exist and are valid

Performance Optimization: TODO_LIST.md provides task metadata and status. Task JSONs are NOT loaded here - deferred to Step 3 (lazy loading).

Step 2: Execution Strategy Parsing

Step 2A: Parse Execution Strategy from IMPL_PLAN.md

Read IMPL_PLAN.md Section 4 to extract:

• Execution Model: Sequential | Parallel | Phased | TDD Cycles
• Parallelization Opportunities: Which tasks can run in parallel
• Serialization Requirements: Which tasks must run sequentially
• Critical Path: Priority execution order

Step 2B: Intelligent Fallback

If IMPL_PLAN.md lacks execution strategy, use intelligent fallback:

1. Analyze task structure:
   • Check meta.execution_group in task JSONs
   • Analyze depends_on relationships
   • Understand task complexity and risk
2. Apply smart defaults:
   • No dependencies + same execution_group → Parallel
   • Has dependencies → Sequential (wait for deps)
   • Critical/high-risk tasks → Sequential
3. Conservative approach: When uncertain, prefer sequential execution

Execution Models

1. Sequential Execution

When: IMPL_PLAN specifies "Sequential" OR no clear parallelization guidance Pattern: Execute tasks one by one in TODO_LIST order via spawn_agent → wait → close_agent per task TodoWrite: ONE task marked as in_progress at a time

2. Parallel Execution

When: IMPL_PLAN specifies "Parallel" with clear parallelization opportunities Pattern: Execute independent task groups concurrently by spawning multiple agents and batch waiting TodoWrite: MULTIPLE tasks (in same batch) marked as in_progress simultaneously Agent Instantiation: spawn one agent per task (respects ONE AGENT = ONE TASK JSON rule), then batch wait

3. Phased Execution

When: IMPL_PLAN specifies "Phased" with phase breakdown Pattern: Execute tasks in phases, respect phase boundaries TodoWrite: Within each phase, follow Sequential or Parallel rules

Step 3: Task Execution Loop

Execution Loop Pattern

while (TODO_LIST.md has pending tasks) {
  next_task_id = getTodoWriteInProgressTask()
  task_json = Read(${projectRoot}/.workflow/active/{session}/.task/{next_task_id}.json)  // Lazy load
  executeTaskWithAgent(task_json)  // spawn_agent → wait → close_agent
  updateTodoListMarkCompleted(next_task_id)
  advanceTodoWriteToNextTask()
}

Execution Process per Task

1. Identify Next Task: From TodoWrite, get the next in_progress task ID
2. Load Task JSON on Demand: Read .task/{task-id}.json for current task ONLY
3. Validate Task Structure: Ensure all required fields exist (id, title, status, meta, context, flow_control)
4. Launch Agent: Invoke specialized agent via spawn_agent with complete context including flow control steps
5. Wait for Completion: wait for agent result, handle timeout
6. Close Agent: close_agent to free resources
7. Collect Results: Gather implementation results and outputs
8. [with-commit] Auto-Commit: If --with-commit flag enabled, commit changes based on summary
9. Continue Workflow: Identify next pending task from TODO_LIST.md and repeat

Note: TODO_LIST.md updates are handled by agents (e.g., code-developer.md), not by the orchestrator.

Agent Prompt Template (Sequential)

Path-Based Invocation: Pass paths and trigger markers, let agent parse task JSON autonomously.

// Step 1: Spawn agent
const agentId = spawn_agent({
  message: `
## TASK ASSIGNMENT

### MANDATORY FIRST STEPS (Agent Execute)
1. **Read role definition**: ~/.codex/agents/${meta.agent}.md (MUST read first)
2. Read: ${projectRoot}/.workflow/project-tech.json
3. Read: ${projectRoot}/.workflow/project-guidelines.json

---

Implement task ${task.id}: ${task.title}

[FLOW_CONTROL]

**Input**:
- Task JSON: ${session.task_json_path}
- Context Package: ${session.context_package_path}

**Output Location**:
- Workflow: ${session.workflow_dir}
- TODO List: ${session.todo_list_path}
- Summaries: ${session.summaries_dir}

**Execution**: Read task JSON → Execute pre_analysis → Check execution_config.method → (CLI: handoff to CLI tool | Agent: direct implementation) → Update TODO_LIST.md → Generate summary
`
});

// Step 2: Wait for completion
const result = wait({
  ids: [agentId],
  timeout_ms: 600000  // 10 minutes per task
});

// Step 3: Close agent (IMPORTANT: always close)
close_agent({ id: agentId });

Key Markers:

• Implement keyword: Triggers tech stack detection and guidelines loading
• [FLOW_CONTROL]: Triggers flow_control.pre_analysis execution

Parallel Execution Pattern

// Step 1: Spawn agents for parallel batch
const batchAgents = [];
parallelTasks.forEach(task => {
  const agentId = spawn_agent({
    message: `
## TASK ASSIGNMENT

### MANDATORY FIRST STEPS (Agent Execute)
1. **Read role definition**: ~/.codex/agents/${task.meta.agent}.md (MUST read first)
2. Read: ${projectRoot}/.workflow/project-tech.json
3. Read: ${projectRoot}/.workflow/project-guidelines.json

---

Implement task ${task.id}: ${task.title}

[FLOW_CONTROL]

**Input**:
- Task JSON: ${session.task_json_path}
- Context Package: ${session.context_package_path}

**Output Location**:
- Workflow: ${session.workflow_dir}
- TODO List: ${session.todo_list_path}
- Summaries: ${session.summaries_dir}

**Execution**: Read task JSON → Execute pre_analysis → Check execution_config.method → (CLI: handoff to CLI tool | Agent: direct implementation) → Update TODO_LIST.md → Generate summary
`
  });
  batchAgents.push({ agentId, taskId: task.id });
});

// Step 2: Batch wait for all agents
const batchResult = wait({
  ids: batchAgents.map(a => a.agentId),
  timeout_ms: 600000
});

// Step 3: Check results and handle timeouts
if (batchResult.timed_out) {
  console.log('Some parallel tasks timed out, continuing with completed results');
}

// Step 4: Close all agents
batchAgents.forEach(a => close_agent({ id: a.agentId }));

Agent Assignment Rules

meta.agent specified → Use specified agent
meta.agent missing → Infer from meta.type:
  - "feature" → @code-developer  (role: ~/.codex/agents/code-developer.md)
  - "test-gen" → @code-developer  (role: ~/.codex/agents/code-developer.md)
  - "test-fix" → @test-fix-agent  (role: ~/.codex/agents/test-fix-agent.md)
  - "review" → @universal-executor  (role: ~/.codex/agents/universal-executor.md)
  - "docs" → @doc-generator  (role: ~/.codex/agents/doc-generator.md)

Task Status Logic

pending + dependencies_met → executable
completed → skip
blocked → skip until dependencies clear

Step 4: Completion

Process

1. Update Task Status: Mark completed tasks in JSON files
2. Generate Summary: Create task summary in .summaries/
3. Update TodoWrite: Mark current task complete, advance to next
4. Synchronize State: Update session state and workflow status
5. Check Workflow Complete: Verify all tasks are completed
6. User Choice: When all tasks finished, ask user to choose next step:

// Parse --yes flag
const autoYes = $ARGUMENTS.includes('--yes') || $ARGUMENTS.includes('-y')

if (autoYes) {
  // Auto mode: Complete session automatically
  console.log(`[--yes] Auto-selecting: Complete Session`)
  // Execute: workflow:session:complete --yes
} else {
  // Interactive mode: Ask user
  ASK_USER([{
    id: "completion-next-step",
    type: "select",
    prompt: "All tasks completed. What would you like to do next?",
    options: [
      {
        label: "Enter Review",
        description: "Run specialized review (security/architecture/quality/action-items)"
      },
      {
        label: "Complete Session",
        description: "Archive session and update manifest"
      }
    ]
  }])  // BLOCKS (wait for user response)
}

Based on user selection:

• "Enter Review": Execute workflow:review
• "Complete Session": Execute workflow:session:complete

Post-Completion Expansion

After completion, ask user whether to expand to issues (test/enhance/refactor/doc). Selected items invoke issue:new "{summary} - {dimension}".

Auto-Commit Mode (--with-commit)

Behavior: After each agent task completes, automatically commit changes based on summary document.

Minimal Principle: Only commit files modified by the completed task.

