create-meta-prompts

<objective> Create prompts optimized for Claude-to-Claude communication in multi-stage workflows. Outputs are structured with XML and metadata for efficient parsing by subsequent prompts.

Every execution produces a SUMMARY.md for quick human scanning without reading full outputs.

Each prompt gets its own folder in .prompts/ with its output artifacts, enabling clear provenance and chain detection. </objective>

<quick_start> <workflow>

1. Intake: Determine purpose (Do/Plan/Research/Refine), gather requirements
2. Chain detection: Check for existing research/plan files to reference
3. Generate: Create prompt using purpose-specific patterns
4. Save: Create folder in .prompts/{number}-{topic}-{purpose}/
5. Present: Show decision tree for running
6. Execute: Run prompt(s) with dependency-aware execution engine
7. Summarize: Create SUMMARY.md for human scanning </workflow>

<folder_structure>

.prompts/
├── 001-auth-research/
│   ├── completed/
│   │   └── 001-auth-research.md    # Prompt (archived after run)
│   ├── auth-research.md            # Full output (XML for Claude)
│   └── SUMMARY.md                  # Executive summary (markdown for human)
├── 002-auth-plan/
│   ├── completed/
│   │   └── 002-auth-plan.md
│   ├── auth-plan.md
│   └── SUMMARY.md
├── 003-auth-implement/
│   ├── completed/
│   │   └── 003-auth-implement.md
│   └── SUMMARY.md                  # Do prompts create code elsewhere
├── 004-auth-research-refine/
│   ├── completed/
│   │   └── 004-auth-research-refine.md
│   ├── archive/
│   │   └── auth-research-v1.md     # Previous version
│   └── SUMMARY.md

</folder_structure> </quick_start>

<context> Prompts directory: !`[ -d ./.prompts ] && echo "exists" || echo "missing"` Existing research/plans: !`find ./.prompts -name "*-research.md" -o -name "*-plan.md" 2>/dev/null | head -10` Next prompt number: !`ls -d ./.prompts/*/ 2>/dev/null | wc -l | xargs -I {} expr {} + 1` </context>

<automated_workflow>

<step_0_intake_gate>

<title>Adaptive Requirements Gathering</title>

<critical_first_action> BEFORE analyzing anything, check if context was provided.

IF no context provided (skill invoked without description): → IMMEDIATELY use AskUserQuestion with:

• header: "Purpose"
• question: "What is the purpose of this prompt?"
• options:
  • "Do" - Execute a task, produce an artifact
  • "Plan" - Create an approach, roadmap, or strategy
  • "Research" - Gather information or understand something
  • "Refine" - Improve an existing research or plan output

After selection, ask: "Describe what you want to accomplish" (they select "Other" to provide free text).

IF context was provided: → Check if purpose is inferable from keywords:

• implement, build, create, fix, add, refactor → Do
• plan, roadmap, approach, strategy, decide, phases → Plan
• research, understand, learn, gather, analyze, explore → Research
• refine, improve, deepen, expand, iterate, update → Refine

→ If unclear, ask the Purpose question above as first contextual question → If clear, proceed to adaptive_analysis with inferred purpose </critical_first_action>

<adaptive_analysis> Extract and infer:

• Purpose: Do, Plan, Research, or Refine
• Topic identifier: Kebab-case identifier for file naming (e.g., auth, stripe-payments)
• Complexity: Simple vs complex (affects prompt depth)
• Prompt structure: Single vs multiple prompts
• Target (Refine only): Which existing output to improve

If topic identifier not obvious, ask:

• header: "Topic"
• question: "What topic/feature is this for? (used for file naming)"
• Let user provide via "Other" option
• Enforce kebab-case (convert spaces/underscores to hyphens)

For Refine purpose, also identify target output from .prompts/*/ to improve. </adaptive_analysis>

<chain_detection> Scan .prompts/*/ for existing *-research.md and *-plan.md files.

If found:

1. List them: "Found existing files: auth-research.md (in 001-auth-research/), stripe-plan.md (in 005-stripe-plan/)"
2. Use AskUserQuestion:
   • header: "Reference"
   • question: "Should this prompt reference any existing research or plans?"
   • options: List found files + "None"
   • multiSelect: true

Match by topic keyword when possible (e.g., "auth plan" → suggest auth-research.md). </chain_detection>

<contextual_questioning> Generate 2-4 questions using AskUserQuestion based on purpose and gaps.

Load questions from: references/question-bank.md

Route by purpose:

• Do → artifact type, scope, approach
• Plan → plan purpose, format, constraints
• Research → depth, sources, output format
• Refine → target selection, feedback, preservation </contextual_questioning>

<decision_gate> After receiving answers, present decision gate using AskUserQuestion:

• header: "Ready"
• question: "Ready to create the prompt?"
• options:
  • "Proceed" - Create the prompt with current context
  • "Ask more questions" - I have more details to clarify
  • "Let me add context" - I want to provide additional information

Loop until "Proceed" selected. </decision_gate>

<finalization> After "Proceed" selected, state confirmation:

"Creating a {purpose} prompt for: {topic} Folder: .prompts/{number}-{topic}-{purpose}/ References: {list any chained files}"

Then proceed to generation. </finalization> </step_0_intake_gate>

<step_1_generate>

<title>Generate Prompt</title>

Load purpose-specific patterns:

• Do: references/do-patterns.md
• Plan: references/plan-patterns.md
• Research: references/research-patterns.md
• Refine: references/refine-patterns.md

Load intelligence rules: references/intelligence-rules.md

<prompt_structure> All generated prompts include:

1. Objective: What to accomplish, why it matters
2. Context: Referenced files (@), dynamic context (!)
3. Requirements: Specific instructions for the task
4. Output specification: Where to save, what structure
5. Metadata requirements: For research/plan outputs, specify XML metadata structure
6. SUMMARY.md requirement: All prompts must create a SUMMARY.md file
7. Success criteria: How to know it worked

For Research and Plan prompts, output must include:

• <confidence> - How confident in findings
• <dependencies> - What's needed to proceed
• <open_questions> - What remains uncertain
• <assumptions> - What was assumed

All prompts must create SUMMARY.md with:

• One-liner - Substantive description of outcome
• Version - v1 or iteration info
• Key Findings - Actionable takeaways
• Files Created - (Do prompts only)
• Decisions Needed - What requires user input
• Blockers - External impediments
• Next Step - Concrete forward action </prompt_structure>

<file_creation>

1. Create folder: .prompts/{number}-{topic}-{purpose}/
2. Create completed/ subfolder
3. Write prompt to: .prompts/{number}-{topic}-{purpose}/{number}-{topic}-{purpose}.md
4. Prompt instructs output to: .prompts/{number}-{topic}-{purpose}/{topic}-{purpose}.md </file_creation> </step_1_generate>

<step_2_present>

<title>Present Decision Tree</title>

After saving prompt(s), present inline (not AskUserQuestion):

<single_prompt_presentation>

Prompt created: .prompts/{number}-{topic}-{purpose}/{number}-{topic}-{purpose}.md

What's next?

1. Run prompt now
2. Review/edit prompt first
3. Save for later
4. Other

Choose (1-4): _

</single_prompt_presentation>

<multi_prompt_presentation>

Prompts created:
- .prompts/001-auth-research/001-auth-research.md
- .prompts/002-auth-plan/002-auth-plan.md
- .prompts/003-auth-implement/003-auth-implement.md

Detected execution order: Sequential (002 references 001 output, 003 references 002 output)

What's next?

1. Run all prompts (sequential)
2. Review/edit prompts first
3. Save for later
4. Other

Choose (1-4): _

</multi_prompt_presentation> </step_2_present>

<step_3_execute>

<title>Execution Engine</title>

<execution_modes> <single_prompt> Straightforward execution of one prompt.

1. Read prompt file contents
2. Spawn Task agent with subagent_type="general-purpose"
3. Include in task prompt:
   • The complete prompt contents
   • Output location: .prompts/{number}-{topic}-{purpose}/{topic}-{purpose}.md
4. Wait for completion
5. Validate output (see validation section)
6. Archive prompt to completed/ subfolder
7. Report results with next-step options </single_prompt>

<sequential_execution> For chained prompts where each depends on previous output.

1. Build execution queue from dependency order
2. For each prompt in queue: a. Read prompt file b. Spawn Task agent c. Wait for completion d. Validate output e. If validation fails → stop, report failure, offer recovery options f. If success → archive prompt, continue to next
3. Report consolidated results

<progress_reporting> Show progress during execution:

Executing 1/3: 001-auth-research... ✓
Executing 2/3: 002-auth-plan... ✓
Executing 3/3: 003-auth-implement... (running)

</progress_reporting> </sequential_execution>

<parallel_execution> For independent prompts with no dependencies.

1. Read all prompt files
2. CRITICAL: Spawn ALL Task agents in a SINGLE message
   • This is required for true parallel execution
   • Each task includes its output location
3. Wait for all to complete
4. Validate all outputs
5. Archive all prompts
6. Report consolidated results (successes and failures)

<failure_handling> Unlike sequential, parallel continues even if some fail:

• Collect all results
• Archive successful prompts
• Report failures with details
• Offer to retry failed prompts </failure_handling> </parallel_execution>

<mixed_dependencies> For complex DAGs (e.g., two parallel research → one plan).

1. Analyze dependency graph

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