Rudel – Claude Code Session Analytics
We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time. So we built an analytics layer for it. After connecting our own sessions, we ended up with a dataset of 1,573 real Claude Code sessions, 15M+ tokens, 270K+ interactions. Some things we found that surprised us: - Skills were only being used in 4% of our sessions - 26% of sessions are abandoned, most within the first 60 seconds - Session success rate varies…
Rudel – Claude Code Session Analytics launched on March 12, 2026 with 144 votes, #255 of 23,380 launches that month and more than 95% of that year's launches.
In plain words
Rudel is an analytics platform that provides visibility into Claude Code sessions by tracking efficiency, abandonment rates, and performance metrics. It analyzes session data to identify patterns such as skill usage, task-type success rates, and early error indicators that predict abandonment. Built after the creators realized they lacked insight into their own daily Claude Code usage, the tool processes real session data to establish benchmarks for agentic session performance and help users understand which sessions succeed and why.
written from the facts on this page · September 2026
From the sources
Opaline: CLI for Claude Code and Codex session analytics. Formerly rudel. - opalinehq/cli
Capture Claude Code and OpenAI Codex sessions and upload them to Opaline for team analytics. The production API remains at https://app.rudel.ai during the Opaline migration. npm install --global @opalinehq/cli opaline login opaline upload opaline login opens a browser-based device flow. opaline upload groups local worktrees by repository, lets you choose which repositories stay synchronized, installs the required Claude Code and/or Codex hooks, and uploads new sessions. opaline enable remains a shortcut for enabling the current repository. Existing rudel users do not need to log in again. Opaline intentionally reads the existing ~/.rudel credential and state directory, and the rudel package…from github.com
In the maker’s words, at launch
We built rudel.ai after realizing we had no visibility into our own Claude Code sessions. We were using it daily but had no idea which sessions were efficient, why some got abandoned, or whether we were actually improving over time. So we built an analytics layer for it. After connecting our own sessions, we ended up…
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Claude Code & Codex Usage Trading Cards by RudelMay 2026 · opaline.so · ▲169Get your trading card based on your CC & codex usage
Claude Code Usage Monitor – real-time tracker to dodge usage cut-offs2025 · github.com · ▲245I kept slamming into Claude Code limits mid-session and couldn’t find a quick way to see how close I was getting, so I hacked together a tiny local tracker. Streams your prompt + completion usage in real time Predicts whether you’ll hit the cap before the session ends Runs 100 % locally (no auth, no server) Presets for Pro, Max × 5, Max × 20 — tweak a JSON if your plan’s different GitHub: https://github.com/Maciek-roboblog/Claude-Code-Usage-Monitor It’s already spared me a few “why did my run just stop?” moments, but it’s still rough around the edges. Feedback, bug…
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Rudel – Claude Code / Codex sessions reveals 9 types of AI coderMay 2026 · app.rudel.ai · ▲5Claude Code / Codex session metadata can actually tell a story about how you work with AI coding agents. 50 days ago we posted about analyzing 1.6k Claude Code sessions from our own team. Skills were used in 4% of sessions, 26% were abandoned early, and we had no real benchmark for what good looked like. Now across 20k+ sessions, we started looking at behavior patterns from derived session metadata: consistency, intensity, session shape, repo breadth, output, cost intensity, and model range. Nine archetypes fell out, which we turned into playful cards. We built a Spotify Wrapped meets…
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