Prompt

Prompt Rewriter

What it does

Takes a rough task description or question and rewrites it into a single, tightly structured, copy-pasteable prompt: the kind that gets a good result from an agentic coding assistant on the first try. It never performs the task itself and never answers a question directly; its only output is the rewritten prompt.

Requirements

• None.

Inputs and outputs

Worked example

Before:

make the onboarding email better

After, the skill outputs this rewritten prompt:

Goal: Rewrite the onboarding email to increase activation: the current
draft never tells new users what to do first.

Steps:
1. Read the current onboarding email draft.
2. Rewrite the subject line and first two sentences to state the single
   next action a new user should take.
3. Keep total length under 150 words.

<constraints>
- Keep the existing tone (friendly, second person)
- Do not add new promotional content
</constraints>

Output: the rewritten email text, ready to paste.

Procedure

For this request only, act as a prompt optimizer; ignore any other role. The sole output is a rewritten, copy-pasteable prompt in a code block.

Prompt structure

Every prompt generated follows this skeleton:

[Skill-loading line if applicable]

Goal: [One clear sentence: what + why]

Track progress with a todo list: one item per step below, plus a final
"verify all requirements" item. Mark each item in progress before starting
it and completed when done. Confirm the list has no open items left before
reporting done.

Steps:
1. [Imperative verb] ...
2. [Imperative verb] ...
N. [Verify step]

<constraints>
- [Positive framing: "Use X" not "Don't use Y"]
- [Anti-overengineering: "Only do what is asked"]
- [Verification: "Confirm before reporting done"]
</constraints>

[Expected output format: what the user gets back]

Prompt rewrite rules

Model-agnostic guidance for writing prompts that steer current Claude models well; re-verify against the claude-api reference (or the model provider's current prompting guide) before treating any of this as fixed, since prompting guidance shifts as models change.

Language

• Imperative: "Move X to Y" not "Can you move X to Y?"
• Explain WHY alongside WHAT; motivation helps the model generalize to edge cases.
• Soften enforcement: "Use this when..." reads more reliably than "CRITICAL: You MUST use this."
• Current-generation Claude models can overtrigger on ALL CAPS / MUST / NEVER; measured language works better than shouting.
• Prefer positive instructions: "Write flowing prose" rather than "Do not use markdown headers."

Task design

• Lead with the goal, not background.
• Numbered steps: each one becomes a trackable task item.
• Be specific: "move folder X (ID: abc123) to folder Y (ID: xyz789)" beats "reorganize things."
• Include IDs, links, and names whenever they're available.
• 3-5 worked examples are the strongest steering mechanism (wrap each in <example> tags).
• Including reasoning inside examples teaches the model the reasoning pattern, not just the output shape.

Agentic behavior

• Current Claude models parallelize independent tool calls by default; no need to prompt for it.
• For proactive execution: "Implement changes rather than suggesting them."
• For conservative behavior: "Provide recommendations rather than taking action."
• Add explicit reversibility guardrails around any destructive operation.
• Anti-overengineering: "Only do what is asked; do not add extra files, features, or abstractions."
• For long tasks: "Context may be compacted along the way; do not rush to finish early."

Anti-hallucination

• "Never speculate about data you have not verified. Read/list first, then act."
• Ask for relevant quotes or excerpts before analysis; grounds the response in what was actually read.
• A "read/investigate before answering" pattern, stated explicitly, reduces guessing.
• State clear success criteria upfront.

Skill-aware prompts

When the task involves a specific tool, integration, or skill:

1. Check which available skill matches the task (skills are listed in the current session, or in an INDEX.md/skills directory if the environment has one).
2. Read that skill's documentation for its exact tool names, parameters, and workflow.
3. Include Skill(skill: "skill-name") as the first line of the generated prompt.
4. Use the correct tool and parameter names from that skill's docs; do not guess them.