Agent-Skills.md

Agent Skills: Compound Engineering

Compound Engineering workflow for AI-assisted development. Use when planning features, executing work, reviewing code, or codifying learnings. Follows the Plan -> Work -> Review -> Compound loop where each unit of engineering makes subsequent work easier. Triggers on: plan this feature, implement this, review this code, compound learnings, create implementation plan, systematic development.

UncategorizedID: 48Nauts-Operator/opencode-baseline/compound-engineering

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48Nauts-Operator/opencode-baseline
48Nauts-Operator
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.opencode/skill/compound-engineering/SKILL.md

Skill Metadata

Name
compound-engineering
Description
"Compound Engineering workflow for AI-assisted development. Use when planning features, executing work, reviewing code, or codifying learnings. Follows the Plan -> Work -> Review -> Compound loop where each unit of engineering makes subsequent work easier. Triggers on: plan this feature, implement this, review this code, compound learnings, create implementation plan, systematic development."

Compound Engineering

A development methodology where each unit of work makes subsequent work easier, not harder.

Core Philosophy

Each unit of engineering work should make subsequent units of work easier--not harder.

Traditional development accumulates technical debt. Compound engineering inverts this by creating a learning loop where each bug, failed test, or problem-solving insight gets documented and used by future work.

The Compound Engineering Loop

Plan -> Work -> Review -> Compound -> (repeat)
  1. Plan (40%): Research approaches, synthesize information into detailed implementation plans
  2. Work (20%): Execute the plan systematically with continuous validation
  3. Review (20%): Evaluate output quality and identify learnings
  4. Compound (20%): Feed results back into the system to make the next loop better

80% of compound engineering is in planning and review. 20% is in execution.

Step 1: Plan

Before writing any code, create a comprehensive plan.

Research Phase

  1. Codebase Analysis: Search for similar patterns, conventions, and prior art
  2. Commit History: Use git log to understand how related features were built
  3. Documentation: Check README, AGENTS.md, and inline documentation
  4. External Research: Search for best practices relevant to the problem

Plan Document Structure

# Feature: [Name]

## Context
- What problem does this solve?
- Who is affected?
- What's the current behavior vs desired behavior?

## Research Findings
- Similar patterns found in codebase: [list with file links]
- Relevant prior implementations: [commit references]
- Best practices discovered: [external references]

## Acceptance Criteria
- [ ] Criterion 1 (testable)
- [ ] Criterion 2 (testable)

## Technical Approach
1. Step 1: [specific action]
2. Step 2: [specific action]

## Testing Strategy
- Unit tests: [what to test]
- Integration tests: [what to test]
- Manual verification: [steps]

## Risks & Mitigations
- Risk 1: [mitigation]

Step 2: Work

Execute the plan systematically:

  1. Create isolated environment: Use feature branch or git worktree
  2. Break down into tasks: Create TODO list from plan
  3. Execute systematically: One task at a time
  4. Validate continuously: Run tests after each change
  5. Commit incrementally: Small, focused commits

Quality Checks During Work

npm run typecheck
npm test
npm run lint

Step 3: Review

Review Checklist

Code Quality

  • [ ] Follows existing codebase patterns and conventions
  • [ ] No unnecessary complexity
  • [ ] Clear naming that matches project conventions
  • [ ] No debug code left behind

Security

  • [ ] No secrets or sensitive data exposed
  • [ ] Input validation where needed

Performance

  • [ ] No obvious performance regressions
  • [ ] Database queries are efficient (no N+1)

Testing

  • [ ] Tests cover acceptance criteria
  • [ ] Edge cases considered

Step 4: Compound

Capture learnings to make future work easier:

What to Compound

Patterns: Document new patterns discovered

## Pattern: [Name]
When to use: [context]
Implementation: [example code]
See: [file reference]

Decisions: Record why certain approaches were chosen

## Decision: [Choice Made]
Context: [situation]
Options considered: [alternatives]
Rationale: [why this choice]

Failures: Turn every bug into a lesson

## Lesson: [What Went Wrong]
Symptom: [what was observed]
Root cause: [actual problem]
Fix: [solution]
Prevention: [how to avoid in future]

Where to Codify Learnings

  1. AGENTS.md: Project-wide guidance
  2. Subdirectory AGENTS.md: Specific guidance for subsystems
  3. Inline comments: Only when the code isn't self-explanatory
  4. Test cases: Turn bugs into regression tests

Key Principles

  1. Prefer duplication over wrong abstraction
  2. Document as you go
  3. Quality compounds
  4. Systematic beats heroic
  5. Knowledge should be codified

Success Metrics

You're doing compound engineering well when:

  • Each feature takes less effort than the last similar feature
  • Bugs become one-time events (documented and prevented)
  • New team members can be productive quickly
  • Code reviews surface fewer issues
  • Technical debt decreases over time