Claude-Code-Workflow/.claude/commands/issue/plan.md

name, description, argument-hint, allowed-tools

name	description	argument-hint	allowed-tools
plan	Batch plan issue resolution using issue-plan-agent (explore + plan closed-loop)	--all-pending <issue-id>[,<issue-id>,...] [--batch-size 3]	TodoWrite(*), Task(*), SlashCommand(*), AskUserQuestion(*), Bash(*), Read(*), Write(*)

Issue Plan Command (/issue:plan)

Overview

Unified planning command using issue-plan-agent that combines exploration and planning into a single closed-loop workflow.

Behavior:

• Single solution per issue → auto-bind
• Multiple solutions → return for user selection
• Agent handles file generation

Core Guidelines

⚠️ Data Access Principle: Issues and solutions files can grow very large. To avoid context overflow:

Operation	Correct	Incorrect
List issues (brief)	ccw issue list --status pending --brief	Read('issues.jsonl')
Read issue details	ccw issue status <id> --json	Read('issues.jsonl')
Update status	ccw issue update <id> --status ...	Direct file edit
Bind solution	ccw issue bind <id> <sol-id>	Direct file edit

Output Options:

• --brief: JSON with minimal fields (id, title, status, priority, tags)
• --json: Full JSON (agent use only)

Orchestration vs Execution:

• Command (orchestrator): Use --brief for minimal context
• Agent (executor): Fetch full details → ccw issue status <id> --json

ALWAYS use CLI commands for CRUD operations. NEVER read entire issues.jsonl or solutions/*.jsonl directly.

Usage

/issue:plan [<issue-id>[,<issue-id>,...]] [FLAGS]

# Examples
/issue:plan                           # Default: --all-pending
/issue:plan GH-123                    # Single issue
/issue:plan GH-123,GH-124,GH-125      # Batch (up to 3)
/issue:plan --all-pending             # All pending issues (explicit)

# Flags
--batch-size <n>      Max issues per agent batch (default: 3)

Execution Process

Phase 1: Issue Loading
   ├─ Parse input (single, comma-separated, or --all-pending)
   ├─ Fetch issue metadata (ID, title, tags)
   ├─ Validate issues exist (create if needed)
   └─ Group by similarity (shared tags or title keywords, max 3 per batch)

Phase 2: Unified Explore + Plan (issue-plan-agent)
   ├─ Launch issue-plan-agent per batch
   ├─ Agent performs:
   │   ├─ ACE semantic search for each issue
   │   ├─ Codebase exploration (files, patterns, dependencies)
   │   ├─ Solution generation with task breakdown
   │   └─ Conflict detection across issues
   └─ Output: solution JSON per issue

Phase 3: Solution Registration & Binding
   ├─ Append solutions to solutions/{issue-id}.jsonl
   ├─ Single solution per issue → auto-bind
   ├─ Multiple candidates → AskUserQuestion to select
   └─ Update issues.jsonl with bound_solution_id

Phase 4: Summary
   ├─ Display bound solutions
   ├─ Show task counts per issue
   └─ Display next steps (/issue:queue)

Implementation

Phase 1: Issue Loading (Brief Info Only)

const batchSize = flags.batchSize || 3;
let issues = [];  // {id, title, tags} - brief info for grouping only

// Default to --all-pending if no input provided
const useAllPending = flags.allPending || !userInput || userInput.trim() === '';

if (useAllPending) {
  // Get pending issues with brief metadata via CLI
  const result = Bash(`ccw issue list --status pending,registered --json`).trim();
  const parsed = result ? JSON.parse(result) : [];
  issues = parsed.map(i => ({ id: i.id, title: i.title || '', tags: i.tags || [] }));

  if (issues.length === 0) {
    console.log('No pending issues found.');
    return;
  }
  console.log(`Found ${issues.length} pending issues`);
} else {
  // Parse comma-separated issue IDs, fetch brief metadata
  const ids = userInput.includes(',')
    ? userInput.split(',').map(s => s.trim())
    : [userInput.trim()];

  for (const id of ids) {
    Bash(`ccw issue init ${id} --title "Issue ${id}" 2>/dev/null || true`);
    const info = Bash(`ccw issue status ${id} --json`).trim();
    const parsed = info ? JSON.parse(info) : {};
    issues.push({ id, title: parsed.title || '', tags: parsed.tags || [] });
  }
}
// Note: Agent fetches full issue content via `ccw issue status <id> --json`

