Prompt Architect

Transform vague prompts into expert-level, structured prompts using 27 research-backed frameworks across 7 intent categories.

Works with Claude Code, ChatGPT, Gemini CLI, Cursor, GitHub Copilot, Windsurf, OpenAI Codex, and 30+ Agent Skills compatible tools.

Quick Start

npx @ckelsoe/prompt-architect

The interactive installer detects your AI agents (Claude Code, Gemini CLI, Cursor, Copilot, Codex, and more) and lets you choose where to install.

Important: Use npx, not npm install. The npx command runs the interactive multi-agent installer. Running npm install will only install to Claude Code silently via the postinstall hook.

Requires .npmrc with @ckelsoe:registry=https://npm.pkg.github.com and a GitHub token with read:packages scope.

Table of Contents

• Overview
• Key Features
• Example Transformation
• Supported Frameworks
• Quick Start
• Installation
• Usage
• Framework Selection Guide
• Project Structure
• Documentation
• Contributing
• License

Overview

Prompt Architect is an Agent Skills compatible skill that elevates your prompting capabilities through:

• Intelligent Analysis - Evaluates prompts across 5 quality dimensions (clarity, specificity, context, completeness, structure)
• Framework Recommendation - Suggests the best framework(s) for your specific use case with clear reasoning
• Guided Dialogue - Asks targeted clarifying questions to gather missing information progressively
• Systematic Application - Applies selected framework to transform your prompt
• Iterative Refinement - Continues improving based on feedback until perfect

Target Audience:

• Developers using AI coding agents (Claude Code, Gemini CLI, Cursor, Copilot, etc.)
• Prompt engineers optimizing LLM interactions
• AI practitioners seeking systematic prompt improvement
• Teams wanting consistent, high-quality prompts

Key Features

27 Research-Backed Frameworks Across 7 Intent Categories

Quality Scoring System

Every prompt is evaluated across:

• Clarity (1-10): Is the goal clear and unambiguous?
• Specificity (1-10): Are requirements detailed enough?
• Context (1-10): Is background information provided?
• Completeness (1-10): Are all necessary elements present?
• Structure (1-10): Is the prompt well-organized?

Progressive Disclosure

• Asks 3-5 targeted questions at a time (never overwhelming)
• Adapts questions based on framework selection
• Builds understanding iteratively
• Confirms assumptions before proceeding

Continuous Iteration

• Shows before/after comparisons with explanations
• Refines based on your feedback
• Can switch frameworks if initial choice doesn't fit
• Continues until you're satisfied

Example Transformation

Before

"Write about machine learning"

Analysis Scores:

• Clarity: 2/10 (vague goal)
• Specificity: 1/10 (no details)
• Context: 0/10 (no background)
• Completeness: 2/10 (missing most elements)
• Structure: 3/10 (single sentence)
• Overall: 1.6/10

After (CO-STAR Framework)

CONTEXT:
Creating content for a business blog aimed at C-level executives exploring
how AI/ML could benefit their organizations. Readers understand business
strategy but have limited technical ML knowledge. Part of an emerging
technologies series.

OBJECTIVE:
Create an engaging article helping executives understand practical machine
learning applications relevant to their companies. Focus on demonstrating
tangible business value and real-world implementation without overwhelming
technical details.

STYLE:
Professional blog style combining narrative with bullet points. Include 2-3
real-world case studies. Structure with clear subheadings every 150-200 words.
Balance storytelling with concrete information. Avoid jargon; when necessary,
provide plain-language explanations.

TONE:
Professional yet approachable and conversational. Confident and authoritative
without being condescending. Practical and business-focused rather than
theoretical.

AUDIENCE:
C-suite executives and senior managers at mid-to-large enterprises who:
- Make strategic technology investment decisions
- Understand business metrics and ROI
- Have limited technical ML knowledge
- Value practical examples over theory

RESPONSE FORMAT:
800-word article structured as:
- Compelling headline (10 words max)
- Brief hook (2-3 sentences)
- 3-4 main sections with descriptive subheadings
- Mix of paragraphs and bullet points
- Clear call-to-action conclusion

Result Scores:

• Clarity: 9/10
• Specificity: 9/10
• Context: 10/10
• Completeness: 9/10
• Structure: 9/10
• Overall: 8.8/10

Supported Frameworks

CO-STAR (Context, Objective, Style, Tone, Audience, Response)

Best for: Content creation, writing tasks, communications

Components:

• Context - Background information and situation
• Objective - Clear goal and purpose
• Style - Writing style and formatting approach
• Tone - Voice and emotional quality
• Audience - Target reader characteristics
• Response - Expected format and structure

Example Use Cases: Blog posts, emails, presentations, marketing copy, documentation

RISEN (Role, Instructions, Steps, End goal, Narrowing)

Best for: Multi-step processes, systematic procedures

Components:

• Role - Expertise and perspective needed
• Instructions - High-level guidance
• Steps - Detailed methodology
• End goal - Success criteria
• Narrowing - Constraints and boundaries

Example Use Cases: Code reviews, workflows, systematic analysis, project planning

RISE (Dual Variants)

RISE-IE (Input-Expectation)

Best for: Data analysis, transformations, processing tasks

Components:

• Role - Expertise needed
• Input - Data format and characteristics
• Steps - Processing methodology
• Expectation - Output requirements

Example Use Cases: CSV analysis, data processing, file transformations, report generation

RISE-IX (Instructions-Examples)

Best for: Content creation with reference examples

Components:

• Role - Expertise needed
• Instructions - Task guidance
• Steps - Workflow process
• Examples - Reference samples

Example Use Cases: Creative writing, template-based content, style matching

TIDD-EC (Task, Instructions, Do, Don't, Examples, Context)

Best for: High-precision tasks requiring explicit boundaries

Components:

• Task type - Nature of the work
• Instructions - What to accomplish
• Do - Explicit positive guidance
• Don't - Explicit negative guidance (what to avoid)
• Examples - Reference samples
• Context - Background information

Example Use Cases: Code generation with standards, compliance tasks, quality-critical work

CTF (Context, Task, Format)

Best for: Simple tasks where situational background matters more than expertise framing

Components:

• Context - Situation and background
• Task - What needs to be done
• Format - Output structure

Example Use Cases: Handoff documents, mid-project updates, situation-driven requests

RTF (Role, Task, Format)

Best for: Simple, well-defined tasks where expertise framing drives output quality

Components:

• Role - Expertise required
• Task - What needs to be done
• Format - Output structure

Example Use Cases: Quick conversions, simple formatting, straightforward requests

APE (Action, Purpose, Expectation)

Best for: Ultra-minimal prompts — the simplest structured framework

Components:

• Action - What to do (one clear verb-driven instruction)
• Purpose - Why it's needed (one sentence)
• Expectation - What a good result looks like

Example Use Cases: Quick summaries, single-function code, one-off requests, rapid iteration

BAB (Before, After, Bridge)

Best for: Transforming, rewriting, or refactoring existing content

Components:

• Before - Current state and what's wrong
• After - Desired end state
• Bridge - Transformation rules and constraints

Example Use Cases: Code refactoring, copy rewrites, tone changes, document restructuring, version migrations

RACE (Role, Action, Context, Expectation)

Best for: Medium-complexity tasks needing expertise + background + explicit success criteria

Components:

• Role - Expertise or persona
• Action - What needs to be done
• Context - Situational background and constraints
• Expectation - What a good result looks like

Example Use Cases: Technical reviews, expert analysis, contextual recommendations, documentation with standards

CRISPE (Capacity+Role, Insight, Instructions, Personality, Experiment)

Best for: Comprehensive prompts where you want multiple output variants to compare

Components:

• Capacity & Role - Expertise level and professional persona
• Insight - Background context and situational knowledge
• Instructions - The specific task
• Personality - Tone, voice, communication style
• Experiment - Request N variants to compare (the defining differentiator)

Example Use Cases: Marketing campaigns (A/B variants), content with tone options, strategic analysis needing multiple angles

