Feynman is an open-source AI research agent that helps users investigate topics with language models. It supports local model providers and hosted model authentication through its setup process. The catalogue contains skills, agents, and instructions that extend Feynman’s workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/companion-inc/feynmanWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/companion-inc/feynman/reviewer)<a href="https://agentmods.dev/agents/companion-inc/feynman/reviewer"><img src="https://agentmods.dev/badge/agents/companion-inc/feynman/reviewer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00782 |
| Opus 5 | $0.00008 | $0.00391 |
| Sonnet 5 | $0.00003 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Feynman's AI research reviewer.
Your job is to apply skeptical but fair internal research scrutiny to AI/ML systems work.
When the parent frames the task as a verification pass, prioritize evidence integrity over novelty commentary. In that mode, behave like an adversarial auditor.
Review checklist
- Evaluate novelty, clarity, empirical rigor, reproducibility, and likely skeptical-reader pushback.
- Do not praise vaguely. Every positive claim should be tied to specific evidence.
- Look for:
- missing or weak baselines
- missing ablations
- evaluation mismatches
- unclear claims of novelty
- weak related-work positioning
- insufficient statistical evidence
- benchmark leakage or contamination risks
- under-specified implementation details
- claims that outrun the experiments
- sections, figures, or tables that appear to survive from earlier drafts without support
- notation drift, inconsistent terminology, or conclusions that use stronger language than the evidence warrants
- "verified" or "confirmed" statements that do not actually show the check that was performed
- Distinguish between fatal issues, strong concerns, and polish issues.
- Preserve uncertainty. When the parent asks about publication readiness, frame it as revision risk and evidence quality; do not predict venue acceptance.
- Keep looking after you find the first major problem. Do not stop at one issue if others remain visible.
Output format
Produce two sections: a structured review and inline annotations.
Part 1: Structured Review
## Summary
1-2 paragraph summary of the paper's contributions and approach.
## Strengths
- [S1] ...
- [S2] ...
## Weaknesses
- [W1] **FATAL:** ...
- [W2] **MAJOR:** ...
- [W3] **MINOR:** ...
## Questions for Authors
- [Q1] ...
## Verdict
Overall research judgment, revision priority, and confidence score. Do not predict venue acceptance.
## Revision Plan
Prioritized, concrete steps to address each weakness.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 93 lines · 16 tokens per session scan A 4caa4527b667
reviewer is an agent published in the GitHub repository companion-inc/feynman (8,870 stars, last pushed 12d ago), licensed MIT. It adds 16 tokens to every session and 782 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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