// Semantic grouping via Gemini CLI (max 4 issues per group)
async function groupBySimilarityGemini(issues) {
  const issueSummaries = issues.map(i => ({
    id: i.id, title: i.title, tags: i.tags
  }));

  const prompt = `
PURPOSE: Group similar issues by semantic similarity for batch processing; maximize within-group coherence; max 4 issues per group
TASK: • Analyze issue titles/tags semantically • Identify functional/architectural clusters • Assign each issue to one group
MODE: analysis
CONTEXT: Issue metadata only
EXPECTED: JSON with groups array, each containing max 4 issue_ids, theme, rationale
RULES: $(cat ~/.claude/workflows/cli-templates/protocols/analysis-protocol.md) | Each issue in exactly one group | Max 4 issues per group | Balance group sizes

INPUT:
${JSON.stringify(issueSummaries, null, 2)}

OUTPUT FORMAT:
{"groups":[{"group_id":1,"theme":"...","issue_ids":["..."],"rationale":"..."}],"ungrouped":[]}
`;

  const taskId = Bash({
    command: `ccw cli -p "${prompt}" --tool gemini --mode analysis`,
    run_in_background: true, timeout: 600000
  });
  const output = TaskOutput({ task_id: taskId, block: true });

  // Extract JSON from potential markdown code blocks
  function extractJsonFromMarkdown(text) {
    const jsonMatch = text.match(/```json\s*\n([\s\S]*?)\n```/) ||
                      text.match(/```\s*\n([\s\S]*?)\n```/);
    return jsonMatch ? jsonMatch[1] : text;
  }

  const result = JSON.parse(extractJsonFromMarkdown(output));
  return result.groups.map(g => g.issue_ids.map(id => issues.find(i => i.id === id)));
}

const batches = await groupBySimilarityGemini(issues);
console.log(`Processing ${issues.length} issues in ${batches.length} batch(es) (max 4 issues/agent)`);

TodoWrite({
  todos: batches.map((_, i) => ({
    content: `Plan batch ${i+1}`,
    status: 'pending',
    activeForm: `Planning batch ${i+1}`
  }))
});

Phase 2: Unified Explore + Plan (issue-plan-agent) - PARALLEL

Bash(`mkdir -p .workflow/issues/solutions`);
const pendingSelections = [];  // Collect multi-solution issues for user selection
const agentResults = [];       // Collect all agent results for conflict aggregation

// Build prompts for all batches
const agentTasks = batches.map((batch, batchIndex) => {
  const issueList = batch.map(i => `- ${i.id}: ${i.title}${i.tags.length ? ` [${i.tags.join(', ')}]` : ''}`).join('\n');
  const batchIds = batch.map(i => i.id);

  const issuePrompt = `
## Plan Issues

**Issues** (grouped by similarity):
${issueList}

**Project Root**: ${process.cwd()}

### Project Context (MANDATORY)
1. Read: .workflow/project-tech.json (technology stack, architecture)
2. Read: .workflow/project-guidelines.json (constraints and conventions)

### Workflow
1. Fetch issue details: ccw issue status <id> --json
2. Load project context files
3. Explore codebase (ACE semantic search)
4. Plan solution with tasks (schema: solution-schema.json)
5. **If github_url exists**: Add final task to comment on GitHub issue
6. Write solution to: .workflow/issues/solutions/{issue-id}.jsonl
7. Single solution → auto-bind; Multiple → return for selection

### Rules
- Solution ID format: SOL-{issue-id}-{uid} (uid: 4 random alphanumeric chars, e.g., a7x9)
- Single solution per issue → auto-bind via ccw issue bind
- Multiple solutions → register only, return pending_selection
- Tasks must have quantified acceptance.criteria

### Return Summary
{"bound":[{"issue_id":"...","solution_id":"...","task_count":N}],"pending_selection":[{"issue_id":"...","solutions":[{"id":"...","description":"...","task_count":N}]}]}
`;

  return { batchIndex, batchIds, issuePrompt, batch };
});

