Planet Review

Project: Planet Review

Self-reported basis: The description is entirely self-reported by the author, Jethro Moore, and unverified. No third-party corroboration, revenue, customer data, or traction evidence is present.

What it appears to be: A software tool for astronomy learners and reviewers that organizes transit-signal evidence into cautious, reproducible reviews using a deterministic offline benchmark workflow. It includes a Streamlit UI, CLI, JSON/Markdown reporting, automated tests, and GitHub Actions CI.

What changed: The project was submitted as part of the OpenAI 2026 hackathon. It is described as a proof-of-concept demonstration with no live data or paid service.

Single most important open question: Is there any evidence that this tool has been adopted, used, or tested beyond the author’s own development and submission?

• The description states that Planet Review is a transit-signal review tool for astronomy learning and technical reviewing.
• It organizes evidence into cautious, reproducible reviews instead of rewarding exciting interpretations.
• It includes:
  • A deterministic offline Kepler-265 benchmark workflow
  • Recognition of known harmonics (e.g., 34.057671-day peak as part of Kepler-265 c's harmonic family)
  • Retirement of inconclusive residuals under stricter requirements
  • A synthetic software-test fixture not presented as telescope evidence
  • A Streamlit interface, command-line workflow, Markdown and JSON reporting
  • Automated tests and GitHub Actions CI
• The tool is built with Python, astropy, codex, GPT-5.6, matplotlib, numpy, pandas, pytest, and streamlit.
• It does not confirm planets, calculate validated probabilities, or replace professional vetting.

Inference: The product is a software prototype for educational and early-review use in astronomy, designed to promote cautious interpretation of transit signals.

• The description states that Planet Review is an “evidence-first” tool for transit-signal review.
• It positions itself as a way to separate evidence, limitations, and follow-up recommendations instead of rewarding the most exciting interpretations.
• It is described as a tool for astronomy students, educators, citizen-science learners, and technical reviewers.
• The author claims that GPT-5.6 helped shape architecture, define scientific claim controls, and identify circular or overstated interpretations.
• Codex was used to implement modules, tests, CI workflow, and documentation.

Inference: The positioning is educational and cautious, aiming to reduce false positives in early-stage transit signal reviews by promoting reproducibility and transparency.

• The description states that the tool is designed for:
  • Astronomy students
  • Educators
  • Citizen-science learners
  • Technical reviewers who want a transparent example of how to separate evidence, limitations, and follow-up recommendations

Inference: The target customer is likely a niche group within astronomy education or citizen science, with no evidence of broader commercial adoption.

• Not evidenced.
• No pricing, monetization, or business model information is provided in the description.

• Built with Python 3.11 and uses:
  • astropy, codex, GPT-5.6, matplotlib, numpy, pandas, pytest, python, streamlit
• Includes:
  • A deterministic offline Kepler-265 benchmark workflow
  • Streamlit UI, CLI, Markdown/JSON reporting
  • Automated tests (46 passing)
  • GitHub Actions CI
  • Command-line setup instructions
• The same typed pipeline is shared across UI, reports, JSON export, CLI, and tests.
• Includes conservative classification guardrails, harmonic-family checks, preserved historical dispositions, explicit limitations.

Inference: The tool is built for reproducibility and transparency, with a focus on software quality and scientific rigor. It does not appear to require live data or external services.

• Not evidenced.
• No evidence of revenue, customers, usage metrics, or adoption beyond the author’s own development.
• The project is described as a hackathon submission with no indication of further deployment or use in practice.

• Not evidenced.
• No information about competitors or market context is provided in the description.

• The tool is described as a hackathon submission, not a commercial product.
• It does not confirm planets or replace professional astronomical vetting — this may limit its utility for real-world applications.
• The only evidence of use is the author’s own development and submission.
• No external validation, user feedback, or third-party integration is mentioned.
• The tool is offline and deterministic; it does not appear to scale beyond a single benchmark.

1. Has this tool been used by others beyond the author's own development?
2. Are there any plans for broader deployment or integration with live astronomical data sources?
3. What is the intended path from this prototype to a product that could be adopted by educators or citizen scientists?
4. How does the team plan to validate or improve the tool’s scientific claim controls over time?
5. Is there any feedback from users in astronomy education or citizen science?