context-os
Claude Code's first turn opens the right file instead of grepping for it. A 400-line Python hook builds a static graph of your repo (symbols + imports + git-hot files) and injects ranked file:line candidates into the prompt before Claude's first turn — so it skips the Glob → Grep → Read → Read → Read hunt and goes straight to the file.
No embeddings. No server. No model call. ~50 ms.
What's proven vs. what to expect. Retrieval quality is measured and CI-gated (MRR 0.984 synthetic, 0.756 on this repo, beats BM25 — see below). A --print A/B showed −40.9% tokens (N=36, cold-cache, earlier build, p=5e-7). Honest caveat: that bench is cold-cache one-shots; in a warm interactive session, prompt caching makes re-sent context cheap, so your dollar savings are smaller than 40.9% — the durable wins are fewer first-turn tool calls and hitting context limits/compaction later. Don't take our number — replay it on your own history (no API key): python3 python/evals/runners/replay_history.py.
Need this applied to a private repo this week? I have 2 paid audit slots open. Fund the $1,000 AI Agent Cost Leak Audit or read the audit scope. The OSS tool stays free; the sprint is for teams that want a private report, CI leak gate, and one concrete repo/workflow patch.
curl -fsSL https://raw.githubusercontent.com/sravan27/context-os/main/setup.sh | bash
60-second demo: graph stats → autocontext block with import counts → cross-repo eval (auto_context 0.545 winning) → 9/9 CI floors PASS. Reproduce with bash docs/distribution/demo.sh.
Private repo audit
If your team is already spending heavily on Claude Code, Codex, Cursor, or other coding agents and wants a private cost-leak report, I am doing 2 paid 48-hour audits this week: AI Agent Cost Leak Audit.
Payment starts the slot. After checkout, send repo/access details by email or private intake; do not paste private code into a public issue.
The open-source hook stays MIT and free. The paid audit is for teams that want the same measurement discipline applied to their own repo, prompts, and agent workflows.
What the paid sprint ships:
- a private repo scorecard using the same leak signals as the Action
- a short report on the highest-cost agent loops and file-noise sources
- one concrete CI, ignore-rule, or repo-guidance patch where the fix is clear
- a handoff note your team can reuse when running Claude Code, Codex, Cursor, or internal agents
Quick local preview:
python3 python/agent_cost_leak_check.py --repo . --json
CI recipe: docs/AGENT-COST-LEAK-CHECKER.md. For public intake without sharing private code, use the private audit request template.
Versioned GitHub Action:
- uses: sravan27/[email protected]
with:
max-score: "40"
The number
Live A/B on 36 real claude --print calls, identical fixture, identical model, only difference is whether the hook is active:
| Metric | Value |
|---|
| Aggregate tokens | −40.9% |
| Prompt-level wins | 6/6 |
| Bootstrap 95% CI | 32.7%–48.9% |
| Paired t-test | p = 5.1e-7 |
| Wall-clock | −35.3% |
Read this number honestly: these are cold-cache --print one-shots (Claude Code pays full cache creation per call — see METHODOLOGY.md). That's the right setup to isolate the hook's effect, but it's not your warm interactive session: there, prompt caching makes re-sent context cheap, so the dollar delta is smaller. It's also N=36 on one repo, on an earlier build. Treat it as "the hook clearly removes first-turn exploration," not "you'll save 41% on your bill." The version-independent, CI-gated claim is the retrieval quality below.
Raw JSON for every call: python/evals/reports/live-session-bench-raw.json · methodology: docs/METHODOLOGY.md.
