Pi Workflow Orchestration

This skill provides Pi's structured approach to task management, quality assurance, and continuous self-improvement.

Core Workflows

1. Plan Node Default

Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions):

• Write detailed specs upfront to reduce ambiguity
• If something goes sideways, STOP and re-plan immediately—don't keep pushing
• Use plan mode for verification steps, not just building

2. Subagent Strategy

• Use subagents liberally to keep main context window clean
• Offload research, exploration, and parallel analysis to subagents
• For complex problems, throw more compute at it via subagents
• One tack per subagent for focused execution

3. Self-Improvement Loop

• After ANY correction from the user: update tasks/lessons.md with metadata (Priority, Status, Area, Pattern-Key)
• Log command failures to tasks/errors.md for diagnosis patterns
• Log feature requests to tasks/feature_requests.md for future work
• Write rules for yourself that prevent the same mistake
• Ruthlessly iterate on these lessons until mistake rate drops
• Review lessons at session start for relevant projects
• Track recurring patterns with Recurrence-Count (bump priority at ≥3 occurrences)

4. Verification Before Done

• Never mark a task complete without proving it works
• Diff behavior between main and your changes when relevant
• Ask yourself: "Would a staff engineer approve this?"
• Run tests, check logs, demonstrate correctness

5. Demand Elegance (Balanced)

• For non-trivial changes: pause and ask "is there a more elegant way?"
• If a fix feels hacky: "Knowing everything I know now, implement the elegant solution"
• Skip this for simple, obvious fixes—don't over-engineer
• Challenge your own work before presenting it

6. Autonomous Bug Fixing

• When given a bug report: just fix it. Don't ask for hand-holding
• Point at logs, errors, failing tests—then resolve them
• Zero context switching required from the user
• Go fix failing CI tests without being told how

Task Management

1. Plan First: Write plan to tasks/todo.md with checkable items
2. Verify Plan: Check in before starting implementation
3. Track Progress: Mark items complete as you go
4. Explain Changes: High-level summary at each step
5. Document Results: Add review section to tasks/todo.md
6. Capture Lessons: Update tasks/lessons.md after corrections

File Organization

• tasks/todo.md — active sprint (current project)
• tasks/lessons.md — corrections, insights, best practices (structured)
• tasks/errors.md — command failures, API errors, exceptions (NEW)
• tasks/feature_requests.md — missing capabilities, feature requests (NEW)
• memory/YYYY-MM-DD.md — session logs (daily)
• MEMORY.md — your curated memories (maintained by user)

See WORKFLOW_ORCHESTRATION.md for detailed reference.

See LESSONS.md for philosophy and framing.

See PHASE1-PHASE2-ENHANCED-LESSONS.md for structured lesson format and file separation.

See LESSONS_UPDATE_GUIDE.md for syncing lessons from workspace to skill.

Capturing Lessons

Lessons Format (Phase 1+2 Enhanced)

Each lesson gets structured metadata for filtering and recurring pattern detection:

## [LRN-YYYYMMDD-XXX] rule_name (category)

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending | in_progress | resolved | promoted
**Area**: backend | infra | tests | docs | config
**Pattern-Key**: category.pattern_name (optional, for recurring detection)

### Summary
One-line description

### Details
Full context and examples

### Applied to
Projects or files where this was used

### Metadata
- Source: correction | insight | user_feedback
- Related Files: path/to/file
- Tags: tag1, tag2
- See Also: LRN-20250225-001 (if related to existing entry)
- Recurrence-Count: 1 (increment if you see it again)
- First-Seen: 2025-02-23
- Last-Seen: 2025-02-23

Errors & Features (NEW)

Log failures and feature gaps separately for better organization:

Errors (tasks/errors.md):

• Command failures, API errors, exceptions
• Include reproducibility, environment, suggested fix

Features (tasks/feature_requests.md):

• Missing capabilities, things you wish existed
• Include complexity estimate and suggested implementation

Syncing to Skill

Periodically merge workspace lessons into the published skill:

# From openclaw-workflow repo
python3 scripts/sync_lessons.py --workspace ~/.openclaw/workspace

# Dry run (preview changes)
python3 scripts/sync_lessons.py --workspace ~/.openclaw/workspace --dry-run

This merges workspace lessons into references/lessons.md for version control and sharing.

Hooks (Optional)

Enable automatic bootstrap reminders for self-improvement:

openclaw hooks enable pi-workflow

This injects a reminder at session start showing:

• When to log lessons/errors/features
• Format and metadata fields
• Recurring pattern detection
• Promotion paths

See hooks/openclaw/HOOK.md for details.

Core Principles

• Simplicity First: Make every change as simple as possible. Minimal code impact.
• No Laziness: Find root causes. No temporary fixes. Senior developer standards.
• Minimal Impact: Changes should only touch what's necessary. Avoid introducing bugs.