Tuneloop

Hey HN,I think session transcripts written by coding agents like Claude Code and Codex are very interesting because they offer a detailed window into how work gets shipped. You can see the sequence of decisions that resulted in the final PR, what the agent got wrong, tools used etc. So I built a cli that analyzes these sessions and provides a local dashboard that shows what each session shipped (PRs, features), how much each PR cost, and recommendations for more effective usage.Concretely, it enriches each session with:- Outcome links: merged PRs, features shipped, files changed- Granular cost attribution to outcomes- Task complexity- Agent autonomy- Work type- Key decisions- Tool error categoriesand across sessions, identifies:- Agent rework / re-steer themes- Patterns of deviations from best practicesCombined with the data already in the transcript like model, agent harness and repo, this data lets you answer questions like:- How much of my AI spend went into PR #2, or feature X?- Are my agents getting more autonomous over time on complex tasks?- What's my success rate on repo X vs. repo Y (or any other dimension you care about?)Works with Claude Code, Codex, OpenCode, and Pi. Everything runs and stays on your machine; enrichments that need an LLM can use your own provider key or a local model.The repo is at https://github.com/tuneloop/tuneloop, and you can try it by running `npx tuneloop@latest analyze`.Some things that are in the works next:- Skill invocation analysis – was re-work needed after a skill invocation?- harness/model comparisons on merged PRs – creates swe-bench style tasks from merged PRs on internal repos to help compare model/harness choices.- a version that offers this visibility at the team level.I’d love to hear from folks here if you find this interesting. Do you look at session transcripts much (perhaps via `/insights` on Claude Code or otherwise)? what do you find useful to track and actionable?