YaroslavKomarov/ai-factory.implement

Implement - Execute Task Plan

Execute tasks from the plan, track progress, and enable session continuation.

Workflow

Step 0: Check Current State

FIRST: Determine what state we're in:

1. Check for uncommitted changes (git status)
2. Check for plan files (.ai-factory/PLAN.md or branch-named)
3. Check current branch

If uncommitted changes exist:

You have uncommitted changes. Commit them first?
- [ ] Yes, commit now (/ai-factory.commit)
- [ ] No, stash and continue
- [ ] Cancel

If NO plan file exists (all tasks completed or fresh start):

No active plan found.

Current branch: feature/user-auth

What would you like to do?
- [ ] Start new feature from current branch
- [ ] Return to main/master and start new feature
- [ ] Create quick task plan (no branch)
- [ ] Nothing, just checking status

Based on choice:

• New feature from current → /ai-factory.feature <description>
• Return to main → git checkout main && git pull → /ai-factory.feature <description>
• Quick task → /ai-factory.task <description>

If plan file exists → continue to Step 0.1

Step 0.1: Load Project Context & Past Experience

Read .ai-factory/DESCRIPTION.md if it exists to understand:

• Tech stack (language, framework, database, ORM)
• Project architecture and conventions
• Non-functional requirements

Read all patches from .ai-factory/patches/ if the directory exists:

• Use Glob to find all *.md files in .ai-factory/patches/
• Read each patch to learn from past fixes and mistakes
• Apply lessons learned: avoid patterns that caused bugs, use patterns that prevented them
• Pay attention to Root Cause and Prevention sections — they tell you what NOT to do

Use this context when implementing:

• Follow the specified tech stack
• Use correct import patterns and conventions
• Apply proper error handling and logging as specified
• Avoid pitfalls documented in patches — don't repeat past mistakes

Step 0.1: Find Plan File

Check for plan files in this order:

1. .ai-factory/PLAN.md exists? → Use it (direct /ai-factory.task call)
2. No .ai-factory/PLAN.md → Check current git branch:
   git branch --show-current
   → Look for .ai-factory/features/<branch-name>.md (e.g., .ai-factory/features/feature-user-auth.md)

Priority:

1. .ai-factory/PLAN.md - always takes priority (from direct /ai-factory.task)
2. Branch-named file - if no .ai-factory/PLAN.md (from /ai-factory.feature)

Read the plan file to understand:

• Context and settings (testing, logging preferences)
• Commit checkpoints (when to commit)
• Task dependencies

Step 1: Load Current State

TaskList → Get all tasks with status

Find:

• Next pending task (not blocked, not completed)
• Any in_progress tasks (resume these first)

Step 2: Display Progress

## Implementation Progress

✅ Completed: 3/8 tasks
🔄 In Progress: Task #4 - Implement search service
⏳ Pending: 4 tasks

Current task: #4 - Implement search service

Step 3: Execute Current Task

For each task:

3.1: Fetch full details

TaskGet(taskId) → Get description, files, context

3.2: Mark as in_progress

TaskUpdate(taskId, status: "in_progress")

3.3: Implement the task

• Read relevant files
• Make necessary changes
• Follow existing code patterns
• NO tests unless plan includes test tasks
• NO reports or summaries

3.4: Verify implementation

• Check code compiles/runs
• Verify functionality works
• Fix any immediate issues

3.5: Mark as completed

TaskUpdate(taskId, status: "completed")

3.6: Update checkbox in plan file

IMMEDIATELY after completing a task, update the checkbox in the plan file:

# Before
- [ ] Task 1: Create user model

# After
- [x] Task 1: Create user model

This is MANDATORY — checkboxes must reflect actual progress:

• Use Edit tool to change - [ ] to - [x]
• Do this RIGHT AFTER each task completion
• Even if deletion will be offered later
• Plan file is the source of truth for progress

3.7: Update .ai-factory/DESCRIPTION.md if needed

If during implementation:

• New dependency/library was added
• Tech stack changed (e.g., added Redis, switched ORM)
• New integration added (e.g., Stripe, SendGrid)
• Architecture decision was made

→ Update .ai-factory/DESCRIPTION.md to reflect the change:

## Tech Stack
- **Cache:** Redis (added for session storage)

This keeps .ai-factory/DESCRIPTION.md as the source of truth.

3.8: Check for commit checkpoint

If the plan has commit checkpoints and current task is at a checkpoint:

✅ Tasks 1-4 completed.

