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AI Tool — Free & Instant

Prompt Optimizer

Paste any messy prompt and restructure it as a standard brief, a multi-agent orchestrator plan, or a Swamp workflow prompt — tuned for Cursor, Codex, Antigravity, and more.

📝 Your raw prompt

Paste what you want the AI to do. Tune options below, then optimize.

Use this prompt in
What are you trying to do?

We’ll auto-suggest a goal from your text when you optimize.

Tone
Detail level
Prompt structure

Standard RTCF works for most chat prompts. Use Orchestrator for multi-step agent work, or Swamp for repeatable ops workflows in .swamp/.

AI role
Detected role

Paste a prompt above — we’ll infer the best role when you optimize (or live as you type).

Try an example

✨ Optimized prompt

Structured result — copy sections or the full prompt below.

🎯

Your optimized prompt will appear here.
Paste a raw prompt above and click Optimize prompt.

💡 Pro tips for better prompts

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  • Match the role to the task. Use Auto-detect for most prompts, or switch to Pick manually when you know exactly who the AI should be.
  • Include your audience. Who will read or use the output shapes tone, depth, and vocabulary.
  • Set negative constraints. Tell the AI what to avoid — jargon, filler, made-up stats, etc.
  • Pick the right structure. Standard for chat; Orchestrator for multi-agent work; Swamp for repeatable ops workflows.
  • Iterate. Use the optimized prompt, review results, then refine once or twice.

What Is Prompt Engineering and Why Does It Matter?

Prompt engineering is the skill of crafting instructions that get the best possible output from AI models like ChatGPT, Claude, Gemini, Midjourney, and DALL-E. The difference between a vague prompt and a well-structured one can mean the difference between a generic, unusable response and a precise, high-quality result that saves you hours of work. As AI becomes integral to writing, coding, marketing, and creative work, learning to communicate effectively with these models is rapidly becoming one of the most valuable skills in any professional's toolkit.

Three Prompt Structures

Standard (RTCFE) — Role, Task, Context, Constraints, and Output Format for everyday chat and writing. Orchestrator — a coordinator prompt with a task graph, worker agent definitions, coordination rules, and handoff protocol for multi-step agent work in Cursor, Codex, or Claude Code. Swamp — prompts aligned with Swamp Club automation: models, YAML definitions, workflow DAGs, vault secrets, and CLI validation in .swamp/.

Common Prompt Mistakes to Avoid

  • Being too vague: "Write something about marketing" gives the AI no direction. Specify the topic, angle, audience, and purpose.
  • Skipping context: Without knowing who the content is for or where it will be used, AI models default to generic, one-size-fits-all responses.
  • No format specification: If you don't ask for bullet points, headings, or a specific structure, you'll get a wall of text that requires heavy editing.
  • Ignoring constraints: Failing to set tone, word count, or negative constraints ("don't use jargon") leads to outputs that miss the mark.

Tips for Editors & Models

Cursor, Windsurf, and Claude Code work best with file paths, constraints, and verification steps — not vague “fix my app” requests. Codex / Copilot prefers short, scoped coding tasks tied to visible context. Antigravity benefits from step-by-step objectives and clear done criteria. ChatGPT and Claude excel with audience, tone, and format spelled out. Midjourney and DALL·E need visual specifics: style, lighting, composition, and mood — not abstract concepts alone.

FAQ

Does a better prompt really make a difference?

Yes. Well-structured prompts usually produce clearer, more usable output than one-line requests. A defined role, task, context, and format reduce guesswork and follow-up edits.

What's the ideal prompt length?

For most tasks, 100–300 words in the optimized brief is a good target. Very short raw prompts often lack context; extremely long ones can dilute focus. Aim for completeness without repetition.

Should I specify the output format?

Yes — format is one of the highest-impact improvements (headings, bullets, code blocks, tables). It turns generic text into something you can use immediately.

When should I use the Orchestrator structure?

Choose Orchestrator when one coordinator should plan work and delegate to multiple workers — for example parallel code tasks, separate research vs implementation tracks, or any job with clear sub-tasks and dependencies. The optimizer outputs a mission, task graph (waves/dependencies), worker agent definitions, coordination rules (parallelism, conflict checks, retries), and a handoff format. Best in agentic IDEs like Cursor, Claude Code, Windsurf, or Codex when you want a “lead agent” rather than a single chat reply.

When should I use the Swamp structure?

Choose Swamp when you want repeatable, reviewable automation aligned with Swamp Club — typed models, YAML definitions, workflow DAGs, vault-backed secrets, and versioned run data under .swamp/. Use it for ops-style goals (inventory cloud resources, scheduled checks, credential rotation, DNS audits) not one-off creative writing. The prompt reminds the agent to search existing models, write definitions, compose workflows, keep secrets in vaults, and review YAML before destructive runs.

Do I need Swamp installed to use the Swamp prompt template?

No — the template is a structured brief you paste into your agent. To actually run workflows you need Swamp in your repo (swamp repo init) and an agent with Swamp skills (Cursor, Codex, Claude Code, etc.). Until then, the output still helps you plan automation clearly.