Preflight is an MCP server for Claude Code that optimizes AI coding assistant interactions by triaging prompts, preventing token waste, and providing detailed performance analytics.

Can I search my past AI coding sessions with Preflight?

Yes, Preflight integrates LanceDB vector embeddings to power semantic search across months of your AI session history, making it easy to retrieve relevant context and solutions.

What insights does Preflight offer into AI usage?

Preflight provides 12-category scorecards for prompt discipline, token efficiency, and cost estimation, enabling developers to analyze trends, compare projects, and identify areas for improvement.

Which AI coding assistants does Preflight support?

Preflight is built as an MCP server specifically for Claude Code, enhancing interactions with Anthropic's Claude models through its advanced prompt optimization and analytics features.

How does Preflight reduce AI token waste?

It implements intelligent pre-flight checks on prompts, leveraging a triage decision tree and learning from past correction patterns to prevent vague instructions that lead to costly wrong-fix cycles.

Preflight

Name: Preflight
Author: preflight-dev

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Optimizes AI coding assistant interactions by implementing pre-flight checks on prompts, leveraging session history, and providing detailed performance analytics to prevent token waste.

Preflight

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Optimizes AI coding assistant interactions by implementing pre-flight checks on prompts, leveraging session history, and providing detailed performance analytics to prevent token waste.

Preflight acts as a powerful Model Context Protocol (MCP) server designed to enhance interactions with AI coding assistants like Claude Code. It addresses the significant problem of token wastage and inefficiencies caused by vague or ambiguous prompts, which often lead to costly wrong-fix cycles. By providing a suite of 24 specialized tools, Preflight performs rigorous checks on prompts, drawing upon cross-service contract awareness, historical session data, and learned correction patterns to ensure clarity and precision before any AI-driven work begins. This proactive approach helps developers save tokens, improve output quality, and streamline their AI-assisted development workflow.

주요 기능

01Offers 12-category scorecards for prompt discipline, token efficiency, and cost estimation.

02Learns and warns about past correction patterns to avoid repeating mistakes.

03Extracts and searches cross-service contracts (types, interfaces, routes, schemas) across projects.

04Provides semantic search across months of AI session history using LanceDB vector embeddings.

05Unified `preflight_check` entry point for automated prompt triage and tool chaining.

060 GitHub stars

사용 사례

01Preventing token waste and improving efficiency by catching ambiguous prompts before AI execution.

02Leveraging historical session data and project-specific contracts for informed AI interactions.

03Enhancing the specificity and context of prompts for AI coding assistants like Claude Code.