spawn-planning-agents
SKILL.md
Spawn Planning Agents
Use specialized agents to gather context needed for creating detailed implementation plans.
Planning Research Needs
When planning implementations, you need:
- Existing code patterns - How is similar functionality implemented?
- Integration points - What files will need to change?
- Test patterns - How should tests be written?
- Documentation - What prior decisions or research exists?
Agent Selection for Planning
Find what exists:
codebase-locator- Find files related to the feature areathoughts-locator- Find existing plans, research, or tickets
Understand patterns:
codebase-pattern-finder- Find similar features to model aftercodebase-analyzer- Understand existing architecture
Extract insights:
thoughts-analyzer- Extract key decisions from prior research
Planning Workflow
-
Context gathering (parallel):
- Locate existing code in the feature area
- Find similar implementations to model after
- Check for prior research or decisions
-
Pattern analysis (parallel, after context):
- Analyze architecture of similar features
- Understand integration points
-
Plan creation:
- Use findings to inform phases
- Reference specific files and patterns
- Include concrete examples from codebase
Example
Planning task: "Add email notifications feature"
Step 1 - Context (parallel):
Task(subagent_type="codebase-locator", prompt="Find all notification-related code")
Task(subagent_type="codebase-pattern-finder", prompt="Find examples of background job handlers and email sending")
Task(subagent_type="thoughts-locator", prompt="Find any research about notification systems")
Step 2 - Analysis (after context):
Task(subagent_type="codebase-analyzer", prompt="Analyze how the existing SMS notification system works, including job queuing and template rendering")
Step 3 - Plan with specifics: Reference the patterns found and create phases that follow existing conventions.
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eveld/claudeGitHub Stars
4
First Seen
Feb 28, 2026
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