Pi Workflow Orchestration

Pi Workflow Orchestration

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

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

Plan First: Write plan to tasks/todo.md with checkable items Verify Plan: Check in before starting implementation Track Progress: Mark items complete as you go Explain Changes: High-level summary at each step Document Results: Add review section to tasks/todo.md 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.

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.