perfect-prompt
Skill2 starsChanged 6 months agoMITView on GitHub
Stress-test and refine user prompts until they are specific, unambiguous, and execution-ready. Use when the user asks to improve, validate, critique, rewrite, or make a perfect prompt, or when they mention prompt quality, ambiguity, missing context, unclear requirements, or prompt engineering.
In this repository17 other files it gives its agents.
- create-nextjs-specificationSkill
- create-product-demoSkill
- find-github-repoSkill
- find-npm-packageSkill
- image-styleSkill
- nextjs-route-scaffoldSkill
- place-meSkill
- review-meSkill
- setup-aeo-geoSkill
- setup-harnessSkill
- songwritingSkill
- step-by-stepSkill
- stock-analysisSkill
- study-meSkill
- teach-meSkill
- three-linesSkill
- visualstormingSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
14 rules. About 0 are common, 14 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈14
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Do not rewrite immediately if the prompt is underspecified.
- Distinctive: line 23
Rough: ask for the missing detail that changes the result most.
- Distinctive: line 25
Unclear: ask for the goal before rewriting.
- Distinctive: line 36
Ask one question at a time until blockers are resolved.
- Distinctive: line 38
If the answer can be discovered from the codebase, files, conversation, or provided references, inspect those instead of asking.
- Distinctive: line 44
Ask the highest-impact unresolved question first.
- Distinctive: line 45
Do not ask about preferences that do not change the final prompt.
- Distinctive: line 46
Do not bundle unrelated questions together.
- Distinctive: line 47
If the user asks for speed, make reasonable assumptions and label them clearly in the final prompt.
- Distinctive: line 48
If the prompt is already good, say so and offer only targeted improvements.
- Distinctive: line 52
Do not return a long critique when the user needs a usable prompt.
- Distinctive: line 53
Keep the final prompt copy-pasteable.
- Distinctive: line 56
If the prompt controls an agent, include when the agent should ask before acting.
- Distinctive: line 60
Use this structure when it fits the task:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Perfect Prompt
Goal
Turn a rough prompt into an execution-ready prompt by finding vague wording, missing context, hidden assumptions, weak success criteria, and unnecessary scope.
Do not rewrite immediately if the prompt is underspecified. Interrogate it first.
Workflow
- Classify the prompt and choose the next action:
- Simple: enough context and a clear expected output.
- Rough: direction exists, but important details are missing.
- Ambiguous: multiple interpretations could produce different work.
- Unclear: goal, audience, or output cannot be inferred safely.
- Act on the classification:
- Simple: tighten the wording and return the improved prompt.
- Rough: ask for the missing detail that changes the result most.
- Ambiguous: name the competing interpretations, recommend one, then ask.
- Unclear: ask for the goal before rewriting.
- Audit the prompt across these axes:
- Objective: what outcome should the agent produce?
- Context: what background, files, audience, constraints, or examples matter?
- Scope: what is included, excluded, and deferred?
- Output: format, length, tone, language, artifacts, and acceptance criteria.
- Process: whether the agent should ask first, research, plan, implement, verify, or stop.
- Risk: irreversible actions, sensitive data, external calls, cost, credentials, or ambiguity.
- Separate blockers from improvements:
- Blocker: missing information that would change the task, output, or safety boundary.
- Improvement: detail that would polish the result but is not required to proceed.
- Ask one question at a time until blockers are resolved.
- For each question, provide a recommended answer the user can accept or edit.
- If the answer can be discovered from the codebase, files, conversation, or provided references, inspect those instead of asking.
- Rewrite the prompt after blockers are resolved, preserving the user's intent.
- Provide the final prompt plus a short change note explaining what was clarified.
Question Rules
- Ask the highest-impact unresolved question first.
- Do not ask about preferences that do not change the final prompt.
- Do not bundle unrelated questions together.
- If the user asks for speed, make reasonable assumptions and label them clearly in the final prompt.
- If the prompt is already good, say so and offer only targeted improvements.
Output Rules
- Do not return a long critique when the user needs a usable prompt.
- Keep the final prompt copy-pasteable.
- Preserve requested language, tone, and domain vocabulary unless they weaken clarity.
- Include assumptions only when they materially affect execution.
- If the prompt controls an agent, include when the agent should ask before acting.
Final Prompt Shape
Use this structure when it fits the task:
Role: [who the agent should act as]
Goal: [specific outcome]
Context: [relevant background and inputs]
Task: [ordered work to perform]
Constraints: [must / must not rules]
Output: [format, tone, language, length, files]
Verification: [how completion should be checked]
Ask first if: [conditions that require clarification]For short prompts, collapse this into one concise paragraph.
Quality Bar
A perfect prompt is:
- Clear enough that two capable agents would produce similar results.
- Specific about output and success criteria.
- Explicit about constraints and non-goals.
- Honest about uncertainty and missing information.
- Short enough to use, but complete enough to prevent avoidable rework.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

