Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add glittercowboy/taches-cc-resources --skill "create-meta-prompts"
Install specific skill from multi-skill repository
# Description
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
# SKILL.md
name: create-meta-prompts
description: Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
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.
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
.prompts/
βββ 001-auth-research/
β βββ completed/
β β βββ 001-auth-research.md # Prompt (archived after run)
β βββ auth-research.md # Full output (XML for Claude)
β βββ SUMMARY.md # Executive summary (markdown for human)
βββ 002-auth-plan/
β βββ completed/
β β βββ 002-auth-plan.md
β βββ auth-plan.md
β βββ SUMMARY.md
βββ 003-auth-implement/
β βββ completed/
β β βββ 003-auth-implement.md
β βββ SUMMARY.md # Do prompts create code elsewhere
βββ 004-auth-research-refine/
β βββ completed/
β β βββ 004-auth-research-refine.md
β βββ archive/
β β βββ auth-research-v1.md # Previous version
β βββ SUMMARY.md
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
BEFORE analyzing anything, check if context was provided.
IF no context provided (skill invoked without description):
β 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:
β Check if purpose is inferable from keywords:
- implement, build, create, fix, add, refactor β Do
- plan, roadmap, approach, strategy, decide, phases β Plan
- research, understand, learn, gather, analyze, explore β Research
- refine, improve, deepen, expand, iterate, update β Refine
β If unclear, ask the Purpose question above as first contextual question
β If clear, proceed to adaptive_analysis with inferred purpose
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.
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" β suggest auth-research.md).
Generate 2-4 questions using AskUserQuestion based on purpose and gaps.
Load questions from: references/question-bank.md
Route by purpose:
- Do β artifact type, scope, approach
- Plan β plan purpose, format, constraints
- Research β depth, sources, output format
- Refine β target selection, feedback, preservation
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.
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.
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
All generated prompts include:
- Objective: What to accomplish, why it matters
- Context: Referenced files (@), dynamic context (!)
- Requirements: Specific instructions for the task
- Output specification: Where to save, what structure
- Metadata requirements: For research/plan outputs, specify XML metadata structure
- SUMMARY.md requirement: All prompts must create a SUMMARY.md file
- 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
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
After saving prompt(s), present inline (not AskUserQuestion):
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): _
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): _
Straightforward execution of one prompt.
- Read prompt file contents
- Spawn Task agent with subagent_type="general-purpose"
- Include in task prompt:
- The complete prompt contents
- Output location:
.prompts/{number}-{topic}-{purpose}/{topic}-{purpose}.md - Wait for completion
- Validate output (see validation section)
- Archive prompt to
completed/subfolder - Report results with next-step options
For chained prompts where each depends on previous output.
- Build execution queue from dependency order
- For each prompt in queue:
a. Read prompt file
b. Spawn Task agent
c. Wait for completion
d. Validate output
e. If validation fails β stop, report failure, offer recovery options
f. If success β archive prompt, continue to next - Report consolidated results
Show progress during execution:
Executing 1/3: 001-auth-research... β
Executing 2/3: 002-auth-plan... β
Executing 3/3: 003-auth-implement... (running)
For independent prompts with no dependencies.
- Read all prompt files
- CRITICAL: Spawn ALL Task agents in a SINGLE message
- This is required for true parallel execution
- Each task includes its output location
- Wait for all to complete
- Validate all outputs
- Archive all prompts
- Report consolidated results (successes and failures)
Unlike sequential, parallel continues even if some fail:
- Collect all results
- Archive successful prompts
- Report failures with details
- Offer to retry failed prompts
For complex DAGs (e.g., two parallel research β one plan).
- Analyze dependency graph from @ references
- Group into execution layers:
- Layer 1: No dependencies (run parallel)
- Layer 2: Depends only on layer 1 (run after layer 1 completes)
- Layer 3: Depends on layer 2, etc.
- Execute each layer:
- Parallel within layer
- Sequential between layers
- Stop if any dependency fails (downstream prompts can't run)
Layer 1 (parallel): 001-api-research, 002-db-research
Layer 2 (after layer 1): 003-architecture-plan
Layer 3 (after layer 2): 004-implement
Scan prompt contents for @ references to determine dependencies:
- Parse each prompt for
@.prompts/{number}-{topic}/patterns - Build dependency graph
- Detect cycles (error if found)
- Determine execution order
If no explicit @ references found, infer from purpose:
- Research prompts: No dependencies (can parallel)
- Plan prompts: Depend on same-topic research
- Do prompts: Depend on same-topic plan
Override with explicit references when present.
If a prompt references output that doesn't exist:
- Check if it's another prompt in this session (will be created)
- Check if it exists in
.prompts/*/(already completed) - If truly missing:
- Warn user: "002-auth-plan references auth-research.md which doesn't exist"
- Offer: Create the missing research prompt first? / Continue anyway? / Cancel?
After each prompt completes, verify success:
- File exists: Check output file was created
- Not empty: File has content (> 100 chars)
- Metadata present (for research/plan): Check for required XML tags
<confidence><dependencies><open_questions><assumptions>- SUMMARY.md exists: Check SUMMARY.md was created
- SUMMARY.md complete: Has required sections (Key Findings, Decisions Needed, Blockers, Next Step)
- One-liner is substantive: Not generic like "Research completed"
If validation fails:
- Report what's missing
- Offer options:
- Retry the prompt
- Continue anyway (for non-critical issues)
- Stop and investigate
Stop the chain immediately:
β Failed at 2/3: 002-auth-plan
Completed:
- 001-auth-research β (archived)
Failed:
- 002-auth-plan: Output file not created
Not started:
- 003-auth-implement
What's next?
