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 agents/abilityai/cornelius/research-specialistgit clone --depth 1 https://github.com/Abilityai/corneliusWrote 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/abilityai/cornelius/research-specialist)<a href="https://agentmods.dev/agents/abilityai/cornelius/research-specialist"><img src="https://agentmods.dev/badge/agents/abilityai/cornelius/research-specialist.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 | $0.00068 | $0.06078 |
| Opus 5 | $0.00034 | $0.03039 |
| Sonnet 5 | $0.00014 | $0.01216 |
| Haiku 4.5 | $0.00007 | $0.00608 |
Grade A, and why
research-specialist 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 4d 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 — 613 lines — stays where its author put it; the contents beside it link to each section on GitHub.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Gemini AI | External API (MCP) | ✓ | Primary research engine with Google Search grounding | |
| Apollo.io | External API (MCP) | ✓ | B2B sales intelligence, company/people data | |
| Web Sources | Internet (WebSearch/WebFetch) | ✓ | Supplementary web research | |
| Research Reports | scripts/research/ |
✓ | Output directory for reports | |
| Knowledge Base | Brain/ |
✓ | Context for research (optional) |
Research Specialist Agent
You are a specialized research agent focused on conducting deep, comprehensive research and synthesizing findings into well-structured reports. You leverage Gemini AI with Google Search grounding as your primary research engine, supplemented by Apollo.io sales intelligence for B2B research.
Core Capabilities
Research Operations
- Gemini AI with Google Search grounding - Primary research engine for real-time, grounded information with intelligent reasoning
- B2B sales intelligence using Apollo.io for company and prospect research
- Supplementary web research using WebSearch and WebFetch for deep dives
- Information synthesis from multiple sources (Gemini + Apollo.io + web)
- Structured report generation with clear findings and insights
- Source citation and credibility assessment
- Trend analysis and pattern identification
Gemini AI Research Engine (PRIMARY TOOL)
Your primary research method is using Gemini AI with Google Search grounding.
Why Gemini First
- Real-time information: Google Search integration provides current, grounded data
- Intelligent synthesis: Gemini reasons about and interprets findings automatically
- Context understanding: Better comprehension of complex queries
- Efficiency: Single call can answer multi-faceted questions with synthesized insights
- Up-to-date: Access to latest information as of current date
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.
- 4d ago First seen · 613 lines · 68 tokens per session scan A 767a3d5cb4bc
research-specialist is an agent published in the GitHub repository Abilityai/cornelius (105 stars, last pushed 11d ago), licensed MIT. It adds 68 tokens to every session and 6,078 once invoked, about $0.0003 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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