ctxray

See how you really use AI.

X-ray your AI coding sessions across Claude Code, Cursor, ChatGPT, and 6 more tools. Discover your patterns, find wasted tokens, catch leaked secrets — all locally, nothing leaves your machine.

Quick start

pip install ctxray

ctxray scan                    # discover prompts from your AI tools
ctxray wrapped                 # your AI coding persona + shareable card
ctxray insights                # your patterns vs research-optimal
ctxray privacy                 # what sensitive data you've exposed

Works in your pipeline

Drop ctxray into your CI as a prompt quality gate. No LLM, no API key, no network — <50ms per prompt.

# .github/workflows/prompt-quality.yml
- uses: ctxray/ctxray@main
  with:
    score-threshold: 43    # experimentally validated quality threshold
    model: claude          # model-specific rules (claude/gpt/gemini)
    comment-on-pr: true

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/ctxray/ctxray
    rev: v3.0.0
    hooks:
      - id: ctxray-lint-score   # fail below quality threshold
      # or: id: ctxray-lint-claude  # Claude-specific rules + threshold

• Deterministic — same prompt, same score, every run. No flaky LLM-based checks.
• Air-gapped — runs in offline and private networks. All analysis stays on your infrastructure.
• Configurable — .ctxray.toml or [tool.ctxray.lint] in pyproject.toml. Per-project rules.

Full setup: GitHub Action · pre-commit · .ctxray.toml

What you'll discover

Your AI coding persona

ctxray wrapped generates a Spotify Wrapped-style report of your AI interactions — your persona (Debugger? Architect? Explorer?), top patterns, and a shareable card.

Your prompt patterns

ctxray insights compares your actual prompting habits against research-backed benchmarks. Are your prompts specific enough? Do you front-load instructions? How much context do you provide?

Your privacy exposure

ctxray privacy --deep scans every prompt you've sent for API keys, tokens, passwords, and PII. See exactly what you've shared with which AI tool.

Full prompt diagnostic

ctxray check "your prompt" scores, lints, and rewrites in one command — no LLM, <50ms.

Experimentally validated on 3000+ LLM calls across 8 models (1.5B → 27B): prompts at or above score 43 hit ~93% pass rate on executable code tests. Below 43 they average 72% or lower. ctxray tells you which side you're on and what to fix — see experiments/RESULTS.md for the full cross-model data.

ctxray check "fix the auth bug in login.ts"        # threshold pass/fail + diagnostics
ctxray check "fix bug" --model claude               # model-specific scoring for Claude
ctxray check "refactor middleware" --threshold 50    # custom threshold for stricter teams

More screenshots

ctxray rewrite — rule-based prompt improvement

ctxray build — assemble prompts from components

What a bad prompt looks like

<img src="docs/screenshots/check

... View full README on GitHub