Codesight: AI Context Engine Cuts 30K-60K Tokens from Claude Code Sessions
What Codesight Does
Codesight is a GitHub project that creates structured context for AI coding agents like Claude Code and Cursor. Instead of forcing the AI to read through entire repositories to understand code structure, Codesight pre-processes the codebase and provides a map of routes, database schemas, components, environment variables, and hot files.
Key Features from the Source
- Smart parsing for TypeScript, Python, and Go that understands actual routes, models, and components instead of guessing
- AST parsing for Next.js + Prisma added through developer collaboration
- Eval suite with ground truth for testing
- Token telemetry to measure usage
- Config + profiles for Claude Code and Cursor
- HTML report and MCP server
- Blast radius feature: shows exactly which files, routes, and tests depend on a given file, allowing Claude to answer "if I change this, what breaks?" without guessing
Development Process
A developer found Codesight while searching for solutions to stop Claude Code from wasting tokens understanding repositories. After emailing the maintainer with specific pain points from their TypeScript/Next.js project, they engaged in rapid iteration: the developer ran Codesight on their repo, sent concrete issues like "this Next.js route detection missed X" or "the Prisma schema parsing needs Y," the maintainer shipped updates, and the developer retested and provided more feedback.
This collaboration transformed Codesight from a "rough script" to a tool the developer plans to use daily for their Claude Code workflow.
Performance Impact
The developer reports being "90% convinced" that Codesight cuts the "wasted exploration" phase that previously consumed 30,000-60,000 tokens per deep session on their project.
📖 Read the full source: r/ClaudeAI
👀 See Also
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Forge: A Memory System for Claude Code Built with Claude Code
A developer built Forge, a TypeScript monorepo MCP server that automatically captures decisions, constraints, and rejections from Claude Code conversations. It uses a six-stage pipeline to classify, extract, and persist structured data in an event-sourced SQLite model.