prompt-generator

Solid

Turns a vague ask into a rigorous, grounded, token-efficient prompt: role + objective, testable done criteria, anti-hallucination (verify or say "I don't know"), anti-tokenmaxing (lead with the answer, output budget), strict agent discipline. Use proactively when the user hands over a prompt or task spec destined for another agent or LLM. Also on "write a prompt", "improve this prompt", "prompt for an agent". Not for human-facing prose or work you'll execute yourself this session.

AI & Automation 6 stars 0 forks Updated 2 days ago MIT

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Quality Score: 81/100

Stars 20%
28
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Prompt Generator Produce the prompt the user should have written. A good prompt is precise, grounded, and short — every token earns its place. You output a *prompt*, not an essay about prompting. ## Proactive use If the user hands over text another agent or LLM will run — a rough prompt, a task spec, a "have it do X" — invoke this without being asked: announce in one line ("Tightening this into a rigorous prompt") and proceed. Never ask permission to run the skill. ## Process 1. **Read the intent.** What outcome does the user actually want, and who runs the prompt (a coding agent, a chat model, a one-shot task)? 2. **Ask only if blocked.** At most 1–2 questions, and only when a missing fact would change the prompt's structure. Otherwise proceed and list assumptions in one line. 3. **Draft** using the section menu — include only the sections the task needs (a one-shot classifier doesn't need "When blocked"). 4. **Self-check** against the rubric below. Cut anything that doesn't change the model's behavior. 5. **Output** the finished prompt in a fenced block, then ≤3 lines on key choices and what to tune. ## Section menu (use what the task needs) - **Role** — who the model is and its single objective. - **Context / Inputs** — what it's given; mark placeholders as `{{like_this}}`. - **Rules** — the grounding + efficiency rules below, plus task-specific ones. - **Steps** — ordered, only if the task is genuinely multi-step. - **Output format** — exact sha...

Details

Author
alonbaron
Repository
alonbaron/claude-skills
Created
1 months ago
Last Updated
2 days ago
Language
N/A
License
MIT

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