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npx skills add mhylle/claude-skills-collection --skill "prompt-generator"
Install specific skill from multi-skill repository
# Description
Generate implementation prompts for phase-based project execution using an orchestration pattern. Use when users request prompts for implementing project phases, need orchestration prompts for multi-step implementations, ask for "/prompt", "generate prompt", "create implementation prompt", or want to create structured implementation instructions for subagent delegation. Outputs the prompt to chat and saves to docs/prompts folder.
# SKILL.md
name: prompt-generator
description: Generate implementation prompts for phase-based project execution using an orchestration pattern. Use when users request prompts for implementing project phases, need orchestration prompts for multi-step implementations, ask for "/prompt", "generate prompt", "create implementation prompt", or want to create structured implementation instructions for subagent delegation. Outputs the prompt to chat and saves to docs/prompts folder.
Prompt Generator
Generate structured implementation prompts for phase-based project execution using an orchestrator/subagent pattern.
Quick Start
When triggered, gather these inputs from the user:
| Variable | Description | Example |
|---|---|---|
PHASE_NUMBER |
Phase identifier | "1", "2", "3" |
PHASE_NAME |
Descriptive phase name | "Foundation", "Data Pipeline" |
PHASE_DOC_PATH |
Path to phase plan document | /project/docs/plans/01-foundation.md |
PROJECT_ROOT |
Project root directory | /home/user/myproject |
GENERAL_PLAN_PATH |
Path to general plan (optional) | /project/docs/plans/00-general-plan.md |
ADR_PATH |
Path to ADR directory (optional) | /project/docs/decisions/ |
ADR Integration: The generated prompt will instruct the orchestrator to:
- Read any ADRs referenced in the plan before implementation
- Create new ADRs when architectural decisions arise during implementation
- Update the plan with ADR references when deviating from the original design
Workflow
1. Gather User Input
Ask the user for required variables. If context provides these values, confirm them:
To generate the implementation prompt, I need:
1. Phase number (e.g., 1, 2, 3)
2. Phase name (e.g., "Foundation", "Data Pipeline")
3. Path to the phase document
4. Project root directory
5. Path to general plan (optional)
2. Generate the Prompt
Read the template from references/implementation-prompt-template.md and substitute all {PLACEHOLDER} values with user-provided inputs.
3. Output and Save
- Display: Output the complete generated prompt in the chat
- Save: Use
scripts/save_prompt.pyto save to<PROJECT_ROOT>/docs/prompts/
echo "<generated_prompt>" | python scripts/save_prompt.py <project_root> <phase_number> <phase_name>
The file is saved as: docs/prompts/phase-<N>-<name>.md
Template Features
The generated prompt includes:
- Orchestration requirements: Main session coordinates, subagents implement
- Delegation patterns: File creation, testing, infrastructure, integration
- Progress tracking: TodoWrite integration for task management
- Validation workflow: Group validation and final success criteria checks
- Error handling: Failure analysis, rollback procedures, escalation guidance
- Handoff preparation: Documentation for next phase transition
- Plan updates: Update implementation plan with actual outcomes upon completion
Subagent Patterns Reference
| Task Type | Pattern | Concurrency |
|---|---|---|
| File creation (independent) | Parallel | High (5-7) |
| File creation (dependent) | Sequential | Low (1-2) |
| Testing/Validation | Per test suite | Medium (3-5) |
| Docker/Infrastructure | Per service | Medium (3-4) |
| Integration | Per integration point | Low (2-3) |
Output Format
The generated prompt follows this structure:
Implement Phase {N}: {Name} of the trading platform...
IMPORTANT ORCHESTRATION REQUIREMENTS:
1. This is an ORCHESTRATION SESSION...
2. Use the implement-plan skill...
3. Spawn SUBAGENTS for all implementation...
4. Track progress using TodoWrite...
5. Coordinate subagents and validate...
## Implementation Strategy
[Phase overview, orchestration workflow, delegation patterns]
## Error Handling
[Failure analysis, rollback, escalation]
## Success Criteria Validation
[Automated and manual checks]
## Handoff to Next Phase
[Update implementation plan, completion documentation, context prep, clean state]
Integration with implement-plan/implement-phase
Generated prompts are automatically discovered and used by the implementation skills:
Workflow
1. prompt-generator creates: docs/prompts/phase-2-data-pipeline.md
β
2. implement-plan discovers prompt via Glob("docs/prompts/phase-*.md")
β
3. implement-plan passes prompt path to implement-phase
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4. implement-phase uses prompt for detailed orchestration instructions
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5. On completion, implement-phase archives to: docs/prompts/completed/
Naming Convention
Prompts must follow this naming pattern for auto-discovery:
docs/prompts/phase-<N>-<name>.md
Examples:
docs/prompts/phase-1-foundation.md β Phase 1
docs/prompts/phase-2-data-pipeline.md β Phase 2
docs/prompts/phase-3-agent-system.md β Phase 3
Directory Structure
docs/prompts/
βββ phase-1-foundation.md # Pending - ready for implementation
βββ phase-2-data-pipeline.md # Pending - ready for implementation
βββ phase-3-agent-system.md # Pending - ready for implementation
βββ completed/ # Archived after successful completion
βββ phase-1-foundation.md # Completed
βββ phase-2-data-pipeline.md # Completed
Benefits
- Pre-planning: Generate all prompts upfront before implementation
- Consistency: Same orchestration patterns across all phases
- Tracking: Completed folder shows implementation progress
- Review: Archived prompts document what instructions were used
Resources
references/
implementation-prompt-template.md: Full prompt template with all placeholders and patterns
scripts/
save_prompt.py: Saves generated prompts todocs/prompts/with metadata header
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.