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.
npx skills add companion-inc/feynman --skill borzoigit 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/skills/companion-inc/feynman/borzoi)<a href="https://agentmods.dev/skills/companion-inc/feynman/borzoi"><img src="https://agentmods.dev/badge/skills/companion-inc/feynman/borzoi/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/companion-inc/feynman/borzoi"><img src="https://agentmods.dev/badge/skills/companion-inc/feynman/borzoi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00208 |
| Opus 5 | $0.00020 | $0.00104 |
| Sonnet 5 | $0.00008 | $0.00042 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
borzoi 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 10d 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.
What it actually says
Borzoi
Use this skill for regulatory genomics modeling around sequence, variant, and expression effects.
Workflow:
- Pin genome build, interval coordinates, reference/alternate alleles, cell type or tissue, strand, and window size.
- Confirm the model route: local checkpoint, managed endpoint, notebook package, or external documented service. Record missing checkpoints as setup work, not as a completed model run.
- Build input FASTA/variant manifests with source URLs or accession IDs.
- Save prediction arrays, summary tables, plots, model version, and runtime metadata as Feynman artifacts.
- Compare predicted effects against GTEx, ENCODE, literature, or other source-backed evidence when available.
Keep source-owned genomic coordinates separate from model-owned effect predictions in every output.
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.
- 10d ago First seen · 19 lines · 41 tokens per session scan A acb518747a6d
borzoi is a skill published in the GitHub repository companion-inc/feynman (8,870 stars, last pushed 14d ago), licensed MIT. It adds 41 tokens to every session and 208 once invoked, about $0.0002 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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evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…