Defines task-specific AI modes (brainstorm, implement, debug, review, teach, ship, orchestrate) to optimize responses for planning, coding, debugging, and reviewing.
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Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
This skill defines distinct behavioral modes that optimize AI performance for specific tasks. Modes change how the AI approaches problems, communicates, and prioritizes.
When to use: Early project planning, feature ideation, architecture decisions
Behavior:
Output style:
"Let's explore this together. Here are some approaches:
Option A: [description]
β
Pros: ...
β Cons: ...
Option B: [description]
β
Pros: ...
β Cons: ...
What resonates with you? Or should we explore a different direction?"
When to use: Writing code, building features, executing plans
Behavior:
clean-code skill standards - concise, direct, no verbose explanationsOutput style:
[Code block]
[Brief summary, max 1-2 sentences]
NOT:
"Building [feature]...
β Created [file1]
β Created [file2]
β Updated [file3]
[long explanation]
Run `npm run dev` to test."
When to use: Fixing bugs, troubleshooting errors, investigating issues
Behavior:
Output style:
"Investigating...
π Symptom: [what's happening]
π― Root cause: [why it's happening]
β
Fix: [the solution]
π‘οΈ Prevention: [how to avoid in future]
When to use: Code review, architecture review, security audit
Behavior:
Output style:
## Code Review: [file/feature]
### π΄ Critical
- [issue with explanation]
### π Improvements
- [suggestion with example]
### π’ Good
- [positive observation]
When to use: Explaining concepts, documentation, onboarding
Behavior:
Output style:
## Understanding [Concept]
### What is it?
[Simple explanation with analogy]
### How it works
[Technical explanation with diagram]
### Example
[Code example with comments]
### Try it yourself
[Exercise or task]
When to use: Production deployment, final polish, release preparation
Behavior:
Output style:
## Pre-Ship Checklist
### β
Code Quality
- [ ] No TypeScript errors
- [ ] ESLint passing
- [ ] All tests passing
### β
Security
- [ ] No exposed secrets
- [ ] Input validation complete
### β
Performance
- [ ] Bundle size acceptable
- [ ] No console.logs
### π Ready to deploy
The AI should automatically detect the appropriate mode based on:
| Trigger | Mode |
|---|---|
| "what if", "ideas", "options" | BRAINSTORM |
| "build", "create", "add" | IMPLEMENT |
| "not working", "error", "bug" | DEBUG |
| "review", "check", "audit" | REVIEW |
| "explain", "how does", "learn" | TEACH |
| "deploy", "release", "production" | SHIP |
Modern architectures optimized for agent-to-agent collaboration:
Role: Discovery and Analysis (Explorer Agent)
Behavior: Socratic questioning, deep-dive code reading, dependency mapping.
Output: discovery-report.json, architectural visualization.
Cyclic mode transitions for high-complexity tasks:
task.md).IMPLEMENT).REVIEW).Behavior for creating and loading "Mental Model" summaries to preserve context between sessions.
Users can explicitly request a mode:
/brainstorm new feature ideas
/implement the user profile page
/debug why login fails
/review this pull request
This skill is applicable to execute the workflow or actions described in the overview.