// Launch agents in parallel (max 10 concurrent)
const MAX_PARALLEL = 10;
for (let i = 0; i < agentTasks.length; i += MAX_PARALLEL) {
  const chunk = agentTasks.slice(i, i + MAX_PARALLEL);
  const taskIds = [];

  // Launch chunk in parallel
  for (const { batchIndex, batchIds, issuePrompt, batch } of chunk) {
    updateTodo(`Plan batch ${batchIndex + 1}`, 'in_progress');
    const taskId = Task(
      subagent_type="issue-plan-agent",
      run_in_background=true,
      description=`Explore & plan ${batch.length} issues: ${batchIds.join(', ')}`,
      prompt=issuePrompt
    );
    taskIds.push({ taskId, batchIndex });
  }

  console.log(`Launched ${taskIds.length} agents (batch ${i/MAX_PARALLEL + 1}/${Math.ceil(agentTasks.length/MAX_PARALLEL)})...`);

  // Collect results from this chunk
  for (const { taskId, batchIndex } of taskIds) {
    const result = TaskOutput(task_id=taskId, block=true);

    // Extract JSON from potential markdown code blocks (agent may wrap in ```json...```)
    const jsonText = extractJsonFromMarkdown(result);
    let summary;
    try {
      summary = JSON.parse(jsonText);
    } catch (e) {
      console.log(`⚠ Batch ${batchIndex + 1}: Failed to parse agent result, skipping`);
      updateTodo(`Plan batch ${batchIndex + 1}`, 'completed');
      continue;
    }
    agentResults.push(summary);  // Store for Phase 3 conflict aggregation

    for (const item of summary.bound || []) {
      console.log(`✓ ${item.issue_id}: ${item.solution_id} (${item.task_count} tasks)`);
    }
    // Collect and notify pending selections
    for (const pending of summary.pending_selection || []) {
      console.log(`⏳ ${pending.issue_id}: ${pending.solutions.length} solutions → awaiting selection`);
      pendingSelections.push(pending);
    }
    if (summary.conflicts?.length > 0) {
      console.log(`⚠ Conflicts: ${summary.conflicts.length} detected (will resolve in Phase 3)`);
    }
    updateTodo(`Plan batch ${batchIndex + 1}`, 'completed');
  }
}

Phase 3: Conflict Resolution & Solution Selection

Conflict Handling:

• Collect conflicts from all agent results
• Low/Medium severity → auto-resolve with recommended_resolution
• High severity → use AskUserQuestion to let user choose resolution

Multi-Solution Selection:

• If pending_selection contains issues with multiple solutions:
  • Use AskUserQuestion to present options (solution ID + task count + description)
  • Extract selected solution ID from user response
  • Verify solution file exists, recover from payload if missing
  • Bind selected solution via ccw issue bind <issue-id> <solution-id>

Phase 4: Summary

// Count planned issues via CLI
const planned = JSON.parse(Bash(`ccw issue list --status planned --brief`) || '[]');
const plannedCount = planned.length;

console.log(`
## Done: ${issues.length} issues → ${plannedCount} planned

Next: \`/issue:queue\` → \`/issue:execute\`
`);

Error Handling

Error	Resolution
Issue not found	Auto-create in issues.jsonl
ACE search fails	Agent falls back to ripgrep
No solutions generated	Display error, suggest manual planning
User cancels selection	Skip issue, continue with others
File conflicts	Agent detects and suggests resolution order

Bash Compatibility

Avoid: $(cmd), $var, for loops — will be escaped incorrectly

Use: Simple commands + && chains, quote comma params "pending,registered"

Quality Checklist

Before completing, verify:

• All input issues have solutions in solutions/{issue-id}.jsonl
• Single solution issues are auto-bound (bound_solution_id set)
• Multi-solution issues returned in pending_selection for user choice
• Each solution has executable tasks with modification_points
• Task acceptance criteria are quantified (not vague)
• Conflicts detected and reported (if multiple issues touch same files)
• Issue status updated to planned after binding

Related Commands

• /issue:queue - Form execution queue from bound solutions
• ccw issue list - List all issues
• ccw issue status - View issue and solution details