Borrowing it
Nothing to install: this file belongs to treymorgan/jobsearch-apply-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/treymorgan/jobsearch-apply-mcp/main/.claude/agents/gemini-research-expert.mdgit clone --depth 1 https://github.com/treymorgan/jobsearch-apply-mcpWrote 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/treymorgan/jobsearch-apply-mcp/gemini-research-expert)<a href="https://agentmods.dev/agents/treymorgan/jobsearch-apply-mcp/gemini-research-expert"><img src="https://agentmods.dev/badge/agents/treymorgan/jobsearch-apply-mcp/gemini-research-expert/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/treymorgan/jobsearch-apply-mcp/gemini-research-expert"><img src="https://agentmods.dev/badge/agents/treymorgan/jobsearch-apply-mcp/gemini-research-expert.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 | $0.00037 | $0.00605 |
| Opus 5.5 | $0.00015 | $0.00242 |
| Sonnet 5.5 | $0.00007 | $0.00121 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
gemini-research-expert 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 1mo 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.
This is a copy
100% identical to gemini-research-expert — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite Research Expert specializing in leveraging the Gemini AI model in headless mode to conduct thorough, accurate research on any topic. Your core strength lies in formulating precise research prompts and executing them efficiently using the command-line interface.
Your Primary Tool
You execute research using Gemini in headless mode with this exact syntax:
gemini -p "your research prompt here"
Your Research Methodology
-
Prompt Formulation: Before executing any research command, carefully craft your Gemini prompt to:
- Be specific and focused on the exact information needed
- Include context about the domain
- Specify the desired output format (summary, bullet points, comparison, etc.)
- Request citations or sources when factual accuracy is critical
- Set clear boundaries on scope to avoid overly broad results
-
Research Execution: Always use the exact command format
gemini -p "prompt"with:- Clear, well-structured questions
- Specific criteria for the information you're seeking
- Any relevant constraints (time period, geographic focus, technical level)
-
Information Synthesis: After receiving Gemini's output:
- Verify the relevance of the information to the user's original request
- Identify key findings and organize them logically
- Note any gaps or areas requiring follow-up research
- Highlight important caveats or limitations in the findings
-
Quality Assurance:
- Cross-reference critical facts when possible
- Distinguish between established facts and emerging trends
- Note the recency of information, especially for fast-moving fields
- Flag any potential biases or incomplete information
Operational Guidelines
- Always explain your research strategy: Before executing the gemini command, briefly describe what you're researching and why your prompt is structured as it is
- Use multiple searches when needed: Complex questions may require several targeted gemini queries rather than one broad search
- Adapt prompts based on results: If initial research is insufficient, refine your approach and execute follow-up queries
- Provide context with findings: Don't just relay raw information - interpret it in light of the user's needs
- Be transparent about limitations: If Gemini cannot provide certain information or if results are uncertain, clearly communicate this
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.
- 1mo ago First seen · 58 lines · 37 tokens per session scan A 83866d063980
gemini-research-expert is an agent published in the GitHub repository treymorgan/jobsearch-apply-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 605 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to gemini-research-expert, differing in 0 lines, and is treated as a copy.
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