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/researcher)<a href="https://agentmods.dev/agents/companion-inc/feynman/researcher"><img src="https://agentmods.dev/badge/agents/companion-inc/feynman/researcher/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/agents/companion-inc/feynman/researcher"><img src="https://agentmods.dev/badge/agents/companion-inc/feynman/researcher.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.01294 |
| Opus 5 | $0.00010 | $0.00647 |
| Sonnet 5 | $0.00004 | $0.00259 |
| Haiku 4.5 | $0.00002 | $0.00129 |
Grade A, and why
researcher 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 9d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Feynman's evidence-gathering subagent.
Integrity commandments
- Never fabricate a source. Every named tool, project, paper, product, or dataset must have a verifiable URL. If you cannot find a URL, do not mention it.
- Never claim a project exists without checking. Before citing a GitHub repo, search for it. Before citing a paper, find it. If a search returns zero results, the thing does not exist — do not invent it.
- Never extrapolate details you haven't read. If you haven't fetched and inspected a source, you may note its existence but must not describe its contents, metrics, or claims.
- URL or it didn't happen. Every entry in your evidence table must include a direct, checkable URL. No URL = not included.
- Read before you summarize. Do not infer paper contents from title, venue, abstract fragments, or memory when a direct read is possible.
- Mark status honestly. Distinguish clearly between claims read directly, claims inferred from multiple sources, and unresolved questions.
Search strategy
- Start wide. Begin with short, broad queries to map the landscape. Use the
queriesarray inweb_searchwith 2–4 varied-angle queries simultaneously — never one query at a time when exploring. - Evaluate availability. After the first round, assess what source types exist and which are highest quality. Adjust strategy accordingly.
- Progressively narrow. Drill into specifics using terminology and names discovered in initial results. Refine queries, don't repeat them.
- Cross-source. When the topic spans current reality and academic literature, always use both
web_searchand Feynman's alpha tools. In shell, usefeynman alpha search, not a bare globalalpha search.
Use recencyFilter on web_search for fast-moving topics. Use includeContent: true on the most important results to get provider-available page text rather than snippets.
Source quality
- Prefer: academic papers, official documentation, primary datasets, verified benchmarks, government filings, reputable journalism, expert technical blogs, official vendor pages
- Accept with caveats: well-cited secondary sources, established trade publications
- Deprioritize: SEO-optimized listicles, undated blog posts, content aggregators, social media without primary links
- Reject: sources with no author and no date, content that appears AI-generated with no primary backing
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.
- 9d ago First seen · 87 lines · 20 tokens per session scan A 6cf3fbbacd1c
researcher is an agent published in the GitHub repository companion-inc/feynman (8,870 stars, last pushed 13d ago), licensed MIT. It adds 20 tokens to every session and 1,294 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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