/blueprint:prp-create

Create a comprehensive PRP (Product Requirement Prompt) - a self-contained packet with all context an AI agent needs to deliver production code on first attempt.

What is a PRP?

PRD + Curated Codebase Intelligence + Implementation Blueprint + Validation Gates = everything needed for reliable implementation.

Usage

:

/blueprint:prp-create [feature-name]

Prerequisites

:

Blueprint Development initialized (

docs/blueprint/

exists)

Clear understanding of the feature to implement

When to Use This Skill

Use this skill when...

Use alternative when...

Creating new feature implementation packet

Executing an existing PRP (use

/blueprint:prp-execute

)

Want comprehensive research and documentation

Quick prototyping without formal requirements

Planning for AI agent or subagent execution

Solo developer implementing without research

Need to document implementation approach

Implementing based on existing codebase patterns

Context

Blueprint initialized: !

find docs/blueprint -maxdepth 1 -name 'manifest.json' -type f

Last PRP ID: !

jq -r '.id_registry.last_prp // 0' docs/blueprint/manifest.json

ai_docs available: !

find docs/blueprint/ai_docs -type f -name "*.md"

Existing PRDs: !

find docs/prds -name "*.md" -type f

Parameters

Parse

$ARGUMENTS

:

feature-name

(required): Kebab-case name for PRP (e.g.,

auth-oauth2

,

api-rate-limiting

)

Used for filename and document ID generation

Execution

Execute the complete PRP creation workflow:

Step 1: Verify prerequisites and understand requirements

If Blueprint not initialized → Error: "Run

/blueprint:init

first"

Ask user to describe the feature: "What feature needs to be implemented?"

Capture: Goal, why it matters, success criteria

Ask if this implements an existing PRD: "Does this implement an existing PRD or is it standalone?"

If PRD chosen → Store PRD ID for linking

Determine feature type (API endpoint, database, UI, integration, etc.)

Step 2: Research codebase patterns

Use Explore agent to find existing patterns:

Similar features already implemented (identify existing patterns to follow)

Relevant file locations and integration points

Testing patterns used for similar features

Architecture decisions that affect this feature

Store findings with specific file paths and line numbers.

Step 3: Research external documentation

For relevant libraries/frameworks, gather:

Official documentation sections (capture URLs with specific sections)

Known issues and gotchas from Stack Overflow / GitHub discussions

Best practices from documentation

Common implementation patterns

Use WebSearch/WebFetch as needed. Create or update ai_docs entries if needed (see

REFERENCE.md

).

Step 4: Generate PRP document ID and structure

Generate next PRP ID from manifest:

Extract

id_registry.last_prp

from manifest.json

Next ID = last_prp + 1 (format:

PRP-NNN

)

Create

docs/prps/[feature-name].md

with frontmatter and sections (see

REFERENCE.md

).

Step 5: Draft PRP content with research findings

Fill all required sections (see

REFERENCE.md

):

Goal & Why

One-sentence goal, business justification, target users, priority

Success Criteria

Specific, testable acceptance criteria with metrics

Context

:

Documentation references (URLs with specific sections)

ai_docs references (links to curated library docs)

Codebase Intelligence

File paths, code snippets with line numbers, patterns to follow

Known Gotchas

Critical warnings with mitigations

Implementation Blueprint

:

Architecture decision with rationale

Task breakdown (Required / Deferred / Nice-to-Have categories)

Order of implementation

TDD Requirements

Test strategy and critical test cases

Validation Gates

Executable commands (linting, type-checking, tests, coverage)

Critical

All tasks must be explicitly categorized (see REFERENCE.md ). Step 6: Score confidence across dimensions Rate each dimension 1-10: Dimension Criteria Context Completeness Are all file paths, code snippets, and references explicit? Implementation Clarity Is pseudocode clear enough for AI to follow? Gotchas Documented Are all known pitfalls documented with mitigations? Validation Coverage Are all validation gates with executable commands? Calculate overall score as average of dimensions. Target: 7+ for execution, 9+ for subagent delegation. If score < 7 → Return to Steps 2-3 to fill gaps. Step 7: Review and validate completeness Verify checklist (see REFERENCE.md ): Goal is clear and specific Success criteria are testable All file paths are explicit (not "somewhere in...") Code snippets show actual patterns with line references Gotchas include mitigations Validation commands are copy-pasteable Confidence score is honest Update docs/blueprint/manifest.json ID registry with new PRP entry. Step 8: Report PRP and prompt for next action Display summary showing: PRP ID and location Feature summary and approach Context collected (ai_docs, patterns, documentation) Linked documents (source PRD if applicable) Confidence score with breakdown Any gaps if score < 7 If confidence >= 7 , offer user choices: Execute PRP now → /blueprint:prp-execute [feature-name] Create work-order for subagent → /blueprint:work-order Review and refine → Show file location and gaps Done for now → Exit (save for later execution) If confidence < 7 , offer user choices: Research more context → Use Explore agent for gaps Create ai_docs entries → /blueprint:curate-docs Execute anyway (risky) → Proceed with warning Done for now → Save incomplete PRP Agentic Optimizations Context Command Check blueprint init test -f docs/blueprint/manifest.json && echo "YES" || echo "NO" Next PRP ID jq -r '.id_registry.last_prp // 0' docs/blueprint/manifest.json | awk '{print $1+1}' List existing PRPs ls -1 docs/prps/ 2>/dev/null | wc -l Search for patterns Use Explore agent instead of manual grep Fast research Use existing ai_docs rather than fetching docs again For PRP document structure, task categorization, review checklists, and ai_docs creation guidance, see REFERENCE.md .