This is a commit checkpoint. Ready to commit?
Suggested message: "feat: add base models and types"

- [ ] Yes, commit now (/ai-factory.commit)
- [ ] No, continue to next task
- [ ] Skip all commit checkpoints

3.9: Move to next task or pause

Step 4: Session Persistence

Progress is automatically saved via TaskUpdate.

To pause:

Current progress saved.

Completed: 4/8 tasks
Next task: #5 - Add pagination support

To resume later, run:
/ai-factory.implement

To resume (next session):

/ai-factory.implement

→ Automatically finds next incomplete task

Step 5: Completion

When all tasks are done:

## Implementation Complete

All 8 tasks completed.

Branch: feature/product-search
Plan file: .ai-factory/features/feature-product-search.md
Files modified:
- src/services/search.ts (created)
- src/api/products/search.ts (created)
- src/types/search.ts (created)

What's next?

1. 🔍 /ai-factory.verify — Verify nothing was missed (recommended)
2. 💾 /ai-factory.commit — Commit the changes directly

Context Cleanup

Context is heavy after implementation. All code changes are saved — suggest freeing space:

AskUserQuestion: Free up context before continuing?

Options:
1. /clear — Full reset (recommended)
2. /compact — Compress history
3. Continue as is

Suggest verification:

AskUserQuestion: All tasks complete. Run verification?

Options:
1. Verify first — Run /ai-factory.verify to check completeness (recommended)
2. Skip to commit — Go straight to /ai-factory.commit

If user chooses "Verify first" → suggest invoking /ai-factory.verify. If user chooses "Skip to commit" → suggest invoking /ai-factory.commit.

Check if documentation needs updating:

Read the plan file settings. If documentation preference is set to "yes" (from /ai-factory.feature questions), run /ai-factory.docs to update documentation.

If documentation preference is "no" or not set — skip this step silently.

If documentation preference is "yes":

📝 Updating project documentation...

→ Invoke /ai-factory.docs to analyze changes and update docs.

Handle plan file after completion:

• If .ai-factory/PLAN.md (direct /ai-factory.task, not from /ai-factory.feature):

  Would you like to delete .ai-factory/PLAN.md? (It's no longer needed)
  - [ ] Yes, delete it
  - [ ] No, keep it
  

• If branch-named file (e.g., .ai-factory/features/feature-user-auth.md):

  • Keep it - documents what was done
  • User can delete before merging if desired

Check if running in a git worktree:

Detect worktree context:

# If .git is a file (not a directory), we're in a worktree
[ -f .git ]

If we ARE in a worktree, offer to merge back and clean up:

You're working in a parallel worktree.

  Branch:    <current-branch>
  Worktree:  <current-directory>
  Main repo: <main-repo-path>

Would you like to merge this branch into main and clean up?
- [ ] Yes, merge and clean up (recommended)
- [ ] No, I'll handle it manually

If user chooses "Yes, merge and clean up":

1. Ensure everything is committed — check git status. If uncommitted changes exist, suggest /ai-factory.commit first and wait.

2. Get main repo path:

   MAIN_REPO=$(git rev-parse --git-common-dir | sed 's|/\.git$||')
   BRANCH=$(git branch --show-current)
   

3. Switch to main repo:

   cd "${MAIN_REPO}"
   

4. Merge the branch:

   git checkout main
   git pull origin main
   git merge "${BRANCH}"
   

   If merge conflict occurs:

   ⚠️  Merge conflict detected. Resolve manually:
     cd <main-repo-path>
     git merge --abort   # to cancel
     # or resolve conflicts and git commit
   

   → STOP here, do not proceed with cleanup.

5. Remove worktree and branch (only if merge succeeded):

   git worktree remove <worktree-path>
   git branch -d "${BRANCH}"
   

6. Confirm:

   ✅ Merged and cleaned up!
   
     Branch <branch> merged into main.
     Worktree removed.
   
   You're now in: <main-repo-path> (main)
   

If user chooses "No, I'll handle it manually", show a reminder:

To merge and clean up later:
  cd <main-repo-path>
  git merge <branch>
  /ai-factory.feature --cleanup <branch>

IMPORTANT: NO summary reports, NO analysis documents, NO wrap-up tasks.

Commands

Start/Resume Implementation

/ai-factory.implement

Continues from next incomplete task.

Start from Specific Task

/ai-factory.implement 5

Starts from task #5 (useful for skipping or re-doing).

Check Status Only

/ai-factory.implement status

Shows progress without executing.

