Claude-Code-Workflow/.claude/skills/review-cycle/phases/07-fix-parallel-planning.md

Phase 7: Fix Parallel Planning

Source: commands/workflow/review-cycle-fix.md Phase 2

Overview

Launch N planning agents (up to MAX_PARALLEL=10) to analyze finding batches concurrently. Each agent outputs a partial plan. Orchestrator aggregates partial plans into unified fix-plan.json.

Execution Strategy Determination

Strategy Types:

Strategy	When to Use	Stage Structure
Parallel	All groups independent, different files	Single stage, all groups in parallel
Serial	Strong dependencies, shared resources	Multiple stages, one group per stage
Hybrid	Mixed dependencies	Multiple stages, parallel within stages

Dependency Detection:

• Shared file modifications
• Utility creation + usage patterns
• Test dependency chains
• Risk level clustering (high-risk groups isolated)

Phase 2: Parallel Planning Coordination (Orchestrator)

const MAX_PARALLEL = 10;
const partialPlans = [];

// Process batches in chunks of MAX_PARALLEL
for (let i = 0; i < batches.length; i += MAX_PARALLEL) {
  const chunk = batches.slice(i, i + MAX_PARALLEL);
  const taskIds = [];

  // Launch agents in parallel (run_in_background=true)
  for (const batch of chunk) {
    const taskId = Task({
      subagent_type: "cli-planning-agent",
      run_in_background: true,
      description: `Plan batch ${batch.batch_id}: ${batch.findings.length} findings`,
      prompt: planningPrompt(batch)  // See Planning Agent template below
    });
    taskIds.push({ taskId, batch });
  }

  console.log(`Launched ${taskIds.length} planning agents...`);

  // Collect results from this chunk (blocking)
  for (const { taskId, batch } of taskIds) {
    const result = TaskOutput({ task_id: taskId, block: true });
    const partialPlan = JSON.parse(Read(`${sessionDir}/partial-plan-${batch.batch_id}.json`));
    partialPlans.push(partialPlan);
    updateTodo(`Batch ${batch.batch_id}`, 'completed');
  }
}

// Aggregate partial plans → fix-plan.json
let groupCounter = 1;
const groupIdMap = new Map();

for (const partial of partialPlans) {
  for (const group of partial.groups) {
    const newGroupId = `G${groupCounter}`;
    groupIdMap.set(`${partial.batch_id}:${group.group_id}`, newGroupId);
    aggregatedPlan.groups.push({ ...group, group_id: newGroupId, progress_file: `fix-progress-${groupCounter}.json` });
    groupCounter++;
  }
}

// Merge timelines, resolve cross-batch conflicts (shared files → serialize)
let stageCounter = 1;
for (const partial of partialPlans) {
  for (const stage of partial.timeline) {
    aggregatedPlan.timeline.push({
      ...stage, stage_id: stageCounter,
      groups: stage.groups.map(gid => groupIdMap.get(`${partial.batch_id}:${gid}`))
    });
    stageCounter++;
  }
}

// Write aggregated plan + initialize progress files
Write(`${sessionDir}/fix-plan.json`, JSON.stringify(aggregatedPlan, null, 2));
for (let i = 1; i <= aggregatedPlan.groups.length; i++) {
  Write(`${sessionDir}/fix-progress-${i}.json`, JSON.stringify(initProgressFile(aggregatedPlan.groups[i-1]), null, 2));
}

Planning Agent Template (Batch Mode)

Task({
  subagent_type: "cli-planning-agent",
  run_in_background: true,
  description: `Plan batch ${batch.batch_id}: ${batch.findings.length} findings`,
  prompt: `
## Task Objective
Analyze code review findings in batch ${batch.batch_id} and generate **partial** execution plan.

## Input Data
Review Session: ${reviewId}
Fix Session ID: ${fixSessionId}
Batch ID: ${batch.batch_id}
Batch Findings: ${batch.findings.length}

Findings:
${JSON.stringify(batch.findings, null, 2)}

Project Context:
- Structure: ${projectStructure}
- Test Framework: ${testFramework}
- Git Status: ${gitStatus}

## Output Requirements

### 1. partial-plan-${batch.batch_id}.json
Generate partial execution plan with structure:
{
  "batch_id": ${batch.batch_id},
  "groups": [...],  // Groups created from batch findings (use local IDs: G1, G2, ...)
  "timeline": [...],  // Local timeline for this batch only
  "metadata": {
    "findings_count": ${batch.findings.length},
    "groups_count": N,
    "created_at": "ISO-8601-timestamp"
  }
}

**Key Generation Rules**:
- **Groups**: Create groups with local IDs (G1, G2, ...) using intelligent grouping (file+dimension+root cause)
- **Timeline**: Define stages for this batch only (local dependencies within batch)
- **Progress Files**: DO NOT generate fix-progress-*.json here (orchestrator handles after aggregation)

## Analysis Requirements

### Intelligent Grouping Strategy
Group findings using these criteria (in priority order):

1. **File Proximity**: Findings in same file or related files
2. **Dimension Affinity**: Same dimension (security, performance, etc.)
3. **Root Cause Similarity**: Similar underlying issues
4. **Fix Approach Commonality**: Can be fixed with similar approach

**Grouping Guidelines**:
- Optimal group size: 2-5 findings per group
- Avoid cross-cutting concerns in same group
- Consider test isolation (different test suites → different groups)
- Balance workload across groups for parallel execution

### Execution Strategy Determination (Local Only)

**Parallel Mode**: Use when groups are independent, no shared files
**Serial Mode**: Use when groups have dependencies or shared resources
**Hybrid Mode**: Use for mixed dependency graphs (recommended for most cases)

**Dependency Analysis**:
- Identify shared files between groups
- Detect test dependency chains
- Evaluate risk of concurrent modifications

### Risk Assessment

For each group, evaluate:
- **Complexity**: Based on code structure, file size, existing tests
- **Impact Scope**: Number of files affected, API surface changes
- **Rollback Feasibility**: Ease of reverting changes if tests fail

### Test Strategy

For each group, determine:
- **Test Pattern**: Glob pattern matching affected tests
- **Pass Criteria**: All tests must pass (100% pass rate)
- **Test Command**: Infer from project (package.json, pytest.ini, etc.)

## Output Files

Write to ${sessionDir}:
- ./partial-plan-${batch.batch_id}.json

## Quality Checklist

Before finalizing outputs:
- All batch findings assigned to exactly one group
- Group dependencies (within batch) correctly identified
- Timeline stages respect local dependencies
- Test patterns are valid and specific
- Risk assessments are realistic
  `
})

Output

• Files: partial-plan-{batch-id}.json (per agent), fix-plan.json (aggregated), fix-progress-*.json (initialized)
• TaskUpdate: Mark Phase 7 completed, Phase 8 in_progress

Next Phase

Return to orchestrator, then auto-continue to Phase 8: Fix Execution.