BROKE (Background, Role, Objective, Key Results, Evolve)

Best for: Business deliverables with measurable outcomes and built-in self-improvement

Components:

• Background - Situation and context
• Role - Professional persona
• Objective - The task
• Key Results - Measurable business outcomes (OKR-style)
• Evolve - AI self-critiques and suggests 3 improvements

Example Use Cases: Sales process improvements, content strategy with KPIs, product decisions tied to metrics

CARE (Context, Ask, Rules, Examples)

Best for: Tasks with explicit constraints, compliance requirements, or quality standards

Components:

• Context - Situation and background
• Ask - The specific request
• Rules - Explicit constraints, dos/don'ts, standards
• Examples - Reference samples showing the quality bar

Example Use Cases: Healthcare/legal content, UI error messages, interview questions with bias constraints, brand-compliant copy

Skeleton of Thought (SoT)

Best for: Structured long-form content — generate outline first, then expand

Approach:

• Phase 1: Generate a concise skeleton/outline (key points only)
• Phase 2: Expand each skeleton point independently (parallelizable)
• SoT-R variant: Route non-suitable questions to standard generation

Example Use Cases: Technical documentation, structured reports, tutorials, any multi-section content

Step-Back Prompting

Best for: Principle-grounded reasoning — abstract to the underlying concept first

Approach:

• Generate a higher-level "step-back" question about underlying principles
• Answer the abstract question to retrieve principles
• Use those principles as context to answer the original specific question

Example Use Cases: STEM problems, architecture decisions, debugging, any task where first-principles reasoning matters

Least-to-Most (LtM)

Best for: Compositional multi-hop problems with ordered dependencies

Approach:

• Decompose the complex problem into ordered subproblems (simplest first)
• Solve each subproblem sequentially, feeding each answer into the next
• Use accumulated answers to solve the original problem

Example Use Cases: Multi-domain questions (legal + technical), complex calculations, architecture problems with prerequisites

Plan-and-Solve (PS+)

Best for: Zero-shot numerical and calculation reasoning

Approach:

• "Let's first understand the problem, extract relevant variables and their corresponding numerals, and devise a complete plan. Then, let's carry out the plan, calculate intermediate values, pay attention to computation, and solve the problem step by step."

Example Use Cases: Financial calculations (MRR, CAC, payback), math word problems, resource estimation, any zero-shot reasoning task

Self-Refine

Best for: Iterative quality improvement of any output

Approach: Generate → produce specific actionable feedback → refine → repeat until stopping criterion Research: Madaan et al. NeurIPS 2023 — +5-40% improvement across 7 task types

Example Use Cases: Code review and rewriting, writing improvement, plan refinement, pre-submission QA

CAI Critique-Revise

Best for: Aligning output to an explicit stated principle or standard

Approach: Initial output → critique against a specific principle → revision addressing every critique point Research: Anthropic Constitutional AI (arXiv 2212.08073, 2022) — principle-driven alignment

Example Use Cases: Plain language compliance, brand voice enforcement, epistemic quality (claims vs. assertions), legal/regulatory language standards

Devil's Advocate

Best for: Generating the strongest possible opposing argument against a position

Approach: Explicitly instructs the AI to attack a position as forcefully as possible — not balanced, not a straw man, but maximum-strength opposition Research: ACM IUI 2024 peer-reviewed study

Example Use Cases: Decision stress-testing, architecture reviews, countering groupthink, stakeholder preparation, debiasing

Pre-Mortem

Best for: Identifying specific failure causes before they happen

Approach: Assume the project has already failed → describe the failure → work backwards to specific causes with warning signs Research: Gary Klein's prospective hindsight — ~30% improvement over forward risk analysis

Example Use Cases: Project kickoffs, product launches, technical migrations, high-stakes strategic decisions

RCoT (Reverse Chain-of-Thought)

Best for: Verifying that reasoning addressed all conditions in a multi-constraint question

Approach: Generate answer → reconstruct the question from the answer → cross-check conditions → correct overlooked items Research: Academic backward reasoning literature (ACL 2025, NAACL 2025)

Example Use Cases: Multi-condition logic problems, complex requirements analysis, high-stakes reasoning verification

RPEF (Reverse Prompt Engineering)

Best for: Recovering a reusable prompt template from an existing output

Approach: Provide an output (and optionally the input) → AI analyzes it for tone, structure, constraints, persona → generates a reusable template with [PLACEHOLDER] variables Research: Li & Klabjan, EMNLP 2025 (arXiv 2411.06729)

Example Use Cases: Recovering lost prompts, codifying successful one-time outputs, building style templates, understanding system prompt behavior

Reverse Role Prompting (AI-Led Interview)

Best for: Complex tasks where you know the goal but struggle to specify all requirements

Approach: Provide a minimal intent statement → AI asks targeted clarifying questions → executes once context is complete Research: FATA framework (arXiv 2508.08308, 2025) — ~40% improvement over standard prompting

Example Use Cases: Complex strategy tasks, non-expert users, requirements gathering, generating complete prompts from an interview

Tree of Thought

Best for: Decisions where multiple approaches need systematic comparison

Approach:

• Defines the problem and constraints
• Explores 2-5 distinct solution branches in parallel
• Evaluates each branch against defined criteria
• Synthesizes into a reasoned recommendation

Example Use Cases: Architecture decisions, debugging with multiple hypotheses, technology selection, strategic trade-offs

ReAct (Reasoning + Acting)

Best for: Agentic tasks that interleave reasoning with tool use

Approach:

• Defines goal, available tools, and constraints
• Alternates Thought → Action → Observation cycles
• Each observation informs the next thought
• Continues until goal is reached

Example Use Cases: Agentic workflows, multi-step research, debugging with tools, data investigation

Chain of Thought

Best for: Complex reasoning and problem-solving

Approach:

• Breaks down reasoning into explicit steps
• Shows work and intermediate conclusions
• Verifies logic at each stage
• Builds to final answer

Example Use Cases: Math problems, debugging, decision analysis, logical reasoning

Chain of Density

Best for: Iterative refinement and compression

Approach:

• Starts with baseline version
• Progressively refines through iterations
• Increases information density
• Optimizes for specific goals

Example Use Cases: Summarization, content compression, explanation optimization

Installation

Choose the method that matches your AI tool:

Claude Code

/install-skill https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect

Plugin system (enables updates):

/plugin marketplace add ckelsoe/prompt-architect
/plugin install prompt-architect@prompt-architect-marketplace

Codex CLI

$skill-installer install https://github.com/ckelsoe/prompt-architect/tree/main/skills/prompt-architect

Other Agents (Gemini, Cursor, Copilot, Cline, Roo Code, etc.)

Copy skills/prompt-architect/ from this repo to ~/.agents/skills/prompt-architect/

npm (alternative)

npx @ckelsoe/prompt-architect

Installs to both .claude/skills/ and .agents/skills/ in one step.

ChatGPT

ChatGPT natively supports Agent Skills. Available on Business, Enterprise, Edu, Teachers, and Healthcare plans.

1. Download prompt-architect.skill (direct download, always latest version)
2. In ChatGPT, click your profile icon → Skills
3. Click New skill → Upload from your computer
4. Upload the .skill file

Windsurf

Windsurf does not yet support Agent Skills natively:

cat adapters/for-windsurf.md >> .windsurfrules

Any LLM API or Chat Interface

Paste the contents of adapters/system-prompt.md into your system prompt field. Works with OpenAI API, Anthropic API, Google Gemini API, Custom GPTs, or any tool with a system prompt.

See adapters/README.md for detailed instructions for each platform.

Quick Start

After installing, test with:

"Help me improve this prompt: write a technical blog post"

The skill will automatically:

1. Analyze your prompt and score it
2. Recommend the best framework (likely CO-STAR)
3. Ask clarifying questions
4. Generate an improved, structured prompt
5. Explain the changes made

Updating

# npm
npm update -g @ckelsoe/prompt-architect

# Check version
npm list -g @ckelsoe/prompt-architect

# Claude Code plugin
/plugin update prompt-architect@prompt-architect-marketplace

Migrating from v2.x? The package was renamed from @ckelsoe/claude-skill-prompt-architect. See MIGRATION.md for details.