Execution Rules

DO:

• ✅ Execute one task at a time
• ✅ Mark tasks in_progress before starting
• ✅ Mark tasks completed after finishing
• ✅ Follow existing code conventions
• ✅ Create files mentioned in task description
• ✅ Handle edge cases mentioned in task
• ✅ Stop and ask if task is unclear

DON'T:

• ❌ Write tests (unless explicitly in task list)
• ❌ Create report files
• ❌ Create summary documents
• ❌ Add tasks not in the plan
• ❌ Skip tasks without user permission
• ❌ Mark incomplete tasks as done

Progress Display Format

┌─────────────────────────────────────────────┐
│ Feature: User Authentication                │
├─────────────────────────────────────────────┤
│ ✅ #1 Create user model                     │
│ ✅ #2 Add registration endpoint             │
│ ✅ #3 Add login endpoint                    │
│ 🔄 #4 Implement JWT generation    ← current │
│ ⏳ #5 Add password reset                    │
│ ⏳ #6 Add email verification                │
├─────────────────────────────────────────────┤
│ Progress: 3/6 (50%)                         │
└─────────────────────────────────────────────┘

Handling Blockers

If a task cannot be completed:

⚠️ Blocker encountered on Task #4

Issue: [Description of the problem]

Options:
1. Skip this task and continue (mark as blocked)
2. Modify the task approach
3. Stop implementation and discuss

What would you like to do?

Session Continuity

Tasks are persisted in the conversation/project state.

Starting new session:

User: /ai-factory.implement

Claude: Resuming implementation...

Found 3 completed tasks, 5 pending.
Continuing from Task #4: Implement JWT generation

[Executes task #4]

Example Full Flow

Session 1:
  /ai-factory.feature Add user authentication
  → Creates branch: feature/user-authentication
  → Asks about tests (No), logging (Verbose)
  → /ai-factory.task creates 6 tasks
  → Saves plan to: .ai-factory/features/feature-user-authentication.md
  → /ai-factory.implement starts
  → Completes tasks #1, #2, #3
  → User ends session

Session 2:
  /ai-factory.implement
  → Detects branch: feature/user-authentication
  → Reads plan: .ai-factory/features/feature-user-authentication.md
  → Loads state: 3/6 complete
  → Continues from task #4
  → Completes tasks #4, #5, #6
  → All done, suggests /ai-factory.commit

Critical Rules

1. NEVER write tests unless task list explicitly includes test tasks
2. NEVER create reports or summary documents after completion
3. ALWAYS mark task in_progress before starting work
4. ALWAYS mark task completed after finishing
5. ALWAYS update checkbox in plan file - - [ ] → - [x] immediately after task completion
6. PRESERVE progress - tasks survive session boundaries
7. ONE task at a time - focus on current task only

CRITICAL: Logging Requirements

ALWAYS add verbose logging when implementing code. AI-generated code often has subtle bugs that are hard to debug without proper logging.

Logging Guidelines

1. Log function entry/exit with parameters and return values
2. Log state changes - before and after mutations
3. Log external calls - API requests, database queries, file operations
4. Log error context - include relevant variables, not just error message
5. Use structured logging when possible (JSON format)

Example Pattern

function processOrder(order: Order): Result {
  console.log('[processOrder] START', { orderId: order.id, items: order.items.length });

  try {
    const validated = validateOrder(order);
    console.log('[processOrder] Validation passed', { validated });

    const result = submitToPayment(validated);
    console.log('[processOrder] Payment result', { success: result.success, transactionId: result.id });

    return result;
  } catch (error) {
    console.error('[processOrder] ERROR', { orderId: order.id, error: error.message, stack: error.stack });
    throw error;
  }
}

Log Management Requirements

Logs must be configurable and manageable:

1. Use log levels - DEBUG, INFO, WARN, ERROR
2. Environment-based control - LOG_LEVEL env variable
3. Easy to disable - single flag or env var to turn off verbose logs
4. Consider rotation - for file-based logs, implement rotation or use existing tools

// Good: Configurable logging
const LOG_LEVEL = process.env.LOG_LEVEL || 'debug';
const logger = createLogger({ level: LOG_LEVEL });

// Good: Can be disabled
if (process.env.DEBUG) {
  console.log('[debug]', data);
}

// Bad: Hardcoded verbose logs that can't be turned off
console.log(hugeObject); // Will pollute production logs

Why This Matters

• AI-generated code may have edge cases not covered
• Logs help identify WHERE things go wrong
• Debugging without logs wastes significant time
• User can remove logs later if needed, but missing logs during development is costly
• Production safety - logs must be reducible to avoid performance issues and storage costs

DO NOT skip logging to "keep code clean" - verbose logging is REQUIRED during implementation, but MUST be configurable.