Craft Prompt

craft-prompt

Purpose

Turn a goal, scattered notes, or a raw ask into a clear, well-structured prompt that gets the best results from any LLM — a separate step from doing the task itself, so the result is a copy-paste-ready text block the user can drop into any AI interface.

Use this when

• the user asks to write, make, or build a prompt from scratch
• scattered notes need to become a reusable prompt or template
• a /goal condition or other reusable prompt template is needed
• Korean prompt requests appear, such as "프롬프트 만들어" or "프롬프트 작성"

Inputs

• Outcome — what the target should accomplish or return
• Target — the model, product surface, or agent interface when it changes the prompt
• Context — facts the target cannot reliably infer or retrieve itself
• Reuse — one-shot use, or a reusable template with placeholders
• For coding-agent or worktree prompts: the execution context — default to paths relative to the current worktree root unless the user explicitly needs machine-specific absolute paths

Don't over-ask. Infer what the request or available context already establishes, and ask only when the answer would materially change the prompt.

Workflow

1. Resolve the outcome. State what should be true or delivered at the end. Preserve a shape, length, tone, or language only when the user named it or the consumer requires it.
2. Gather only load-bearing context. The delivered prompt usually travels without the current conversation, so include facts the target cannot reliably infer or retrieve; omit only what its surface already provides through files, tools, or attachments. For a /goal, read references/goal-conditions.md, then gather outcome, transcript-visible evidence, constraints, scope, budget, and stop condition — the evaluator's stated check is part of the completion contract, not process prescription. For a reusable template, identify values that genuinely vary, turn each into a {{placeholder}}, and do not hardcode recurring values.
3. Draw the boundary. Add scope, non-goals, approval limits, compatibility requirements, or irreversible-action rules only where violating them would matter. Keep related authorization in one compact policy rather than repeating it.
4. Add evidence when warranted. For research, coding-agent, or other high-impact prompts, say what should be checked before finalizing: requirements, grounding, tests, format, or side effects. Prefer observable checks over generic caution.
5. Repair known failure modes. Start with the lean prompt above. Add a role, explicit format, example, XML boundary, process step, or tool rule only when the request requires it or it corrects a likely or observed failure. Use references/components-guide.md as the repair menu and references/quality-checklist.md for complex prompts. For image generation, video generation, or a system prompt, load the matching template in templates/.
6. Sharpen and deliver. State each instruction once, remove prose that does not change behavior, resolve conflicts, and keep missing-context handling proportionate: retrieve when available, ask when the answer changes the work, otherwise proceed with a labeled assumption. Then follow Output format.

Output format

A fenced code block, ready to copy-paste — always, when the user explicitly asked for a prompt or clearly invoked this skill. Never replace the prompt with direct task execution.

If relevant, add a brief note outside the code block explaining placeholders or a target setting that materially changes results. Do not append generic prompting advice.

Non-English prompts: write the prompt body in the requested language. If XML is actually useful, keep tag names in English (<context>, <task>) for portability.

Guardrails

• always deliver the prompt when one was asked for, even if the underlying task looks simple enough to do directly
• keep volatile target-specific behavior out of this portable spine
• default to worktree-relative paths in coding/worktree prompts; state the base once if it could be ambiguous
• don't inflate a simple prompt to look thorough — see Principles
• don't encode reasoning steps the target can choose better itself unless order or completeness is part of the contract

Principles

Full statements in references/shared-principles.md:

• Context beats instruction
• Outcome over process
• Boundaries over workarounds
• Earn every control
• Right-sized beats thorough-looking

Prompt-specific:

1. The prompt is the product. Deliver polished text the user copies as-is, not a meta-discussion about prompting.
2. Respect the target. Use its native controls for effort, verbosity, search, or structured output when available; don't reproduce those controls as prompt prose without a reason.
3. Reusability when asked. Use {{placeholder}} syntax with clear labels for templates; bake in specifics for one-shot prompts.
4. Verification beats vague caution. For complex or high-impact prompts, say what to verify before finalizing instead of piling on broad "be thorough" instructions.
5. Know when to skip. Skip prompt-building only when the user didn't ask for a prompt and direct execution is clearly better; if they explicitly asked for a prompt, deliver it even when the underlying task is simple.

Failure modes

• process worship — listing every step to reach the outcome instead of stating the outcome and letting the target LLM find its own path
• control accumulation — keeping old roles, examples, formatting rules, and workarounds after the behavior they corrected has disappeared
• template worship — templating a one-shot request nobody will reuse, or the reverse: hardcoding a value that actually varies week to week
• missing output contract — delivering prose about the prompt instead of a copy-pasteable fenced block, or dropping a shape constraint the user named
• fluff inflation — padding with generic "be helpful/thorough" language that doesn't change behavior
• format mismatch — ignoring the named target's conventions, or leaking platform-specific instructions into this portable spine instead of templates/

Example

Input: "write me a code review prompt for GPT, keep it short"

Delivered prompt:

Review the diff below for correctness bugs, security issues, and unnecessary complexity. Skip style nits.

Report only issues you're confident about, one per line: `file:line — issue — suggested fix`.

<diff>
{{diff}}
</diff>

Note outside the block: "Swap {{diff}} for the actual diff before sending."

Templates (special cases where a well-crafted template adds real value)

• templates/image-gen.md — Image generation (photo, illustration, icon, per-platform notes)
• templates/video-gen.md — Video generation (text-to-video, image-to-video, camera keywords, per-platform notes)
• templates/system-prompt.md — Chatbot/agent system prompts (minimal contract plus optional clauses)

References (load on demand)

• references/shared-principles.md — full statements of the five principles
• references/components-guide.md — optional controls to add in response to task needs or concrete failures
• references/prompt-patterns.md — common patterns: research, code gen, review, writing, extraction, analysis, decision
• references/quality-checklist.md — Quality checks with failure modes and fixes
• references/goal-conditions.md — Writing /goal completion conditions and reviewable goal specs for Claude Code and Codex autonomous loops (transcript-visible evidence, cross-platform differences, and caveats)