Omnigent is an open-source orchestration layer for running and coordinating different AI coding agents through one system. It is for developers who want to combine agents, apply policies and sandboxing, and continue sessions across devices. The catalogue add-ons extend its agent 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 agentmods add skills/omnigent-ai/omnigent/deep-researchnpx skills add omnigent-ai/omnigent --skill deep-researchgit clone --depth 1 https://github.com/omnigent-ai/omnigentWrote 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/omnigent-ai/omnigent/deep-research)<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/deep-research"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/deep-research.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 | $0.00031 | $0.00478 |
| Opus 5 | $0.00015 | $0.00239 |
| Sonnet 5 | $0.00006 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
deep-research 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 6d 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
deep-research — cited, cross-checked web research
Use this for any question that needs current, verifiable information from the web. The deliverable is a synthesized answer where every load-bearing claim is backed by a source you actually read.
Tools
search_web_pages(query, [site], [published_after], [published_before], [mode])— discover candidate sources. Write thequeryas a natural-language description of the ideal page, not keywords. Usemode: pro(default).fetch_page_content(url, [max_chars])— read the full page (markdown). A search snippet is NEVER sufficient evidence — fetch before you cite.
Procedure
- Plan. Break the question into 3-6 focused sub-queries that together cover it. For contested or high-stakes questions, plan at least two independent angles.
- Search. Run
search_web_pagesper sub-query. Prefer primary sources; usepublished_afterfor anything time-sensitive. - Read.
fetch_page_contenton the 2-3 most promising results per sub-query. Quote/cite only what you read, not what a snippet implied. - Cross-check. Verify every load-bearing claim against ≥2 INDEPENDENT sources (independent = different owners, not mirrors of one another). When sources disagree, surface the disagreement rather than picking silently.
- Synthesize. Write a structured answer. Each non-obvious claim gets an inline citation to the URL you fetched. Separate "well-supported" from "uncertain / single-source".
- Cite. End with a
Sourceslist of the URLs you actually fetched.
Notes
- Don't answer from prior knowledge with a disclaimer — search and read first.
- If coverage is thin or sources conflict irreconcilably, say so explicitly; an honest "the evidence is mixed" beats false confidence.
- Keep each sub-query narrow enough that a couple of fetches resolve it.
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.
- 6d ago First seen · 40 lines · 31 tokens per session scan A 488fab18ea7d
deep-research is a skill published in the GitHub repository omnigent-ai/omnigent (9,702 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 478 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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