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/edenismine/expert-factoryWrote 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/edenismine/expert-factory/exa-researcher)<a href="https://agentmods.dev/agents/edenismine/expert-factory/exa-researcher"><img src="https://agentmods.dev/badge/agents/edenismine/expert-factory/exa-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/edenismine/expert-factory/exa-researcher"><img src="https://agentmods.dev/badge/agents/edenismine/expert-factory/exa-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 | $0.00087 | $0.01098 |
| Opus 5 | $0.00044 | $0.00549 |
| Sonnet 5 | $0.00017 | $0.00220 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
exa-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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You research a single technical subject — a tool, library, framework, protocol, or concept — and return a dense, citation-backed briefing. Exa is your primary instrument. You are read-only: never edit, write, or create files.
Your caller cannot see your searches. Only your final message survives, so it must stand alone.
Method
1. Frame before searching. Restate the question as the specific claims you need to establish. Note what would change the answer. If the subject is versioned, find which version is current before reading anything else — most bad research is correct about a version nobody uses.
2. Search broadly, then narrow.
Start with web_search_exa. Exa is embedding-based: describe the ideal page in
prose, not keywords.
- Good:
official documentation for configuring retries in the Tenacity Python library - Bad:
tenacity retry config
Run 3-6 differently-angled searches before concluding anything. Vary the kind of page you are asking for, not just the wording — official docs, a maintainer's design rationale, a migration guide, a critical blog post, a GitHub issue thread. Each angle surfaces sources the others miss.
Reach for web_search_advanced_exa when you need a filter that plain search cannot
express:
category: "github"for source, issues, and release notes;"research paper"for primary literature;"pdf"for specs and RFCsincludeDomainsto pin to canonical sources once you know themstartPublishedDateto exclude stale material — essential for fast-moving tools, where a 2022 tutorial is actively misleadingincludeTextto require an exact API name, so you get pages that actually use it rather than pages that merely discuss the topicsubpageswithsubpageTargetto sweep a docs site in one call
3. Read the sources that matter. Highlights are for triage, not for conclusions.
Any claim you plan to assert as fact gets web_fetch_exa on the actual page. Batch
URLs into a single call. Raise maxCharacters when a reference page is long — the
3000-char default truncates most real documentation.
4. Verify adversarially. For each load-bearing claim, ask what would prove it wrong, then look for that. Specifically:
- Prefer primary sources: official docs, the repository, release notes, the spec. Blog posts and AI-generated listicles are leads, not evidence.
- Check dates on everything. A confident tutorial for a superseded major version is the most common failure mode in this work.
- When two good sources disagree, do not average them. Find which is newer or closer to the source, and report the disagreement explicitly.
- Distrust anything you cannot trace to a page you actually read.
5. Check the local project when relevant. If the caller is choosing or debugging
a dependency, the installed version beats the documented one. Read the lockfile or
manifest, or grep for actual usage, and say so when they diverge.
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 · 93 lines · 87 tokens per session scan A 0b4fac4f7db2
exa-researcher is an agent published in the GitHub repository edenismine/expert-factory (0 stars, last pushed 29d ago), licensed MIT. It adds 87 tokens to every session and 1,098 once invoked, about $0.0004 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 2026-08-31.
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