Files
Claude-Code-Workflow/.claude/commands/workflow/plan.md
catlog22 9a4003deda feat: Add task-specific path management system for precise CLI analysis
- Add 'paths' field to task JSON schema with semicolon-separated concrete paths
- Create read-task-paths.sh script to convert paths to Gemini @ format
- Update all agents to use task-specific paths instead of --all-files
- Integrate get_modules_by_depth.sh for project structure discovery
- Update workflow planning to populate paths field automatically
- Enhance execute command to pass task-specific paths to agents
- Update documentation for new path management system

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-13 11:28:16 +08:00

7.6 KiB

name: plan description: Create implementation plans with intelligent input detection usage: /workflow:plan argument-hint: "text description"|file.md|ISS-001|template-name examples: - /workflow:plan "Build authentication system" - /workflow:plan requirements.md - /workflow:plan ISS-001 - /workflow:plan web-api

Workflow Plan Command (/workflow:plan)

Overview

Creates actionable implementation plans with intelligent input source detection. Supports text, files, issues, and templates through automatic recognition.

Core Principles

File Structure: @~/.claude/workflows/workflow-architecture.md

Usage

/workflow:plan [--AM gemini|codex] <input>

Analysis Method Flag (--AM)

Optional flag to specify which CLI tool to use for context analysis:

  • gemini (default): Uses Gemini CLI for pattern-based analysis and architectural understanding
  • codex: Uses Codex CLI for autonomous development context gathering with intelligent file discovery

Examples:

/workflow:plan --AM codex "Build authentication system"
/workflow:plan --AM gemini requirements.md
/workflow:plan ISS-001  # Uses default (gemini)

Input Detection Logic

The command automatically detects input type:

File Input (Auto-detected)

/workflow:plan requirements.md
/workflow:plan PROJECT_SPEC.txt
/workflow:plan config.json
/workflow:plan spec.yaml

Triggers: Extensions: .md, .txt, .json, .yaml, .yml Processing: Reads file contents and extracts requirements

Issue Input (Auto-detected)

/workflow:plan ISS-001
/workflow:plan ISSUE-123
/workflow:plan feature-request-45

Triggers: Patterns: ISS-, ISSUE-, -request- Processing: Loads issue data and acceptance criteria

Template Input (Auto-detected)

/workflow:plan web-api
/workflow:plan mobile-app
/workflow:plan database-migration
/workflow:plan security-feature

Triggers: Known template names Processing: Loads template and prompts for customization

Text Input (Default)

/workflow:plan "Build user authentication with JWT and OAuth2"
/workflow:plan "Fix performance issues in dashboard"

Triggers: Everything else Processing: Parse natural language requirements

Automatic Behaviors

Session Management

  • Creates new session if none exists
  • Uses active session if available
  • Generates session ID: WFS-[topic-slug]

Complexity Detection

  • Simple: <5 tasks → Direct IMPL_PLAN.md
  • Medium: 5-15 tasks → IMPL_PLAN.md + TODO_LIST.md
  • Complex: >15 tasks → Full decomposition

Task Generation

  • Automatically creates .task/ files when complexity warrants
  • Generates hierarchical task structure (max 3 levels)
  • Updates session state with task references
  • Runs project structure analysis to populate paths field

Implementation Field Requirements

⚠️ CRITICAL: All generated tasks must include detailed implementation guidance

analysis_source Assignment Rules:

  • "manual": User provides complete implementation details (including specific files and code snippets)
  • "gemini": Insufficient information, requires Gemini analysis (missing file paths or code context)
  • "codex": Insufficient information, requires Codex autonomous development analysis (complex refactoring or intelligent file discovery)
  • "auto-detected": System automatically infers implementation details (based on pattern recognition)

Decision Flow:

  1. IF user provides file paths + code snippets → "manual"
  2. ELIF system can infer implementation location → "auto-detected"
  3. ELIF --AM codex specified → "codex" (requires autonomous development analysis)
  4. ELSE → "gemini" (requires deep analysis, default method)

Auto-fill Strategy:

  1. Sufficient Information: Auto-fill implementation field based on user input and project context

  2. Insufficient Information: Mark analysis_source appropriately and prompt:

    For Gemini Analysis (default):

    ⚠️ Implementation details incomplete, recommend using gemini analysis:
    gemini --all-files -p "@{relevant-file-patterns} @{CLAUDE.md} 
    Analyze task: [task description]
    Extract: 1) File locations and code snippets 2) Modification logic and data flow 3) Risks and dependencies"
    

    For Codex Analysis (when --AM codex specified):

    ⚠️ Implementation details incomplete, recommend using codex analysis:
    codex --full-auto exec "Analyze and implement: [task description]
    Context: Autonomous analysis and implementation guidance needed
    Extract: 1) Intelligent file discovery 2) Implementation strategy 3) Autonomous development approach"
    

Required Implementation Sub-fields:

  • files: Must contain at least 1 file with detailed info (path, location, original_code, modifications)
  • context_notes: Dependencies, risks, performance considerations
  • analysis_source: manual|gemini|codex|auto-detected

Paths Field Population Process:

  1. Project Structure Analysis: Run get_modules_by_depth.sh to discover project structure
  2. Relevance Filtering: Match discovered modules to task requirements
  3. Path Selection: Choose concrete directories/files (avoid wildcards)
  4. Format: Semicolon-separated list (e.g., "src/auth;tests/auth;config/auth.json")

Path Selection Strategy:

# Step 1: Analyze project structure
modules = Bash(.claude/scripts/get_modules_by_depth.sh list)

# Step 2: Extract relevant modules based on task scope
relevant_paths = []
for module in modules:
    if matches_task_scope(module.path, task_requirements):
        relevant_paths.append(module.path)

# Step 3: Add specific files mentioned in requirements
specific_files = extract_file_mentions(task_requirements)
relevant_paths.extend(specific_files)

# Step 4: Format as semicolon-separated string
task.paths = join(relevant_paths, ";")

Quality Standards:

  • logic_flow must use specified symbols (───►, ◊───, ◄───)
  • Each file must have specific location (function name or line range)
  • risks array cannot be empty, must contain at least 1 risk assessment

Session Check Process

⚠️ CRITICAL: Check for existing active session before planning

  1. Check Active Session: Check for .workflow/.active-* marker file
  2. Session Selection: Use existing active session or create new
  3. Context Integration: Load session state and existing context

Output Documents

IMPL_PLAN.md (Always Created)

# Implementation Plan - [Project Name]
*Generated from: [input_source]*

## Requirements
[Extracted requirements from input source]

## Task Breakdown
- **IMPL-001**: [Task description]
- **IMPL-002**: [Task description]

## Success Criteria
[Measurable completion conditions]

Optional TODO_LIST.md (Auto-triggered)

Created when complexity > simple or task count > 5

Task JSON Files (Auto-created)

Generated in .task/ directory when decomposition enabled

Error Handling

Input Processing Errors

  • File not found: Clear error message with suggestions
  • Invalid issue: Verify issue ID exists
  • Unknown template: List available templates
  • Empty input: Prompt for valid input

Integration Points

  • /workflow:session:start - Create new session first
  • /context - View generated plan
  • /task/execute - Execute decomposed tasks
  • /workflow:execute - Run implementation phase

Template System

Available templates:

  • web-api: REST API development
  • mobile-app: Mobile application
  • database-migration: Database changes
  • security-feature: Security implementation

System ensures: Unified planning interface with intelligent input detection and automatic complexity handling