home / skills / git-fg / thecattoolkit / generating-prompts
This skill generates optimized Claude-to-Claude meta-prompts with research, planning, and execution stages to support multi-step workflows.
npx playbooks add skill git-fg/thecattoolkit --skill generating-promptsReview the files below or copy the command above to add this skill to your agents.
---
name: generating-prompts
description: "Creates optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce structured outputs for other prompts to consume, or when running multi-stage workflows. Do not use for simple prompts, single-step tasks, or basic conversational AI."
allowed-tools: [Read, Write, Edit, Glob, Grep, Bash]
---
# Create Meta-Prompts
## Quick Start
### Workflow
1. **Intake** - Determine purpose (Do/Plan/Research/Refine)
2. **Generate** - Create prompt using purpose-specific templates
3. **Execute** - Run prompt with dependency-aware execution
4. **Validate** - Verify output and create summary
### Directory Structure
```
.prompts/
├── 001-topic-research/
│ ├── 001-topic-research.md # Prompt
│ ├── topic-research.md # Output
│ └── SUMMARY.md # Human summary
├── 002-topic-plan/
│ ├── 002-topic-plan.md
│ ├── topic-plan.md
│ └── SUMMARY.md
```
## Usage
### Create Research Prompt
```
/create-meta-prompt research authentication options for the app
```
**What it does:**
- Determines this is a Research task
- Scans for existing research files to reference
- Creates prompt with research-specific structure
- Saves to `.prompts/{number}-{topic}-research/`
- Executes with validation
### Create Planning Prompt
```
/create-meta-prompt plan the auth implementation approach
```
**What it does:**
- Detects existing auth-research.md
- Asks if it should reference the research
- Creates plan prompt with research context
- Runs after research completes
### Create Execution Prompt
```
/create-meta-prompt implement JWT authentication
```
**What it does:**
- References both research and plan
- Creates implementation prompt
- Includes verification steps
## Prompt Types
### Research Prompts
For gathering information with structured output.
**Structure:**
```xml
<objective>
Research {topic} to inform {purpose}
</objective>
<context>
{Background and requirements}
</context>
<output>
Save to: .prompts/{num}-{topic}-research/{topic}-research.md
Include: findings, recommendations, metadata
Create: SUMMARY.md
</output>
```
### Plan Prompts
For creating approaches and strategies.
**Structure:**
```xml
<objective>
Create implementation plan for {topic}
</objective>
<context>
Research: @.prompts/{num}-{topic}-research/{topic}-research.md
</context>
<output>
Save to: .prompts/{num}-{topic}-plan/{topic}-plan.md
Include: phases, tasks, dependencies
Create: SUMMARY.md
</output>
```
### Do Prompts
For executing tasks and producing artifacts.
**Structure:**
```xml
<objective>
{What to build/create/fix}
</objective>
<context>
Plan: @.prompts/{num}-{topic}-plan/{topic}-plan.md
</context>
<output>
Create/modify: specified files
Verify: tests and checks
Create: SUMMARY.md
</output>
```
## Execution Engine
### Single Prompt
Straightforward execution of one prompt:
1. Create folder: `.prompts/{number}-{topic}-{purpose}/`
2. Write prompt to: `{number}-{topic}-{purpose}.md`
3. Execute with Task agent
4. Validate output
5. Archive prompt
### Sequential Execution
For chained prompts:
1. Build execution queue from dependencies
2. Execute each prompt in order
3. Validate after each completion
4. Stop on failure
5. Report results
### Parallel Execution
For independent prompts:
1. Spawn all Task agents in single message
2. Wait for completion
3. Validate all outputs
4. Report consolidated results
## Chain Detection
Automatically detects dependencies:
- Scans for existing `*-research.md` and `*-plan.md`
- Matches by topic keyword
- Suggests relevant files to reference
- Determines execution order
## Validation
After execution, verifies:
- Output file created and not empty
- Required XML metadata present
- SUMMARY.md created with all sections
- One-liner is substantive
## Summary Format
Every execution creates `SUMMARY.md`:
```markdown
# {Topic} {Purpose} Summary
**{Substantive one-liner describing outcome}**
## Key Findings
- {Finding 1}
- {Finding 2}
## Files Created
- `path/file.ext` - Description
## Decisions Needed
{Specific items or "None"}
## Next Step
{Concrete forward action}
```
## References
**Templates:**
- [references/research-patterns.md](references/research-patterns.md) - Research prompt templates
- [references/plan-patterns.md](references/plan-patterns.md) - Planning prompt templates
- [references/do-patterns.md](references/do-patterns.md) - Execution prompt templates
**Supporting:**
- [references/metadata-guidelines.md](references/metadata-guidelines.md) - Metadata structure
- [references/question-bank.md](references/question-bank.md) - Intake questions
- [references/summary-template.md](references/summary-template.md) - Summary format
## Success Criteria
**Prompt Creation:**
- Purpose identified correctly
- Chain detection performed
- Prompt structure matches purpose
- Output location specified
- SUMMARY.md requirement included
**Execution:**
- Dependencies correctly ordered
- Output validated
- SUMMARY.md created
- Results presented clearly
This skill creates optimized meta-prompts for Claude-to-Claude pipelines, guiding research, planning, and execution stages. It builds structured prompts, manages dependencies across multi-stage workflows, and validates outputs with human-friendly summaries. Use it when you need repeatable, dependency-aware prompt chains rather than one-off requests.
The skill inspects existing .prompts folders to detect research and plan artifacts, then generates purpose-specific prompt files (research, plan, do) and saves them to numbered topic folders. It can queue prompts for sequential or parallel execution, run task agents, validate outputs (file presence, metadata, SUMMARY.md), and produce a concise SUMMARY.md describing findings and next steps.
Can I use this for simple single-step prompts?
No. This skill is designed for multi-stage, dependency-aware pipelines. Use regular prompting for single-step or basic conversational tasks.
How does chain detection work?
It scans .prompts for {topic}-research.md and {topic}-plan.md, matches by topic keywords, suggests references, and orders execution based on detected dependencies.