From brad-personal
Create detailed implementation plans with thorough research and iteration. Use when starting significant features spanning multiple files, planning refactors affecting architecture, working on multi-phase projects with milestones, establishing success criteria before coding, breaking down complex work, or documenting approach for non-trivial technical decisions.
How this skill is triggered — by the user, by Claude, or both
Slash command
/brad-personal:create-planThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- Read all mentioned files completely (no partial reads)
Codebase Research:
codebase-locator - Find WHERE files/components livecodebase-analyzer - Understand HOW existing code workscodebase-pattern-finder - Find similar implementations to learn fromAgent Tips:
Wait for ALL sub-agents, then:
Present outline for approval:
## Overview
[1-2 sentence summary]
## Implementation Phases:
1. [Phase Name] - [What this accomplishes]
2. [Phase Name] - [What this accomplishes]
3. [Phase Name] - [What this accomplishes]
Get explicit user buy-in before investing in detailed planning.
Save to: thoughts/shared/plans/YYYY-MM-DD-brief-description.md (if thoughts/ exists)
Or present inline if no thoughts directory.
Structure:
Per phase:
End with:
humanlayer thoughts sync (if applicable)Automated: Tests, type check, lint, build
Manual: Feature works, performance acceptable, edge cases handled
Database Changes: Schema → Store/Repository methods → Business logic → API endpoints → Client code
New Features: Research patterns → Data model → Backend implementation → API → UI/Frontend
Refactoring: Document current state → Incremental changes → Maintain backwards compatibility → Migration strategy
API Changes: Document current behavior → Deprecation plan → New implementation → Migration guide → Old code removal
npx claudepluginhub carterbs/agent-config --plugin brad-personalGenerates detailed implementation plans for features, refactors, migrations, bug fixes, and architectural changes using multi-agent collaboration. Outputs structured Markdown files with steps, scope, and risks.
Transforms research findings into actionable implementation plans with granular steps, verification criteria, and stakes-based enforcement. Useful for structuring complex coding tasks before execution.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.