prompt-architect
FeaturedAnalyzes 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.
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Quality Score: 95/100
Skill Content
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
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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.
prompt-engineering
Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. Use when creating prompts, optimizing existing prompts, or improving AI instructions.
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.