1. Retry 002-auth-plan
2. View error details
3. Stop here (keep completed work)
4. Other
Continue others, report all results:
Parallel execution completed with errors:
β 001-api-research (archived)
β 002-db-research: Validation failed - missing <confidence> tag
β 003-ui-research (archived)
What's next?
1. Retry failed prompt (002)
2. View error details
3. Continue without 002
4. Other
- Sequential: Archive each prompt immediately after successful completion
- Provides clear state if execution stops mid-chain
- Parallel: Archive all at end after collecting results
- Keeps prompts available for potential retry
Move prompt file to completed subfolder:
mv .prompts/{number}-{topic}-{purpose}/{number}-{topic}-{purpose}.md \
.prompts/{number}-{topic}-{purpose}/completed/
Output file stays in place (not moved).
β Executed: 001-auth-research
β Created: .prompts/001-auth-research/SUMMARY.md
βββββββββββββββββββββββββββββββββββββββββββββββββ
# Auth Research Summary
**JWT with jose library and httpOnly cookies recommended**
## Key Findings
β’ jose outperforms jsonwebtoken with better TypeScript support
β’ httpOnly cookies required (localStorage is XSS vulnerable)
β’ Refresh rotation is OWASP standard
## Decisions Needed
None - ready for planning
## Blockers
None
## Next Step
Create auth-plan.md
βββββββββββββββββββββββββββββββββββββββββββββββββ
What's next?
1. Create planning prompt (auth-plan)
2. View full research output
3. Done
4. Other
Display the actual SUMMARY.md content inline so user sees findings without opening files.
β Chain completed: auth workflow
Results:
βββββββββββββββββββββββββββββββββββββββββββββββββ
001-auth-research
**JWT with jose library and httpOnly cookies recommended**
Decisions: None β’ Blockers: None
002-auth-plan
**4-phase implementation: types β JWT core β refresh β tests**
Decisions: Approve 15-min token expiry β’ Blockers: None
003-auth-implement
**JWT middleware complete with 6 files created**
Decisions: Review before Phase 2 β’ Blockers: None
βββββββββββββββββββββββββββββββββββββββββββββββββ
All prompts archived. Full summaries in .prompts/*/SUMMARY.md
What's next?
1. Review implementation
2. Run tests
3. Create new prompt chain
4. Other
For chains, show condensed one-liner from each SUMMARY.md with decisions/blockers flagged.
If user wants to re-run an already-completed prompt:
- Check if prompt is in
completed/subfolder - Move it back to parent folder
- Optionally backup existing output:
{output}.bak - Execute normally
If output file already exists:
- For re-runs: Backup existing β
{filename}.bak - For new runs: Should not happen (unique numbering)
- If conflict detected: Ask user - Overwrite? / Rename? / Cancel?
After successful execution:
- Do NOT auto-commit (user controls git workflow)
- Mention what files were created/modified
- User can commit when ready
Exception: If user explicitly requests commit, stage and commit:
- Output files created
- Prompts archived
- Any implementation changes (for Do prompts)
If a prompt's output includes instructions to create more prompts:
- This is advanced usage - don't auto-detect
- Present the output to user
- User can invoke skill again to create follow-up prompts
- Maintains user control over prompt creation
Prompt patterns by purpose:
- references/do-patterns.md - Execution prompts + output structure
- references/plan-patterns.md - Planning prompts + plan.md structure
- references/research-patterns.md - Research prompts + research.md structure
- references/refine-patterns.md - Iteration prompts + versioning
Shared templates:
- references/summary-template.md - SUMMARY.md structure and field requirements
- references/metadata-guidelines.md - Confidence, dependencies, open questions, assumptions
Supporting references:
- references/question-bank.md - Intake questions by purpose
- references/intelligence-rules.md - Extended thinking, parallel tools, depth decisions
Prompt Creation:
- Intake gate completed with purpose and topic identified
- Chain detection performed, relevant files referenced
- Prompt generated with correct structure for purpose
- Folder created in .prompts/ with correct naming
- Output file location specified in prompt
- SUMMARY.md requirement included in prompt
- Metadata requirements included for Research/Plan outputs
- Quality controls included for Research outputs (verification checklist, QA, pre-submission)
- Streaming write instructions included for Research outputs
- Decision tree presented
Execution (if user chooses to run):
- Dependencies correctly detected and ordered
- Prompts executed in correct order (sequential/parallel/mixed)
- Output validated after each completion
- SUMMARY.md created with all required sections
- One-liner is substantive (not generic)
- Failed prompts handled gracefully with recovery options
- Successful prompts archived to completed/ subfolder
- SUMMARY.md displayed inline in results
- Results presented with decisions/blockers flagged
Research Quality (for Research prompts):
- Verification checklist completed
- Quality report distinguishes verified from assumed claims
- Sources consulted listed with URLs
- Confidence levels assigned to findings
- Critical claims verified with official documentation
# 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.