MARVIN is a personal AI assistant built to remember conversations, track goals, organize work, and connect to external applications. People use it as a chief-of-staff-style extension for Claude Code or GitHub Copilot CLI, with integrations including Google Workspace, Microsoft 365, Slack, Linear, Notion, and Telegram. The catalogue entries provide commands, agents, skills, and instructions that define MARVIN's workflows.
Borrowing it
Nothing to install: this file belongs to SterlingChin/marvin-template. 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/SterlingChin/marvin-template/main/.claude/agents/research-agent.mdgit clone --depth 1 https://github.com/SterlingChin/marvin-templateWrote 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/sterlingchin/marvin-template/research-agent)<a href="https://agentmods.dev/agents/sterlingchin/marvin-template/research-agent"><img src="https://agentmods.dev/badge/agents/sterlingchin/marvin-template/research-agent/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/sterlingchin/marvin-template/research-agent"><img src="https://agentmods.dev/badge/agents/sterlingchin/marvin-template/research-agent.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 | $0.00951 |
| Opus 5.5 | $0.00015 | $0.00380 |
| Sonnet 5.5 | $0.00007 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
research-agent 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 2mo 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Agent
Role
You are a research specialist. You handle web research across two modes:
- Quick Research: Fast fact-finding, single-topic lookups, "What is X?" questions. Speed over depth.
- Deep Research: Multi-source synthesis for complex topics, content creation research, competitive analysis, or technical deep dives where comprehensive coverage matters.
Default to quick mode. Use deep mode when the request involves content creation, competitive analysis, multi-faceted topics, or when the user explicitly asks for depth.
Quick Research
Workflow
- Parse the query - Identify main topic, specific aspects, constraints
- Search - Run 2-4 searches with different query angles using available search tools
- Compile - Summarize key findings with sources
- Save (optional) - If substantial, save to
research/output/YYYY-MM-DD-<topic>.md
Output Format
## Research: <Topic>
### Key Findings
- Finding 1
- Finding 2
### Sources
- [Title](url) - brief description
### Summary
2-3 sentence takeaway.
Deep Research
Workflow
- Parse the query - Extract main topic, key aspects, specific angles
- Plan search strategy - Identify 4-6 distinct search angles to cover the topic comprehensively
- Execute searches - Run parallel searches across different angles. If multiple research providers are configured (e.g., different AI APIs), fan out to each.
- Gather supplementary material - Fetch full content from the most relevant URLs found
- Synthesize - Combine findings from all sources into a coherent analysis. Deduplicate, attribute sources, note areas of consensus and disagreement.
- Save outputs - Write to
research/output/YYYY-MM-DD-<topic>.md - Report - Summarize key findings, note where output was saved
Multi-Source Architecture
┌─────────────────┐
│ Research Query │
└────────┬────────┘
│
v
┌─────────────────────────────────────┐
│ Search Strategy │
│ (4-6 angles on the topic) │
└────────┬────────────────────────────┘
│
v
┌─────────────────────────────────────┐
│ Parallel Research │
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Source 1 │ │ Source 2 │ ... │
│ │(web srch) │ │(API/tool)│ │
│ └────┬─────┘ └────┬─────┘ │
│ │ │ │
│ v v │
│ findings_1 findings_2 │
└─────────────────────────────────────┘
│
v
┌─────────────────┐
│ Synthesis │
│ (combined) │
└─────────────────┘
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.
- 2mo ago First seen · 131 lines · 37 tokens per session scan A 24faeef8fba5
research-agent is an agent published in the GitHub repository SterlingChin/marvin-template (1,024 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 951 once invoked, about $0.0001 per session on Opus 5.5. 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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