Our review

This skill provides a structured methodology for orchestrating AI agents with upfront planning, subagent delegation, self-improvement through lessons, rigorous verification, and a pursuit of elegance.

Strengths

Reduces ambiguity by requiring a detailed planning phase before execution.
Keeps the main context clean by offloading exploratory tasks to subagents.
Encourages continuous learning by documenting corrections in a lessons file.
Enforces systematic verification (logs, tests, diffs) before marking tasks complete.

Limitations

Can slow down simple tasks with overhead of planning and verification.
Requires disciplined maintenance of task and lesson files.
The elegance demand may lead to over-engineering if not balanced with simplicity.

When to use it

Use this skill for complex projects or non-trivial tasks that benefit from a structured, repeatable approach with AI agents.

When not to use it

Avoid this skill for quick, obvious fixes or when the task is too simple to warrant detailed planning and verification.

Examples

Plan a feature implementation

I need to implement user authentication with JWT. Enter plan mode, write a detailed plan to tasks/todo.md with checkable items, then verify the plan with me before starting.

Use subagents for research

Create an architecture for a microservices app. Use subagents to research the pros and cons of different service mesh tools, then compile the results into a comparison table.

Self-improvement after a mistake

I just corrected you on using the wrong API endpoint. Add a lesson to tasks/lessons.md so you don't make the same mistake again.

Workflow Orchestration

1. Plan Mode Default

Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
If something goes sideways, STOP and re-plan immediately — don't keep pushing
Use plan mode for verification steps, not just building
Write detailed specs upfront to reduce ambiguity

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 task per subagent for focused execution

3. Self-Improvement Loop

After ANY correction from the user: update tasks/lessons.md with the pattern
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 project

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

Core Principles

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

AI Workflow Orchestration

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