Commit Message Format: {type}: {task-title} - {summary}

Type Mapping (from meta.type):

• feature → feat | bugfix → fix | refactor → refactor
• test-gen → test | docs → docs | review → chore

Implementation:

# 1. Read summary from .summaries/{task-id}-summary.md
# 2. Extract files from "Files Modified" section
# 3. Commit: git add <files> && git commit -m "{type}: {title} - {summary}"

Error Handling: Skip commit on no changes/missing summary, log errors, continue workflow.

TodoWrite Coordination

TodoWrite Rules

Rule 1: Initial Creation

• Generate TodoWrite from TODO_LIST.md pending tasks

Rule 2: In-Progress Task Count (Execution-Model-Dependent)

• Sequential execution: Mark ONLY ONE task as in_progress at a time
• Parallel batch execution: Mark ALL tasks in current batch as in_progress simultaneously
• Execution group indicator: Show [execution_group: group-id] for parallel tasks

Rule 3: Status Updates

• Immediate Updates: Update status after each task/batch completion without user interruption
• Status Synchronization: Sync with JSON task files after updates
• Continuous Tracking: Maintain TodoWrite throughout entire workflow execution until completion

Rule 4: Workflow Completion Check

• When all tasks marked completed, prompt user to choose review or complete session

TodoWrite Examples

Sequential Execution:

TodoWrite({
  todos: [
    {
      content: "Execute IMPL-1.1: Design auth schema [code-developer] [FLOW_CONTROL]",
      status: "in_progress",  // ONE task in progress
      activeForm: "Executing IMPL-1.1: Design auth schema"
    },
    {
      content: "Execute IMPL-1.2: Implement auth logic [code-developer] [FLOW_CONTROL]",
      status: "pending",
      activeForm: "Executing IMPL-1.2: Implement auth logic"
    }
  ]
});

Parallel Batch Execution:

TodoWrite({
  todos: [
    {
      content: "Execute IMPL-1.1: Build Auth API [code-developer] [execution_group: parallel-auth-api]",
      status: "in_progress",
      activeForm: "Executing IMPL-1.1: Build Auth API"
    },
    {
      content: "Execute IMPL-1.2: Build User UI [code-developer] [execution_group: parallel-ui-comp]",
      status: "in_progress",
      activeForm: "Executing IMPL-1.2: Build User UI"
    },
    {
      content: "Execute IMPL-2.1: Integration Tests [test-fix-agent] [depends_on: IMPL-1.1, IMPL-1.2]",
      status: "pending",
      activeForm: "Executing IMPL-2.1: Integration Tests"
    }
  ]
});

Error Handling & Recovery

Common Errors & Recovery

Error Type	Cause	Recovery Strategy	Max Attempts
Execution Errors
Agent failure	Agent crash/timeout	Retry with simplified context (close_agent first, then spawn new)	2
Flow control error	Command failure	Skip optional, fail critical	1 per step
Context loading error	Missing dependencies	Reload from JSON, use defaults	3
JSON file corruption	File system issues	Restore from backup/recreate	1
Lifecycle Errors
Agent timeout	wait timed out	send_input to prompt completion, or close_agent and retry	2
Orphaned agent	Agent not closed after error	Ensure close_agent in error paths	N/A

Error Recovery with Lifecycle Management

// Safe agent execution pattern with error handling
let agentId = null;
try {
  agentId = spawn_agent({ message: taskPrompt });
  const result = wait({ ids: [agentId], timeout_ms: 600000 });

  if (result.timed_out) {
    // Option 1: Send prompt to complete
    send_input({ id: agentId, message: "Please wrap up and generate summary." });
    const retryResult = wait({ ids: [agentId], timeout_ms: 120000 });
  }

  // Process results...
  close_agent({ id: agentId });
} catch (error) {
  // Ensure cleanup on error
  if (agentId) close_agent({ id: agentId });
  // Handle error (retry or skip task)
}

Error Prevention

• Lazy Loading: Reduces upfront memory usage and validation errors
• Atomic Updates: Update JSON files atomically to prevent corruption
• Dependency Validation: Check all depends_on references exist
• Context Verification: Ensure all required context is available
• Lifecycle Cleanup: Always close_agent in both success and error paths

Output

• Updated: Task JSON status fields (completed)
• Created: .summaries/IMPL-*-summary.md per task
• Updated: TODO_LIST.md (by agents)
• Updated: workflow-session.json status
• Created: Git commits (if --with-commit)

Next Step

Return to orchestrator. Orchestrator handles completion summary output.