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 LigphiDonk/Oh-my--paper --skill gemini-deep-researchgit clone --depth 1 https://github.com/LigphiDonk/Oh-my--paperWrote 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/ligphidonk/oh-my--paper/gemini-deep-research)<a href="https://agentmods.dev/skills/ligphidonk/oh-my--paper/gemini-deep-research"><img src="https://agentmods.dev/badge/skills/ligphidonk/oh-my--paper/gemini-deep-research/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/ligphidonk/oh-my--paper/gemini-deep-research"><img src="https://agentmods.dev/badge/skills/ligphidonk/oh-my--paper/gemini-deep-research.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 | $0.00020 | $0.01080 |
| Opus 5 | $0.00010 | $0.00540 |
| Sonnet 5 | $0.00004 | $0.00216 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
gemini-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 11d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gemini-deep-research
Canonical Summary
Perform complex, long-running research tasks using Gemini Deep Research Agent. Use when: asked to research topics requiring multi-source synthesis, competitive analysis, market research, literature review, or comprehensive technical invest...
Trigger Rules
Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.
Resource Use Rules
- Treat
scripts/as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.
Execution Contract
- Resolve every relative path from this skill directory first.
- Prefer inspection before mutation when invoking bundled scripts.
- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.
Upstream Instructions
Gemini Deep Research
Use Google Gemini's Deep Research Agent to perform complex, long-running context gathering and synthesis tasks. The agent autonomously breaks down your query, searches the web, and synthesizes findings into a comprehensive report.
Prerequisites
GEMINI_API_KEYenvironment variable (obtain from Google AI Studio)- Python 3.8+ with
requestslibrary - Note: Requires a direct Gemini API key — OAuth tokens are not supported.
When to Use
- Comprehensive literature or market research
- Competitive landscape analysis
- Technical deep dives requiring multi-source synthesis
- Any research task that benefits from systematic web search
- When you need a structured report with evidence from multiple sources
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 130 lines · 20 tokens per session scan A d793d3d5d0d8
gemini-deep-research is a skill published in the GitHub repository LigphiDonk/Oh-my--paper (722 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 1,080 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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