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

For teams

Drop ctxray into your CI pipeline as a prompt quality gate. Rule-based, <50ms per prompt, no LLM calls, no API keys, no data leaves your infrastructure.

# .github/workflows/prompt-quality.yml
- uses: ctxray/ctxray@main
  with:
    score-threshold: 50
    comment-on-pr: true

• Deterministic — same prompt, same score, every run. Works in pre-commit and CI budgets.

• Air-gapped by default — no external API calls, runs in offline/private networks.

• Independent — MIT licensed, no vendor lock-in. Promptfoo joined OpenAI. Humanloop joined Anthropic. ctxray is open-source infrastructure that stays yours.

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.

ctxray rewrite — rule-based prompt improvement

ctxray build — assemble prompts from components

What a bad prompt looks like

All commands

Discover your patterns

Optimize your prompts

Manage

Supported AI tools

Installation

pip install ctxray              # core (all features, zero config)
pip install ctxray[chinese]     # + Chinese prompt analysis (jieba)
pip install ctxray[mcp]         # + MCP server for Claude Code / Continue.dev / Zed

Auto-scan after every session

ctxray install-hook             # adds post-session hook to Claude Code

Browser extension

Capture prompts from ChatGPT, Claude.ai, and Gemini directly in your browser. Live quality badge shows prompt tier as you type — click "Rewrite & Apply" to improve and replace the text directly in the input box.

1. Install the extension from Chrome Web Store or Firefox Add-ons

2. Connect to the CLI: ctxray install-extension

3. Verify: ctxray extension-status

Captured prompts sync locally via Native Messaging — nothing leaves your machine.

CI integration

GitHub Action

# .github/workflows/prompt-lint.yml
name: Prompt Quality
on: pull_request

jobs:
  lint:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
      - uses: ctxray/ctxray@main
        with:
          score-threshold: 50
          strict: true
          comment-on-pr: true

pre-commit

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/ctxray/ctxray
    rev: v3.0.0
    hooks:
      - id: ctxray-lint

Direct CLI

ctxray lint --score-threshold 50  # exit 1 if avg score < 50
ctxray lint --strict              # exit 1 on warnings
ctxray lint --json                # machine-readable output

Project configuration

ctxray init   # generates .ctxray.toml with all rules documented

# .ctxray.toml (or [tool.ctxray.lint] in pyproject.toml)
[lint]
score-threshold = 50

[lint.rules]
min-length = 20
short-prompt = 40
vague-prompt = true
debug-needs-reference = true

Prompt Science

Scoring is calibrated against 10 peer-reviewed papers covering 30+ features across 5 dimensions:

Cross-validated findings that inform our engine:

• Position bias is architectural — present at initialization, not learned. Front-loading instructions is effective for prompts under 50% of context window (3 papers agree)

• Moderate compression improves output — rule-based filler removal doesn't just save tokens, it enhances LLM performance (2505.00019)

• Prompt quality is independently measurable — prompt-only scoring predicts output quality without seeing the response (ACL 2025, 2503.10084)

All analysis runs locally in <1ms per prompt. No LLM calls, no network requests.

How it works

 Data sources:
 ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
 │Claude Code│ │  Cursor  │ │  Aider   │ │ ChatGPT  │ │ 5 more.. │
 └─────┬────┘ └─────┬────┘ └─────┬────┘ └─────┬────┘ └─────┬────┘
       └─────────────┴───────────┴─────────────┴─────────────┘
                                 │
                    scan -> dedup -> store -> analyze
                                 │
              ┌──────────────────┼──────────────────┐
              v                  v                  v
        ┌──────────┐     ┌──────────────┐    ┌──────────┐
        │ insights │     │  patterns    │    │ sessions │
        │ wrapped  │     │  repetition  │    │ projects │
        │ style    │     │  privacy     │    │ agent    │
        └──────────┘     └──────────────┘    └──────────┘

Key design decisions:

• Pure rules, no LLM — scoring and rewriting use regex + TF-IDF + research heuristics. Deterministic, private, <1ms per prompt.

• Adapter pattern — each AI tool gets a parser that normalizes to a common Prompt model. Adding a new tool = one file.

• Two-layer dedup — SHA-256 for exact matches, TF-IDF cosine similarity for near-dupes.

• Research-calibrated — 10 peer-reviewed papers inform the scoring weights.

Conversation Distillation

ctxray distill scores every turn in a conversation using 6 signals:

• Position — first/last turns carry framing and conclusions

• Length — substantial turns contain more information

• Tool trigger — turns that cause tool calls are action-driving

• Error recovery — turns that follow errors show problem-solving

• Semantic shift — topic changes mark conversation boundaries

• Uniqueness — novel phrasing vs repetitive follow-ups

Session type (debugging, feature-dev, exploration, refactoring) is auto-detected and signal weights adapt accordingly.

Why ctxray?

After Promptfoo joined OpenAI and Humanloop joined Anthropic, ctxray is the independent, open-source alternative for understanding your AI interactions.

• 100% local — your prompts never leave your machine

• No LLM required — pure rule-based analysis, <50ms per prompt

• 9 AI tools — the only tool that works across Claude Code, Cursor, ChatGPT, and more

• Research-backed — calibrated against 10 peer-reviewed papers, not vibes

Previously published as reprompt-cli. Same tool, new name, clean namespace.

Privacy

• All analysis runs locally. No prompts leave your machine.

• ctxray privacy shows exactly what you've sent to which AI tool.

• Optional telemetry sends only anonymous feature vectors — never prompt text.

• Open source: audit exactly what's collected.

Links

• PyPI: ctxray

• Chrome Extension: Chrome Web Store

• Firefox Add-on: Firefox Add-ons

• Changelog: CHANGELOG.md

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT