?? Promptimprover: MCP-First Prompt Governance

Promptimprover is an elite Model Context Protocol (MCP) server that acts as an intelligent prompt governance layer. It intercepts, classifies, and refines raw engineering prompts using deep repository context before they ever reach an LLM, ensuring maximum execution success.

?? Elite Features

• Universal Refiner MCP: Runs cross-CLI and editor-agnostic via the official MCP specification.
• Intelligent Governance: Evaluates raw prompts against past failures and auto-injects missing context.
• Zero-Knowledge Evidence Engine: Tracks execution outcomes and uses RLAIF (Reinforcement Learning from AI Feedback) to refine prompts dynamically.

? Quickstart

1. Ensure Node 22 is installed.
2. Build the MCP Server: \\�ash npm install && npm run build \\
3. Link to your MCP client: \\�ash npm run link-mcp \\

• prompt governance before code execution
• MCP-based integration instead of editor-specific glue
• evidence-backed refinement using history, tests, and repo context

What It Demonstrates

• MCP integration: the active implementation is the universal-refiner package, a TypeScript MCP server for cross-CLI prompt refinement
• Governance pipeline: prompts can be captured, classified, refined, and linked to execution outcomes instead of being treated as disposable chat
• Repo-aware context: detectors, memory, and retrieval components adapt refinement to the current codebase
• Proof-oriented design: tests and architecture docs emphasize traceability, learning, and operational visibility rather than prompt rewriting alone

Features

• RAG snippets: FlexSearch-based retrieval over the local codebase to inject relevant examples into prompt refinement
• Persistent memory: SQLite-backed storage for reusable rules, learned patterns, and prompt history
• Context scouting: detectors identify language, framework, and architectural signals at startup
• Operational traceability: history, timelines, and prompt-to-outcome correlation are first-class design goals

Current Scope vs. Roadmap

The repo contains both implemented components and forward-looking architecture.

• Implemented now: the universal-refiner MCP server, Gemini-oriented packaging, tests, and install/build scripts
• Designed for later expansion: broader routing, portal, and evidence workflows described in the architecture spec

That distinction matters because this repo is about credible system direction, not vague AI middleware claims.

Architecture Snapshot

flowchart LR
    CLI["AI CLI\n(Claude / Cursor)"] -->|"stdio"| PI["PromptImprover\n(universal-refiner)"]
    subgraph internal["PromptImprover Engine"]
        RAG["RAG Snippets\n(FlexSearch)"]
        Memory["SQLite Memory\n(LocalBrain)"]
        AutoHeal["Auto-Heal\n(BackgroundService)"]
    end
    PI --> RAG
    PI --> Memory
    PI --> AutoHeal
    internal --> Out["Augmented Prompt"]

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Proof Points

• Portfolio proof notes
• Architecture spec
• Operator testing guide
• Enterprise release gates
• universal-refiner/package.json
• universal-refiner/tests

Quickstart

git clone https://github.com/Coding-Autopilot-System/Promptimprover.git
cd Promptimprover
.\build_and_install.ps1

On Linux or macOS:

git clone https://github.com/Coding-Autopilot-System/Promptimprover.git
cd Promptimprover
./build_and_install.sh

Both installers perform a deterministic dependency install, run the full test suite, build the package, install it globally, and verify the universal-refiner command. Add that command to your MCP client configuration. See the Setup Guide for full configuration instructions.

For optional automatic pre-prompt linting and post-execution recording, see the cross-CLI automation guide. Claude Code and Gemini CLI expose the required lifecycle hooks. Codex currently requires MCP-first instructions or explicit helper invocation because its hook lifecycle does not transparently intercept each prompt.

Local Semantic Model

PromptImprover uses a local OpenAI-compatible endpoint before optional MCP sampling. The safe defaults target http://localhost:9000/v1, use gemma3:12b first, and fall back to gemma3:1b. If neither local model nor MCP sampling is available, rule-based refinement continues without semantic output.

Override the defaults per repository with .universal-refiner.json:

{
  "semantic": {
    "localEnabled": true,
    "mcpSamplingEnabled": true,
    "baseUrl": "http://localhost:9000/v1",
    "models": ["gemma3:12b", "gemma3:1b"],
    "timeoutMs": 120000,
    "temperature": 0.2,
    "allowNonLoopback": false
  }
}

Non-loopback model endpoints are rejected unless allowNonLoopback is explicitly enabled. Generated lessons and templates remain pending until reviewed through the MCP learning-review tools.

License

MIT - see LICENSE