Usage

Basic Usage

Simply ask Claude Code to improve any prompt:

"Help me improve this prompt: [your prompt here]"

Example Interaction

User: "Help me improve this prompt: create a function to process user data"

Skill: I'll analyze this prompt and help improve it.

**Analysis**:
- Clarity: 4/10 (basic goal stated)
- Specificity: 2/10 (what kind of data? what processing?)
- Context: 1/10 (no background)
- Completeness: 2/10 (missing many details)
- Structure: 3/10 (single sentence)
- **Overall: 2.4/10**

**Recommendation**: RISEN framework (multi-step process requiring methodology)

**Questions**:
1. What type of user data? (names, emails, profiles, etc.)
2. What processing is needed? (validation, transformation, storage?)
3. What's the tech stack? (Python, JavaScript, database type?)
4. Any constraints? (performance, security requirements?)
5. Expected error handling approach?

User: "It's a Python function processing CSV user registrations. Need to validate
emails, normalize names, and save to PostgreSQL. Should handle invalid data gracefully."

Skill: Perfect! Let me apply the RISEN framework...

[Improved prompt generated with clear role, instructions, steps, end goal, and constraints]

Advanced Usage

Request Specific Framework:

"Use the CO-STAR framework to improve: write marketing copy"

Iterate on Existing Prompts:

"Review this prompt and suggest improvements: [existing structured prompt]"

Switch Frameworks:

"Try using TIDD-EC instead for more explicit guidance"

Framework Selection Guide

Decision Tree

Are you transforming existing content (rewrite, refactor, convert)?
├─ YES → BAB (Before, After, Bridge)
└─ NO ↓

Is it an agentic/tool-use task?
├─ YES → ReAct (Thought-Action-Observation cycles)
└─ NO ↓

Is it a decision between multiple approaches?
├─ YES → Tree of Thought (branching exploration)
└─ NO ↓

Is it content/writing focused with audience and tone?
├─ YES → CO-STAR
└─ NO ↓

Is it a multi-step process with methodology?
├─ YES → RISEN
└─ NO ↓

Is it a data transformation (input → output)?
├─ YES → RISE-IE
└─ NO ↓

Does it need reference examples?
├─ YES → RISE-IX
└─ NO ↓

Does it need explicit dos/don'ts?
├─ YES → TIDD-EC
└─ NO ↓

Is it complex reasoning (one clear path)?
├─ YES → Chain of Thought
└─ NO ↓

Does it need iterative refinement?
├─ YES → Chain of Density
└─ NO ↓

Is it a numerical/calculation problem (zero-shot)?
├─ YES → Plan-and-Solve PS+
└─ NO ↓

Is it compositional / multi-hop (answer A needed before B)?
├─ YES → Least-to-Most
└─ NO ↓

Does it need first-principles reasoning?
├─ YES → Step-Back Prompting
└─ NO ↓

Is it structured long-form content (multiple semi-independent sections)?
├─ YES → Skeleton of Thought
└─ NO ↓

Do you need to recover a prompt from an existing output?
├─ YES → RPEF (Reverse Prompt Engineering)
└─ NO ↓

Do you need to clarify requirements before starting?
├─ YES → Reverse Role Prompting (AI interviews you first)
└─ NO ↓

Do you need to critique, stress-test, or verify something?
├─ General quality improvement → Self-Refine
├─ Align to explicit principle → CAI Critique-Revise
├─ Find opposing argument → Devil's Advocate
├─ Find failure modes → Pre-Mortem
└─ Verify conditions weren't missed → RCoT

Is it a simple task? Choose by primary driver:
├─ Expert role matters most → RTF
├─ Situational context matters most → CTF
├─ Need role + context + outcome bar → RACE
├─ Business deliverable with KPIs → BROKE
├─ Want multiple variants to compare → CRISPE
├─ Have explicit rules/constraints → CARE
└─ Ultra-minimal, one-off → APE

Quick Reference

Project Structure

prompt-architect/
│
├── README.md                          # This file
├── LICENSE                            # MIT License
│
└── prompt-architect/                  # The skill
    ├── SKILL.md                       # Core skill instructions (5 KB)
    │
    ├── scripts/                       # Analysis Utilities
    │   ├── framework_analyzer.py      # Framework recommendation logic
    │   └── prompt_evaluator.py        # Quality scoring system
    │
    ├── references/                    # Framework Documentation
    │   └── frameworks/                # Loaded on-demand
    │       ├── co-star.md             # CO-STAR reference (600+ lines)
    │       ├── risen.md               # RISEN reference (600+ lines)
    │       ├── rise.md                # RISE (IE/IX) reference (700+ lines)
    │       ├── tidd-ec.md             # TIDD-EC reference (600+ lines)
    │       ├── ctf.md                 # CTF reference
    │       ├── rtf.md                 # RTF reference (500+ lines)
    │       ├── ape.md                 # APE reference
    │       ├── bab.md                 # BAB reference
    │       ├── race.md                # RACE reference
    │       ├── crispe.md              # CRISPE reference
    │       ├── broke.md               # BROKE reference
    │       ├── care.md                # CARE reference
    │       ├── tree-of-thought.md     # Tree of Thought reference
    │       ├── react.md               # ReAct reference
    │       ├── skeleton-of-thought.md # Skeleton of Thought reference (ICLR 2024)
    │       ├── step-back.md           # Step-Back Prompting reference (Google DeepMind)
    │       ├── least-to-most.md       # Least-to-Most reference (Google Brain)
    │       ├── plan-and-solve.md      # Plan-and-Solve PS+ reference (ACL 2023)
    │       ├── chain-of-thought.md    # CoT reference (500+ lines)
    │       ├── chain-of-density.md    # CoD reference (500+ lines)
    │       ├── self-refine.md         # Self-Refine (NeurIPS 2023)
    │       ├── cai-critique-revise.md # CAI Critique-Revise (Anthropic 2022)
    │       ├── devils-advocate.md     # Devil's Advocate (ACM IUI 2024)
    │       ├── pre-mortem.md          # Pre-Mortem (Gary Klein)
    │       ├── rcot.md                # Reverse Chain-of-Thought
    │       ├── rpef.md                # Reverse Prompt Engineering (EMNLP 2025)
    │       └── reverse-role.md        # AI-Led Interview / FATA (arXiv 2025)
    │
    └── assets/
        └── templates/                 # Framework Templates
            ├── co-star_template.txt
            ├── risen_template.txt
            ├── rise-ie_template.txt
            ├── rise-ix_template.txt
            ├── tidd-ec_template.txt
            ├── ctf_template.txt
            ├── rtf_template.txt
            ├── ape_template.txt
            ├── bab_template.txt
            ├── race_template.txt
            ├── crispe_template.txt
            ├── broke_template.txt
            ├── care_template.txt
            ├── tree-of-thought_template.txt
            ├── react_template.txt
            ├── skeleton-of-thought_template.txt
            ├── step-back_template.txt
            ├── least-to-most_template.txt
            ├── plan-and-solve_template.txt
            ├── chain-of-thought_template.txt
            ├── chain-of-density_template.txt
            ├── self-refine_template.txt
            ├── cai-critique-revise_template.txt
            ├── devils-advocate_template.txt
            ├── pre-mortem_template.txt
            ├── rcot_template.txt
            ├── rpef_template.txt
            ├── reverse-role_template.txt
            └── hybrid_template.txt

Core Components:

• SKILL.md - Main skill logic and instructions (intent-based selection system)
• 27 Framework Docs - Complete references with examples
• 29 Templates - Ready-to-use structures (27 frameworks + hybrid + chain variants)
• 2 Python Scripts - Analysis and scoring utilities

Documentation

Framework References

Detailed documentation for each framework in prompt-architect/references/frameworks/:

