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Prompt Improver

Organization
existential-birds
prompt-improver

Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.

Overview

Publisherexistential-birds
Repositorybeagle
Skill nameprompt-improver
Stars
82
Forks
8
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Prompt Improver AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-core/skills/prompt-improver .claude/skills/prompt-improver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prompt Improver in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prompt Improver on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prompt Improver is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prompt Improver

Optimize code-related prompts for clarity, investigation-first thinking, and verification.

Input

$ARGUMENTS

Step 1: Analyze the Prompt

Evaluate the input prompt across these dimensions:

DimensionWhat to check
Task ClarityIs the task type clear? (implement, fix, refactor, review, test) Are boundaries defined?
InvestigationDoes it specify reading/understanding before acting?
VerificationAre there appropriate checks? (run tests, build, lint)
Context AnchoringDoes it reference specific files, functions, or patterns?
Action SpecificityIs the desired outcome explicit? Quality expectations stated?
Scope ControlIs it appropriately scoped? Clear stopping points?

Identify which dimensions are weak or missing in the input prompt.

Gates (sequenced)

Complete in order; do not skip steps.

  1. Audit gate (end of Step 1): Pass when the forthcoming Analysis names the task type and either lists each weak or missing dimension from the table or explicitly states all dimensions are adequate, with a brief reason for any dimension you treat as already sufficient.
  2. Transform gate (Step 2): Pass when every improvement you will list under "Improvements Applied" maps to a gap from the audit (or to deliberate strengthening when the prompt was already adequate).
  3. Output gate (Step 3): Pass when the Optimized Prompt block appears only after Gates 1–2 pass.

Step 2: Apply Transformation Rules

Task Clarity

  • Convert vague requests → specific task type + scope
  • Add "implement", "fix", "refactor", "review", or "test" when ambiguous
  • Specify affected files/components when inferable

Investigation-First

  • Add "Read and understand [relevant files] before making changes"
  • For bugs: "Reproduce and understand the root cause first"
  • For features: "Check existing patterns in the codebase"

Anti-Hallucination

  • "Do not assume code structure you haven't read"
  • "Verify imports/dependencies exist before using them"
  • "Check for existing utilities before creating new ones"

Verification Steps

  • For implementation: "Run tests after changes"
  • For refactoring: "Ensure behavior is unchanged"
  • For fixes: "Verify the fix and check for regressions"

Positive Framing

  • Convert "don't break tests" → "ensure tests pass after changes"
  • Convert "don't over-engineer" → "implement the minimal solution"

Scope Control

  • Add explicit boundaries when missing
  • Break multi-part requests into phases if needed

Examples

Bug Fix

Original: "fix the login bug"

Optimized:
Fix the login bug. First, read the authentication-related files to understand
the current implementation. Reproduce the bug to confirm the issue. Identify
the root cause before making changes. Implement the fix and run existing tests
to verify no regressions.

Feature Implementation

Original: "add dark mode"

Optimized:
Implement dark mode for the application. First, check how theming/styling is
currently handled in the codebase. Look for existing color variables or theme
patterns. Implement the toggle and theme switching with minimal changes to
existing components. Run tests and verify the UI renders correctly in both modes.

Refactoring

Original: "clean up the user service"

Optimized:
Refactor the user service for improved maintainability. Read the current
implementation and understand its responsibilities. Identify specific issues
(duplication, unclear naming, tight coupling). Make incremental changes,
running tests after each step to ensure behavior is unchanged.

Task-Type Tips

Task TypeTip
Bug fixesInclude reproduction steps if known. Specify whether this is a quick fix or needs root cause analysis.
Feature implementationReference similar features in the codebase. Specify if tests are expected.
RefactoringState the goal (readability, performance, testability). Emphasize incremental changes.
Code reviewSpecify focus areas (security, performance, style). Mention what to ignore.
TestingSpecify test type (unit, integration, e2e). Reference existing test patterns.

Step 3: Generate Output

Follow the Gates under Step 1 (audit → transform → output). Produce output in this exact format:

Analysis

[2-3 sentences identifying the prompt type, which dimensions are weak or missing, or why all dimensions are already adequate]

Improvements Applied

  • [Bullet list of specific transformations applied]

Optimized Prompt

[The improved prompt, ready to copy and use]

Tips for This Prompt Type

[1-2 sentences of relevant tips from the Task-Type Tips table]

Frequently asked questions

What does the Prompt Improver AI skill do?

Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.

Why use Prompt Improver on TypingMind?

Because you install it once and use it with any model. Prompt Improver is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prompt Improver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-core/skills/prompt-improver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prompt Improver?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prompt Improver?

As many as you like. As long as a model supports skills, you can use Prompt Improver with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prompt Improver AI skill free?

Yes. It is published on GitHub by existential-birds under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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