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

Example Use Cases: Handoff documents, mid-project upd