• co-star.md - Context, Objective, Style, Tone, Audience, Response
• risen.md - Role, Instructions, Steps, End goal, Narrowing
• rise.md - Dual variants (IE & IX)
• tidd-ec.md - Task, Instructions, Do, Don't, Examples, Context
• ctf.md - Context, Task, Format
• rtf.md - Role, Task, Format
• ape.md - Action, Purpose, Expectation
• bab.md - Before, After, Bridge
• race.md - Role, Action, Context, Expectation
• crispe.md - Capacity+Role, Insight, Instructions, Personality, Experiment
• broke.md - Background, Role, Objective, Key Results, Evolve
• care.md - Context, Ask, Rules, Examples
• tree-of-thought.md - Branching multi-path exploration
• react.md - Reasoning + Acting (agentic tool use)
• skeleton-of-thought.md - Skeleton-first parallel expansion (ICLR 2024)
• step-back.md - Abstract to principles first (Google DeepMind, ICLR 2024)
• least-to-most.md - Sequential subproblem solving (Google Brain, ICLR 2023)
• plan-and-solve.md - Zero-shot PS+ trigger (ACL 2023)
• chain-of-thought.md - Step-by-step reasoning
• chain-of-density.md - Iterative refinement
• self-refine.md - Generate → Feedback → Refine (NeurIPS 2023)
• cai-critique-revise.md - Principle-based critique + revision (Anthropic 2022)
• devils-advocate.md - Strongest opposing argument (ACM IUI 2024)
• pre-mortem.md - Assumed failure → backward cause analysis (Gary Klein)
• rcot.md - Reverse Chain-of-Thought verification
• rpef.md - Reverse Prompt Engineering (EMNLP 2025)
• reverse-role.md - AI-Led Interview / FATA (arXiv 2025)

Contributing

Contributions are welcome! Here's how you can help:

Ways to Contribute

1. Add New Frameworks - Implement additional prompting frameworks
2. Improve Existing Frameworks - Enhance documentation and examples
3. Bug Fixes - Report and fix issues
4. Documentation - Improve guides and examples
5. Testing - Test with different use cases and report findings

Adding New Frameworks

1. Research and validate the framework has backing
2. Create comprehensive framework reference (see existing in references/frameworks/)
3. Add template file in assets/templates/
4. Update SKILL.md framework selection logic
5. Test with multiple use cases
6. Submit pull request with examples and rationale

Contribution Guidelines

File Structure:

New framework contributions should include:
- references/frameworks/your-framework.md (600+ lines with examples)
- assets/templates/your-framework_template.txt
- Updates to SKILL.md framework selection
- Examples in documentation

Documentation Standards:

• Clear component definitions
• Multiple use case examples
• Selection criteria explanation
• Before/after comparisons
• Research citations

Testing Requirements:

• Test with 3-5 diverse prompts
• Verify scoring improves
• Check framework selection logic
• Validate template structure

Pull Request Process

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-framework)
3. Make your changes following the guidelines
4. Test thoroughly with multiple examples
5. Update relevant documentation
6. Commit with clear messages
7. Push to your fork
8. Submit a Pull Request with:
   • Clear description of changes
   • Examples of improvements
   • Test results
   • Documentation updates

License

This project is licensed under the MIT License - see the LICENSE file for details.

License Summary

Permissions:

• Commercial use
• Modification
• Distribution
• Private use

Conditions:

• License and copyright notice must be included

Limitations:

• No liability
• No warranty

Support

Getting Help

• Issues - Report bugs or request features via GitHub Issues
• Documentation - Check framework references in references/frameworks/

FAQ

Q: Why isn't the skill activating? A: Ensure the skill folder is in the correct location (~/.claude/skills/prompt-architect/) and restart Claude Code.

Q: Can I use this without Claude Code? A: Yes! Include SKILL.md content in your Claude API system prompt.

Q: Which framework should I use? A: The skill will recommend one, but check the Framework Selection Guide above.

Q: Can I add my own framework? A: Yes! See the Contributing section for guidelines.

Q: Does this work with other LLMs? A: The frameworks are universal, but the skill is optimized for Claude.

Transforming your prompts with research-backed frameworks