What the source did
- Pavel is a product manager in tech; astronomy is his hobby. He was going through public data from NASA's TESS telescope with Claude Code when one star, TIC 4206066, about 116 light-years away, looked odd: every 3.18 days it dims by about 0.05% for roughly two hours.
- He first saw it in late-2025 data, then found the same dip in separate TESS observations from 2020 and 2018. If it's a planet, it's about 1.4 times the size of Earth.
- Claude Code (he names Opus 5.5 and Fable 5.1) did most of the work: downloading and parsing the data, writing the search code, fitting the transits, checking nearby stars, looking for secondary eclipses and other false positives, making plots and rerunning when a test failed.
- He chose the questions, which tests mattered and what counted as pass or fail. Independent reviews came from Codex and from separate fresh agent sessions with read-only access.
- In about two weeks: 74 separate analyses and more than 1,000 scripts. His lesson: don't ask the agent whether it's a planet; keep giving it ways to prove it isn't.
- His favourite test: hide one whole year of observations, work out the orbit from the other two, predict where the hidden year's transits should fall, and only then look. He ran it three times (2018, 2020, 2025) and all three matched.
- An audit caught a statistical claim that looked stronger than it was, so he removed it and redid that part. The main signal held.
- He searched 36 catalogs and literature sources and 340,505 automatic TESS planet-search alerts and didn't find either signal reported. A weaker second signal (about 2.2 Earth radii, 11.13 days) is treated separately.
- He submitted a TESS observing proposal; it was approved as program #100, for Oct 31 to Nov 26. On Oct 6, before that data existed, he published the exact predicted transit times and pass/fail rules.
- Update, Nov 10: on Nov 6 the TESS team added the star as TOI 7930, with both signals as planet candidates. Their periods differ from his by 0.29 and 1.6 seconds; their sizes (1.30 and 2.04 Earth radii) are close to his 1.4 and 2.2. The new data should be public around January.
- He published a preprint and the supporting data and code on Zenodo. In a separate X thread he shared his Codex /goal workflow as a gist.
“It's to keep giving it ways to prove that it isn't.”— Pavel Rabtsevich (u/This_Cell_1829, r/ClaudeAI; @p_rabtsevich on X), Reddit
Summary written by Agent Cookbook from the original post. See the original for the full context. How we source recipes
Our notes
- What it takes
- Claude Code for the analysis (he names Opus 5.5 and Fable 5.1), Codex and fresh read-only sessions as reviewers, public NASA TESS data, and your own pass/fail rules. His data, code and Codex /goal gist are linked.
- Where it breaks
- Asking the agent whether a result is real instead of how it could be wrong, letting the same session review its own work, keeping a claim that only looks strong, and calling a candidate a discovery.
- Human checkpoint
- You set the tests and pass/fail rules. Reviewers stay read-only, and you alone submit proposals, publish predictions or preprints, and post results.
- Tools involved
- Claude Code (he names Opus 5.5 and Fable 5.1), Codex and fresh agent sessions with read-only access, as independent reviewers, Public NASA TESS light curves, Your own list of tests and pass/fail rules
✓ Verified Source link checked and still live. Notes by Thomas, editor. How we verify
Get their template
Made and published by Pavel Rabtsevich. Start here; it is their original, not ours.
Repo · by Pavel RabtsevichHis supporting data and code (Zenodo)Published Sep 25, 2026 with his preprint. README plus three archives: inputs and retrieval scripts, per-transit measurements, the hidden-year recovery test, false-positive runs, pixel localisation and the figure data. Code is MIT, data CC BY 4.0.Open it ↗Template · by Pavel RabtsevichHis Codex /goal workflow gistLinked from his Oct 28 X thread. A short guide and two templates: one has GPT turn your notes into a full task prompt, the other starts /goal in Codex with checks, stop conditions and saved progress. He calls them general adaptations, not his private prompts.Open ↗
Our adapted version
Ours Our version, built from his posts. His own code and research workflow are linked above. Fill in the [BRACKETS].
You are my research analyst. Your job is to try to kill my hypothesis, not to confirm it. Report only; I decide what gets published or submitted.
RULES
- Never submit, publish, post or email anything, and never contact anyone. Sending, buying, posting and deleting always need my yes.
- Save every script, input file and output in [project folder] so any step can be rerun.
- Don't claim a result is significant unless the test we agreed says so. If a check can't be run, say so.
1. SETUP
- Hypothesis: [e.g. a periodic dip in this star's light curve].
- Data: [public source and observation windows].
- Before running anything, list the tests that could prove it wrong, with pass/fail rules. Wait for me to agree.
2. RUN
- Download and parse the data, write the search code, fit the signal, and run each agreed test.
- Include a hold-out test: hide one full observation window, fit using the others, predict the hidden window, then check only at those times.
- Check the obvious look-alikes: nearby sources, instrument artefacts, secondary events, anything in published catalogs.
3. REPORT
- One table: test, rule, result (pass / fail / couldn't run), the file that shows it.
- List what the checks couldn't settle and what new data would.
4. REVIEW (separate session)
- A fresh reviewer session or a second agent, with read-only access, rereads the scripts and the report and lists every claim it thinks is overstated.
Approval rule
🛡
In our version the agents analyse and report only. You choose the tests and pass/fail rules, and you alone submit proposals, publish preprints or predictions, post results or contact astronomers. Reviewers get read-only access. Sending, buying, posting and deleting always need a human.
Set it up
- Pick one question in public data and write down, before you look further, what would prove it wrong.
- Have Claude Code fetch the data, write the search and run each agreed test, saving every script.
- Hide one observation window, predict it from the others, then check it. Repeat for each window.
- Give Codex or a fresh session read-only access to audit the analysis, and remove any claim it can break.
- Search the catalogs and alerts yourself to see if anyone already reported it.
- If it survives, publish your predictions and pass/fail rules before new data arrives, and submit anything yourself.
Common failures
⚠
- Asking the agent "is this real?" You get a confident yes. Ask it for ways the result could be wrong.
- Letting the same session that wrote the analysis review it. His reviewers were Codex and fresh read-only sessions.
- Keeping a claim that only looks strong. His audit caught one and he redid that part.
- Treating a candidate as a discovery. These are still candidates until the new data and follow-up say otherwise.
- Posting results the agent wrote up without reading them yourself; he said parts of his first post were written with AI help.
Variations
- Use the same hold-out test on any time-series hunch: predict a hidden window before you look at it.
- Run the long analysis in Codex /goal with his gist: agree on the task list and what "done" means, then let it work and stop when it needs you.
- Make the review a standing rule: every claim gets a read-only second agent before it goes in a write-up.
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