refactor: optimize useWebSocket hook by consolidating store references and improving handler stability

Author: catlog22

.claude/commands/workflow/review-cycle-fix.md

@@ -1,7 +1,7 @@
 ---
 name: review-cycle-fix
 description: Automated fixing of code review findings with AI-powered planning and coordinated execution. Uses intelligent grouping, multi-stage timeline coordination, and test-driven verification.
-argument-hint: "<export-file|review-dir> [--resume] [--max-iterations=N]"
+argument-hint: "<export-file|review-dir> [--resume] [--max-iterations=N] [--batch-size=N]"
 allowed-tools: Skill(*), TodoWrite(*), Read(*), Bash(*), Task(*), Edit(*), Write(*)
 ---
 
@@ -21,37 +21,45 @@ allowed-tools: Skill(*), TodoWrite(*), Read(*), Bash(*), Task(*), Edit(*), Write
 
 # Custom max retry attempts per finding
 /workflow:review-cycle-fix .workflow/active/WFS-123/.review/ --max-iterations=5
+
+# Custom batch size for parallel planning (default: 5 findings per batch)
+/workflow:review-cycle-fix .workflow/active/WFS-123/.review/ --batch-size=3
 ```
 
 **Fix Source**: Exported findings from review cycle dashboard
 **Output Directory**: `{review-dir}/fixes/{fix-session-id}/` (within session .review/)
 **Default Max Iterations**: 3 (per finding, adjustable)
+**Default Batch Size**: 5 (findings per planning batch, adjustable)
+**Max Parallel Agents**: 10 (concurrent planning agents)
 **CLI Tools**: @cli-planning-agent (planning), @cli-execute-agent (fixing)
 
 ## What & Why
 
 ### Core Concept
-Automated fix orchestrator with **two-phase architecture**: AI-powered planning followed by coordinated parallel/serial execution. Generates fix timeline with intelligent grouping and dependency analysis, then executes fixes with conservative test verification.
+Automated fix orchestrator with **parallel planning architecture**: Multiple AI agents analyze findings concurrently in batches, then coordinate parallel/serial execution. Generates fix timeline with intelligent grouping and dependency analysis, executes fixes with conservative test verification.
 
 **Fix Process**:
-- **Planning Phase**: AI analyzes findings, generates fix plan with grouping and execution strategy
-- **Execution Phase**: Main orchestrator coordinates agents per timeline stages
+- **Batching Phase (1.5)**: Orchestrator groups findings by file+dimension similarity, creates batches
+- **Planning Phase (2)**: Up to 10 agents plan batches in parallel, generate partial plans, orchestrator aggregates
+- **Execution Phase (3)**: Main orchestrator coordinates agents per aggregated timeline stages
+- **Parallel Efficiency**: Customizable batch size (default: 5), MAX_PARALLEL=10 agents
 - **No rigid structure**: Adapts to task requirements, not bound to fixed JSON format
 
 **vs Manual Fixing**:
 - **Manual**: Developer reviews findings one-by-one, fixes sequentially
-- **Automated**: AI groups related issues, executes in optimal parallel/serial order with automatic test verification
+- **Automated**: AI groups related issues, multiple agents plan in parallel, executes in optimal parallel/serial order with automatic test verification
 
 ### Value Proposition
-1. **Intelligent Planning**: AI-powered analysis identifies optimal grouping and execution strategy
-2. **Multi-stage Coordination**: Supports complex parallel + serial execution with dependency management
-3. **Conservative Safety**: Mandatory test verification with automatic rollback on failure
-4. **Resume Support**: Checkpoint-based recovery for interrupted sessions
+1. **Parallel Planning**: Multiple agents analyze findings concurrently, reducing planning time for large batches (10+ findings)
+2. **Intelligent Batching**: Semantic similarity grouping ensures related findings are analyzed together
+3. **Multi-stage Coordination**: Supports complex parallel + serial execution with cross-batch dependency management
+4. **Conservative Safety**: Mandatory test verification with automatic rollback on failure
