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, notnpm install. Thenpxcommand runs the interactive multi-agent installer. Runningnpm installwill only install to Claude Code silently via the postinstall hook.
Requires
.npmrcwith@ckelsoe:registry=https://npm.pkg.github.comand a GitHub token withread:packagesscope.
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
| Framework | Best For | Complexity |
|---|---|---|
| CO-STAR | Content creation, writing tasks | High |
| RISEN | Multi-step processes, procedures | High |
| CRISPE | Comprehensive prompts with multiple output variants | High |
| BROKE | Business deliverables with OKR-style measurable outcomes | Medium |
| RISE-IE | Data analysis, transformations (Input-Expectation) | Medium |
| RISE-IX | Content creation with examples (Instructions-Examples) | Medium |
| TIDD-EC | High-precision tasks with explicit dos/don'ts | Medium |
| RACE | Expert tasks requiring role + context + outcome clarity | Medium |
| CARE | Constraint-driven tasks with explicit rules and examples | Medium |
| CTF | Simple tasks where situational context drives the prompt | Low |
| RTF | Simple, focused tasks where expertise framing matters | Low |
| APE | Ultra-minimal one-off prompts | Low |
| BAB | Rewriting, refactoring, transforming existing content | Low |
| Tree of Thought | Decisions requiring exploration of multiple approaches | Medium |
| ReAct | Agentic / tool-use tasks with iterative reasoning | Medium |
| Skeleton of Thought | Structured long-form content (outline-first) | Medium |
| Step-Back | Principle-grounded reasoning (abstract first, then specific) | Medium |
| Least-to-Most | Compositional multi-hop problems (simplest first) | Medium |
| Plan-and-Solve (PS+) | Zero-shot numerical/calculation reasoning | Low |
| Chain of Thought | Reasoning, problem-solving | Medium |
| Chain of Density | Iterative refinement, summarization | Medium |
| Self-Refine | Iterative output quality improvement (any task) | Medium |
| CAI Critique-Revise | Principle-based critique and revision (Anthropic) | Medium |
| Devil's Advocate | Strongest opposing argument against a position | Low |
| Pre-Mortem | Assume failure, identify specific causes | Low |
| RCoT | Verify reasoning by reconstructing the question | Medium |
| RPEF | Recover/reconstruct a prompt from an existing output | Low |
| Reverse Role Prompting | AI interviews you before executing | Low |
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