ContextOS CLI helps developers select relevant code for AI agents

I built a "context OS" that stops AI agents from drowning in your codebase

The problem every AI coding session hits You open Claude or Copilot, paste in your task, and immediately hit the wall: the codebase is too big. You either: Dump everything and burn 80% of your context window on irrelevant files Hand-pick files and miss the one import that breaks everything Pay for a bigger context window and repeat the problem at scale I got tired of this and built ContextOS — a local CLI that acts as an intelligent context layer between your repo and your AI agent. What it does pip install rm-contextos cd your-project contextos scan contextos pack --task "add rate limiting to the auth endpoint" --budget 8000 Output: a Markdown (or JSON) context pack with only the files that matter for that task — ranked by keyword match, import graph centrality, AST symbol overlap, and git churn. Secrets redacted automatically. Token savings report on every pack: Packed 12 files · ~6,840 tokens · saved ~47,200 tokens (87%) vs full repo How ranking works Five signals combine into a score per file: Signal What it catches Keyword match Files whose content/name overlap with your task Import graph centrality Files that everything else imports (critical shared modules) AST symbol overlap Function/class names, not just grep strings Git churn score Recently modified files are probably active code Secret penalty Credential files silently excluded No LLM calls. No cloud. Fully offline. MCP server (for Claude Desktop / Claude Code) pip install "rm-contextos[mcp]" contextos serve --stdio Register in claude_desktop_config.json and your AI agent can call pack_context, scan_repo, list_files, get_file, churn_report directly as tools — no CLI needed. What's shipped 980 tests, 96% coverage Apache-2.0, no telemetry, no accounts Python 3.11–3.13, Linux + macOS Export formats: Claude, Codex, Cursor, Aider, JSON Incremental scan cache — re-scans only changed files pip install rm-contextos pip install "rm-contextos[mcp]" # + MCP server pip install "rm-contextos[all]" # everything GitHub: https://github.com/Rohithmatham12/ContextOS Docs: https://Rohithmatham12.github.io/ContextOS/ Would love feedback — especially on the ranking signals and MCP integration. What signals are you missing?

2 hours ago

I built a tool that cuts Claude/ChatGPT token usage by 97% — here's how it works

The Problem You're debugging a bug. You open Claude. You paste 10 files. You hit the context limit. Or worse — you paste the wrong files and Claude gives you a useless answer. This happens because most repos have hundreds of files but you only need 10-15 for any given task. What I Built ContextOS — it scans your entire repo, ranks files by relevance to your current task, and exports a perfectly-sized context pack. Secrets are automatically redacted. No cloud. No accounts. Real Numbers Tested on FastAPI (2,811 files): bash git clone https://github.com/tiangolo/fastapi cd fastapi pip install rm-contextos contextos scan contextos pack --task "add rate limiting to auth endpoint" Output: Without ContextOS : ~284,000 tokens (2,811 files) With ContextOS : ~7,998 tokens (15 files) Saved : ~276,002 tokens (97% reduction) How It Works 1. contextos scan — indexes every file: imports, exports, symbols, purpose 2. contextos pack --task "your task" — ranks files using: - Keyword matching against your task description - Import graph centrality (files imported by many others rank higher) - AST symbol extraction (matches function/class names) - Git churn scoring (recently modified files rank higher) 3. Fills the token budget greedily, redacts 14 secret patterns, exports MCP Server It also runs as an MCP server so Claude Desktop/Code can call it directly: pip install "rm-contextos[mcp]" contextos serve --stdio /path/to/project Claude then has tools: pack_context, list_files, get_file, churn_report. Install pip install rm-contextos # base pip install "rm-contextos[all]" # + MCP + AST + headroom compression GitHub: https://github.com/Rohithmatham12/ContextOS Docs: https://Rohithmatham12.github.io/ContextOS/ Apache-2.0. Fully local. PRs welcome.

7 hours ago