prompt-architect

Featured

Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.

AI & Automation 259 stars 31 forks Updated 2 days ago MIT

Install

View on GitHub

Quality Score: 95/100

Stars 20%
80
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Prompt Architect You are an expert in prompt engineering and systematic application of prompting frameworks. Help users transform vague or incomplete prompts into well-structured, effective prompts through analysis, dialogue, and framework application. ## Core Process ### 1. Initial Assessment When a user provides a prompt to improve, **score it 1-10 on each of these five dimensions** and report an overall score (the mean, to one decimal place). Always show the scores — they justify the changes you are about to make and give the user a before/after they can feel. | Dimension | What you are scoring | |---|---| | **Clarity** | Is the goal unambiguous? Penalize vague terms ("thing", "stuff", "something", "maybe"), unresolved pronouns, and an implied-but-unstated objective. | | **Specificity** | Are requirements concrete? Reward named entities, quantities, and explicit format/length/style specifications. Penalize prompts so short they cannot carry the detail. | | **Context** | Is the necessary background present? Reward stated situation, audience, and rationale ("because", "in order to"). Penalize a bare instruction with no setting. | | **Completeness** | Are *what*, *why*, *how*, and *output format* all present? Each missing element costs. | | **Structure** | Is it organized for its length? Reward sections, lists, and logical ordering. Penalize run-on sentences and long unbroken prose. | **Rubric anchors** — apply per dimension so scores mean the same thing every time: ...

Details

Author
ckelsoe
Repository
ckelsoe/prompt-architect
Created
8 months ago
Last Updated
2 days ago
Language
JavaScript
License
MIT

Integrates with

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Listed

prompt-builder

Builds and improves prompts of every kind — everyday Claude prompts, SKILL.md instruction bodies, skill descriptions, and agent/system prompts. Detects mode from input: critique-and-rewrite when the user pastes a draft, interview-and-build when the user describes a goal without a draft. Always does live web research on current Anthropic prompting guidance before producing output. Returns a short critique plus a copy/paste-ready prompt block. Use whenever the user asks for help writing, improving, rewriting, critiquing, sharpening, or scoping a prompt — including phrases like "help me write a prompt for…", "improve this prompt", "make this better", "what's wrong with this prompt", "rewrite this", "I need a system prompt for…", "draft a SKILL.md description for…", "write a prompt for", "sharpen this prompt", or whenever the user shares a block of text that is clearly an LLM prompt and asks for any kind of feedback or revision.

0 Updated today
cody-hutson
AI & Automation Listed

prompt-engineering

Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. Use when creating prompts, optimizing existing prompts, or improving AI instructions.

2 Updated today
desilokesh1
AI & Automation Solid

prompt-engineer

Design, optimize, test, and evaluate prompts for large language models. Use when: (1) crafting or refining system prompts, user prompts, or prompt templates, (2) optimizing token usage or cost of existing prompts, (3) designing few-shot examples or chain-of-thought reasoning, (4) setting up prompt evaluation, A/B testing, or regression testing, (5) building production prompt management systems (versioning, monitoring, safety), (6) debugging inconsistent or low-quality LLM outputs, (7) selecting prompt patterns (zero-shot, few-shot, CoT, ToT, ReAct, role-based). Triggers on: prompt engineering, optimize prompt, reduce tokens, prompt template, few-shot, chain-of-thought, prompt evaluation, A/B test prompts, prompt versioning.

36 Updated today
OpenCoven