prompt-architect

Prompt Architect

Transform vague prompts into expert-level, structured prompts using 31 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.

Tip: Use npx rather than npm install. Both work — npm install runs the same installer via the postinstall hook and installs to every agent it detects — but only npx gives you the interactive menu to choose specific targets.

Published on the public npm registry. No authentication required.

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Table of Contents

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

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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

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Key Features

31 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
Self-Consistency	Robust answers: sample many reasoning paths, majority-vote	Medium
Chain of Density	Summarization at fixed length, maximum information density	Medium
Iterative Compression	Shortening text toward a target length	Low
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
Chain-of-Verification	Fact-check a draft by verifying each claim independently	Medium
RPEF	Recover/reconstruct a prompt from an existing output	Low
Reverse Role Prompting	AI interviews you before executing	Low

Composable technique (layered onto any framework, not one of the 31): Few-shot / in-context examples — the discipline of adding well-chosen, well-ordered worked examples to a prompt.

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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RACE (Role, Action, Context, Expectation)

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

Components: