MCPInstall

prompt-improver

A skill for improving prompts by applying general LLM/agent best practices. When the user provides a prompt, this skill outputs an improved version, identifies missing information, and provides specific improvement points. Use when the user asks to "improve this prompt", "review this prompt", or "make this prompt better".

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/dotneet/claude-code-marketplace --skill prompt-improver
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads prompt-improver automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Prompt Improver

Overview

A skill that analyzes and improves prompts based on general LLM/agent best practices. It focuses on verifiability, clear scope, explicit constraints, and context economy so the agent can execute with minimal back-and-forth.

If you are running in Claude Code, also read references/claude.md and apply the additional Claude-specific techniques. If you are running in Codex CLI, also read references/codex.md and apply the additional Codex-specific techniques.

When the input is a document that instructs an agent (e.g., plan files, AGENTS.md, system instruction docs), treat the document as the improvement target; identify issues and propose concrete improvements, and include a revised draft when helpful.

Workflow

Step 0: Classify Task and Complexity

Classify the task and decide whether an explicit exploration/planning phase should be recommended:

  • Task type: bugfix, feature, refactor, research, UI/visual, docs, ops
  • Complexity: single-file/small change vs multi-file/uncertain impact
  • Risk: data safety, security, compatibility, performance
  • Input type: prompt vs agent-instruction document (plan files, AGENTS.md, system instruction docs)

If the task is complex or ambiguous, the improved prompt should explicitly request an exploration/planning phase before implementation.

Step 1: Analyze the Prompt

Analyze the user-provided prompt from the following perspectives:

  1. Verifiability: Does it include means for Claude to verify its own work?
  2. Specificity: Are files, scenarios, and constraints clearly specified?
  3. Context: Is necessary background information provided?
  4. Scope: Is the task scope appropriately defined?
  5. Expected Outcome: Are success criteria clear?
  6. Constraints: Are language/runtime versions, dependencies, security, or compatibility requirements specified?
  7. Context Economy: Is the prompt concise and focused, without unnecessary information?
  8. Execution Preference: Is it clear whether the model should implement, propose, or just analyze?

Step 2: Identify Issues

Check for the following anti-patterns:

Anti-patternDescription
Vague instructionsLacks specificity like "make it better" or "improve it"
No verification methodMissing tests, screenshots, or expected output
No verification commandsMissing how to run tests or check outputs
Overly broad scopeAsking for too many things at once
Insufficient contextMissing file paths, error messages, or references to existing patterns
Symptom-only descriptionNot requesting investigation of root cause
Missing constraintsNo environment, dependency, or compatibility requirements
No exploration/planning cueComplex tasks not asking for exploration/planning first
Context bloatUnnecessary details that increase token usage
Ambiguous deliverableUnclear whether to plan, implement, or only analyze
Unclear response formatMissing brevity/structure expectations

Step 3: Create Improved Prompt

Apply best practices to create an improved version:

Add Verifiability

Before: "implement a function that validates email addresses"
After: "write a validateEmail function. test cases: user@example.com is true, invalid is false, user@.com is false. run the tests after implementing"

Add Specific Context

Before: "fix the login bug"
After: "users report that login fails after session timeout. check the auth flow in src/auth/, especially token refresh. write a failing test that reproduces the issue, then fix it"

Add Reference to Existing Patterns

Before: "add a calendar widget"
After: "look at how existing widgets are implemented on the home page to understand the patterns. HotDogWidget.php is a good example. follow the pattern to implement a new calendar widget with month selection and pagination"

Add Context Economy

Before: "here is a long unrelated history ... fix the dropdown"
After: "fix the dropdown in src/ui/Dropdown.tsx. issue: keyboard navigation skips items. keep the prompt focused; omit unrelated history"

Add Rich Context Inputs

Before: "build fails"
After: "build fails with this error: [paste error]. run the smallest relevant test command. if needed, read the build script and package config"

Add Explicit Exploration/Planning When Needed

Before: "refactor auth to support OAuth"
After: "first explore src/auth and summarize current flow, then propose a plan. after approval, implement with tests"

When information is missing, include explicit questions inside the improved prompt and do not assume defaults.

Example: Bugfix

Before: "search is broken"
After: "users report search returns empty results for queries with hyphens. reproduce in src/search/. paste the error log if any. write a failing test for 'foo-bar' returning results, fix the root cause, run: pnpm test --filter search"

Example: UI/Visual

Before: "make the dashboard look better"
After: "implement the attached screenshot for the dashboard header in src/ui/DashboardHeader.tsx. match spacing and typography. take a new screenshot and list any differences. run: pnpm lint"

Example: Refactor

Before: "clean up the auth code"
After: "inspect src/auth and list duplication hotspots. propose a refactor plan scoped to one module. after approval, remove duplication without changing behavior. add a targeted test if coverage is missing. run the smallest relevant test command"

Example: Research

Before: "why is this API slow?"
After: "explore request flow around src/api/. summarize likely bottlenecks with evidence (logs, timings). propose 2-3 hypotheses and what data is needed to confirm. do not implement yet. if needed, ask for access to profiling output"

Example: Ops

Before: "deployment failed"
After: "deploy fails with error: [paste log]. identify the failing step in scripts/deploy.sh and related CI config. suggest a fix and a rollback plan. run: ./scripts/deploy.sh --dry-run (if available)"

Example: Docs

Before: "update the README"
After: "update README.md to include install + dev steps based on existing scripts. keep it concise. confirm commands exist and match actual scripts"

Step 4: Output Format

Output in the following format:

## Prompt Analysis

### Original Prompt
[User-provided prompt]

### Issues
- [Issue 1 and its impact]
- [Issue 2 and its impact]
...

### Missing Information (Questions to Ask)
The following questions should be answered to make the prompt more effective:
- [Question 1] (Why it is needed)
- [Question 2] (Why it is needed)
...

### Verification Plan
- Commands to run (tests, build, lint, screenshots)
- Expected results or acceptance criteria

### Exploration/Planning Recommendation
- Recommend exploration/planning first? [Yes/No] and why

### Execution Preference
- Implement now / propose plan only / analyze only
- Any constraints on response length or format

### Improved Prompt
[The improved prompt]

### Document Improvement Suggestions (if applicable)
- [Issue and concrete improvement for the instruction document]
- [Issue and concrete improvement for the instruction document]

### Revised Document (optional)
[A revised draft of the instruction document when helpful]

### Improved Prompt Template (optional)
Use this as a fill-in template if the user wants a reusable prompt format:

[Task]

  • Goal:
  • Target files/paths (@...):
  • Constraints (runtime/version/deps/security/compat):
  • Context (symptom, logs, repro, links):

[Verification]

  • Commands:
  • Expected results:
  • UI checks (screenshots/visual diffs):

[Exploration/Planning]

  • Do exploration/planning first? (Yes/No) + reason:

[Execution Preference]

  • Implement now / plan only / analyze only
  • Output format (concise report, patch summary, checklist, etc.)

### Short Prompt Template (optional)
Use this when the user wants the shortest effective prompt:

Goal: ... Targets: path1/path2 Context: symptom + repro + logs Constraints: runtime/deps/compat Verify: command + expected result Explore/Plan: yes/no (why) Execute: implement / plan only / analyze only Output: concise format


### Improvement Points
1. [Explanation of improvement 1]
2. [Explanation of improvement 2]
...

### Additional Recommendations (optional)
- [Whether to do exploration/planning first]
- [Whether to parallelize or delegate investigation]
- [Whether preliminary research is needed]
- [Ways to reduce context usage]

Reference: Best Practices Checklist

Best practices to reference when improving prompts:

Provide Verification Methods

  • Include test cases
  • Specify expected output
  • For UI changes, request screenshot comparison
  • Add "run the tests" or "verify the build succeeds"
  • Include explicit commands to run

Explore → Plan → Implement Order

  • Use an exploration/planning phase first for complex tasks
  • Create a plan before implementation
  • Small fixes don't need a formal plan
  • When uncertain, ask clarifying questions before coding

Include Specific Context

  • Paste error messages
  • Specify existing patterns
  • Clarify edge cases
  • State environment constraints (runtime, language versions, dependencies)

Use Rich Inputs

  • Paste logs and stack traces
  • Provide URLs for docs or API references
  • Attach or paste large text outputs when needed

Manage Context Window

  • Keep prompts concise and focused
  • Remove unrelated history or speculation
  • Ask for missing info instead of guessing

Leverage Parallelism/Delegation

  • For research tasks: request parallel investigation or summaries
  • For code review: request an independent review pass
  • Keeps the main task focused

Patterns to Avoid

  • Vague instructions like "make it better" or "improve it"
  • Implementation requests without verification methods
  • Multiple unrelated tasks at once
  • Requesting changes to files not yet read
  • Large context dumps with no clear signal

FAQ

What does the prompt-improver skill do?

A skill for improving prompts by applying general LLM/agent best practices. When the user provides a prompt, this skill outputs an improved version, identifies missing information, and provides specific improvement points. Use when the user asks to "improve this prompt", "review this prompt", or "make this prompt better".

How do I install prompt-improver?

Run npx skills add https://github.com/dotneet/claude-code-marketplace --skill prompt-improver in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-improver/ (personal) or .claude/skills/prompt-improver/ (project).

Which AI agents can use prompt-improver?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is prompt-improver safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains prompt-improver?

It is published by dotneet in dotneet/claude-code-marketplace. It has 508 recorded installs.

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