+5. **Resume Support**: Checkpoint-based recovery for interrupted sessions
 
 ### Orchestrator Boundary (CRITICAL)
 - **ONLY command** for automated review finding fixes
-- Manages: Planning phase coordination, stage-based execution, agent scheduling, progress tracking
-- Delegates: Fix planning to @cli-planning-agent, fix execution to @cli-execute-agent
+- Manages: Intelligent batching (Phase 1.5), parallel planning coordination (launch N agents), plan aggregation, stage-based execution, agent scheduling, progress tracking
+- Delegates: Batch planning to @cli-planning-agent, fix execution to @cli-execute-agent
 
 
 ### Execution Flow
@@ -60,12 +68,22 @@ Automated fix orchestrator with **two-phase architecture**: AI-powered planning
 Phase 1: Discovery & Initialization
    └─ Validate export file, create fix session structure, initialize state files
 
-Phase 2: Planning Coordination (@cli-planning-agent)
-   ├─ Analyze findings for patterns and dependencies
-   ├─ Group by file + dimension + root cause similarity
-   ├─ Determine execution strategy (parallel/serial/hybrid)
-   ├─ Generate fix timeline with stages
-   └─ Output: fix-plan.json
+Phase 1.5: Intelligent Grouping & Batching
+   ├─ Analyze findings metadata (file, dimension, severity)
+   ├─ Group by semantic similarity (file proximity + dimension affinity)
+   ├─ Create batches respecting --batch-size (default: 5)
+   └─ Output: Finding batches for parallel planning
+
+Phase 2: Parallel Planning Coordination (@cli-planning-agent × N)
+   ├─ Launch MAX_PARALLEL planning agents concurrently (default: 10)
+   ├─ Each agent processes one batch:
+   │  ├─ Analyze findings for patterns and dependencies
+   │  ├─ Group by file + dimension + root cause similarity
+   │  ├─ Determine execution strategy (parallel/serial/hybrid)
+   │  ├─ Generate fix timeline with stages
+   │  └─ Output: partial-plan-{batch-id}.json
+   ├─ Collect results from all agents
+   └─ Aggregate: Merge partial plans → fix-plan.json (resolve cross-batch dependencies)
 
 Phase 3: Execution Orchestration (Stage-based)
    For each timeline stage:
@@ -91,25 +109,29 @@ Phase 5: Session Completion (Optional)
 
 | Agent | Responsibility |
 |-------|---------------|
-| **Orchestrator** | Input validation, session management, planning coordination, stage-based execution scheduling, progress tracking, aggregation |
-| **@cli-planning-agent** | Findings analysis, intelligent grouping (file+dimension+root cause), execution strategy determination (parallel/serial/hybrid), timeline generation with dependency mapping |
+| **Orchestrator** | Input validation, session management, intelligent batching (Phase 1.5), parallel planning coordination (launch N agents), plan aggregation (merge partial plans, resolve cross-batch dependencies), stage-based execution scheduling, progress tracking, result aggregation |
+| **@cli-planning-agent** | Batch findings analysis, intelligent grouping (file+dimension+root cause), execution strategy determination (parallel/serial/hybrid), timeline generation with dependency mapping, partial plan output |
 | **@cli-execute-agent** | Fix execution per group, code context analysis, Edit tool operations, test verification, git rollback on failure, completion JSON generation |
 
 ## Enhanced Features
 
-### 1. Two-Phase Architecture
+### 1. Parallel Planning Architecture
 
-**Phase Separation**:
+**Batch Processing Strategy**:
 
-| Phase | Agent | Output | Purpose |
-|-------|-------|--------|---------|
-| **Planning** | @cli-planning-agent | fix-plan.json | Analyze findings, group intelligently, determine optimal execution strategy |
-| **Execution** | @cli-execute-agent | completions/*.json | Execute fixes per plan with test verification and rollback |
+| Phase | Agent Count | Input | Output | Purpose |
+|-------|-------------|-------|--------|---------|  
+| **Batching (1.5)** | Orchestrator | All findings | Finding batches | Semantic grouping by file+dimension, respecting --batch-size |
+| **Planning (2)** | N agents (≤10) | 1 batch each | partial-plan-{batch-id}.json | Analyze batch in parallel, generate execution groups and timeline |
+| **Aggregation (2)** | Orchestrator | All partial plans | fix-plan.json | Merge timelines, resolve cross-batch dependencies |
+| **Execution (3)** | M agents (dynamic) | 1 group each | fix-progress-{N}.json | Execute fixes per aggregated plan with test verification |
 
 **Benefits**:
-- Clear separation of concerns (analysis vs execution)
-- Reusable plans (can re-execute without re-planning)
-- Better error isolation (planning failures vs execution failures)
+- **Speed**: N agents plan concurrently, reducing planning time for large batches
+- **Scalability**: MAX_PARALLEL=10 prevents resource exhaustion
+- **Flexibility**: Batch size customizable via --batch-size (default: 5)
+- **Isolation**: Each planning agent focuses on related findings (semantic grouping)
+- **Reusable**: Aggregated plan can be re-executed without re-planning
 
 ### 2. Intelligent Grouping Strategy
 
@@ -197,12 +219,30 @@ if (result.passRate < 100%) {
 - Session creation: Generate fix-session-id (`fix-{timestamp}`)
 - Directory structure: Create `{review-dir}/fixes/{fix-session-id}/` with subdirectories
 - State files: Initialize active-fix-session.json (session marker)
-- TodoWrite initialization: Set up 4-phase tracking
-
-**Phase 2: Planning Coordination**
-- Launch @cli-planning-agent with findings data and project context
-- Validate fix-plan.json output (schema conformance, includes metadata with session status)
-- Load plan into memory for execution phase
+- TodoWrite initialization: Set up 5-phase tracking (including Phase 1.5)
+
+**Phase 1.5: Intelligent Grouping & Batching**
+- Load all findings metadata (id, file, dimension, severity, title)
+- Semantic similarity analysis:
+  - Primary: Group by file proximity (same file or related modules)
+  - Secondary: Group by dimension affinity (same review dimension)
+  - Tertiary: Analyze title/description similarity (root cause clustering)
+- Create batches respecting --batch-size (default: 5 findings per batch)
+- Balance workload: Distribute high-severity findings across batches
+- Output: Array of finding batches for parallel planning
+
+**Phase 2: Parallel Planning Coordination**
+- Determine concurrency: MIN(batch_count, MAX_PARALLEL=10)
+- For each batch chunk (≤10 batches):
+  - Launch all agents in parallel with run_in_background=true
+  - Pass batch findings + project context + batch_id to each agent
+  - Each agent outputs: partial-plan-{batch-id}.json
+- Collect results via TaskOutput (blocking until all complete)
+- Aggregate partial plans:
+  - Merge execution groups (renumber group_ids sequentially: G1, G2, ...)
+  - Merge timelines (detect cross-batch dependencies, adjust stages)
+  - Resolve conflicts (same file in multiple batches → serialize)
+- Generate final fix-plan.json with aggregated metadata
 - TodoWrite update: Mark planning complete, start execution
 
 **Phase 3: Execution Orchestration**
@@ -236,7 +276,10 @@ if (result.passRate < 100%) {
 .workflow/active/WFS-{session-id}/.review/
 ├── fix-export-{timestamp}.json     # Exported findings (input)
 └── fixes/{fix-session-id}/
-    ├── fix-plan.json               # Planning agent output (execution plan with metadata)
+    ├── partial-plan-1.json         # Batch 1 partial plan (planning agent 1 output)
+    ├── partial-plan-2.json         # Batch 2 partial plan (planning agent 2 output)
+    ├── partial-plan-N.json         # Batch N partial plan (planning agent N output)
+    ├── fix-plan.json               # Aggregated execution plan (orchestrator merges partials)
     ├── fix-progress-1.json         # Group 1 progress (planning agent init → agent updates)
     ├── fix-progress-2.json         # Group 2 progress (planning agent init → agent updates)
     ├── fix-progress-3.json         # Group 3 progress (planning agent init → agent updates)
@@ -246,28 +289,126 @@ if (result.passRate < 100%) {
 ```
 
 **File Producers**:
-- **Planning Agent**: `fix-plan.json` (with metadata), all `fix-progress-*.json` (initial state)
-- **Execution Agents**: Update assigned `fix-progress-{N}.json` in real-time
+- **Orchestrator**: Batches findings (Phase 1.5), aggregates partial plans → `fix-plan.json` (Phase 2), launches parallel planning agents
+- **Planning Agents (N)**: Each outputs `partial-plan-{batch-id}.json` + initializes `fix-progress-*.json` for assigned groups
+- **Execution Agents (M)**: Update assigned `fix-progress-{N}.json` in real-time
 
 
 ### Agent Invocation Template
 
-**Planning Agent**:
+**Phase 1.5: Intelligent Batching** (Orchestrator):
+```javascript
+// Load findings
+const findings = JSON.parse(Read(exportFile));
+const batchSize = flags.batchSize || 5;
+
+// Semantic similarity analysis: group by file+dimension
+const batches = [];
+const grouped = new Map(); // key: "${file}:${dimension}"
+
+for (const finding of findings) {
+  const key = `${finding.file || 'unknown'}:${finding.dimension || 'general'}`;
+  if (!grouped.has(key)) grouped.set(key, []);
+  grouped.get(key).push(finding);
+}
+
+// Create batches respecting batchSize
+for (const [key, group] of grouped) {
+  while (group.length > 0) {
+    const batch = group.splice(0, batchSize);
+    batches.push({
+      batch_id: batches.length + 1,
+      findings: batch,
+      metadata: { primary_file: batch[0].file, primary_dimension: batch[0].dimension }
+    });
+  }
+}
+
+console.log(`Created ${batches.length} batches (${batchSize} findings per batch)`);
+```
+
+**Phase 2: Parallel Planning** (Orchestrator launches N agents):
+```javascript
+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 (Batch Mode - Partial Plan Only)**:
 ```javascript
 Task({
   subagent_type: "cli-planning-agent",
-  description: `Generate fix plan and initialize progress files for ${findings.length} findings`,
+  run_in_background: true,
+  description: `Plan batch ${batch.batch_id}: ${batch.findings.length} findings`,
   prompt: `
 ## Task Objective
-Analyze ${findings.length} code review findings and generate execution plan with intelligent grouping and timeline coordination.
+Analyze code review findings in batch ${batch.batch_id} and generate **partial** execution plan.
 
 ## Input Data
 Review Session: ${reviewId}
 Fix Session ID: ${fixSessionId}
-Total Findings: ${findings.length}
+Batch ID: ${batch.batch_id}
+Batch Findings: ${batch.findings.length}
 
 Findings:
-${JSON.stringify(findings, null, 2)}
+${JSON.stringify(batch.findings, null, 2)}
 
 Project Context:
 - Structure: ${projectStructure}
@@ -276,31 +417,23 @@ Project Context:
 
 ## Output Requirements
 
-### 1. fix-plan.json
-Execute: cat ~/.claude/workflows/cli-templates/fix-plan-template.json
-
-Generate execution plan following template structure:
+### 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**:
-- **Metadata**: Populate fix_session_id, review_session_id, status ("planning"), created_at, started_at timestamps
-- **Execution Strategy**: Choose approach (parallel/serial/hybrid) based on dependency analysis, set parallel_limit and stages count
-- **Groups**: Create groups (G1, G2, ...) with intelligent grouping (see Analysis Requirements below), assign progress files (fix-progress-1.json, ...), populate fix_strategy with approach/complexity/test_pattern, assess risks, identify dependencies
-- **Timeline**: Define stages respecting dependencies, set execution_mode per stage, map groups to stages, calculate critical path
-
-### 2. fix-progress-{N}.json (one per group)
-Execute: cat ~/.claude/workflows/cli-templates/fix-progress-template.json
-
-For each group (G1, G2, G3, ...), generate fix-progress-{N}.json following template structure:
-
-**Initial State Requirements**:
-- Status: "pending", phase: "waiting"
-- Timestamps: Set last_update to now, others null
-- Findings: Populate from review findings with status "pending", all operation fields null
-- Summary: Initialize all counters to zero
-- Flow control: Empty implementation_approach array
-- Errors: Empty array
-
-**CRITICAL**: Ensure complete template structure - all fields must be present.
+- **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
 
@@ -318,7 +451,7 @@ Group findings using these criteria (in priority order):
 - Consider test isolation (different test suites → different groups)
 - Balance workload across groups for parallel execution
 
-### Execution Strategy Determination
+### 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
@@ -346,21 +479,16 @@ For each group, determine:
 ## Output Files
 
 Write to ${sessionDir}:
-- ./fix-plan.json
-- ./fix-progress-1.json
-- ./fix-progress-2.json
-- ./fix-progress-{N}.json (as many as groups created)
+- ./partial-plan-${batch.batch_id}.json
 
 ## Quality Checklist
 
 Before finalizing outputs:
-- ✅ All findings assigned to exactly one group
-- ✅ Group dependencies correctly identified
-- ✅ Timeline stages respect dependencies
-- ✅ All progress files have complete initial structure
+- ✅ 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
-- ✅ Estimated times are reasonable
   `
 })
 ```