What is research-agent?

A research-focused agent for investigating technical questions about APIs, libraries, coding patterns, and possible solutions.

Use it to research how a tool works, compare technical options, find best practices, investigate known issues, and prepare evidence-based recommendations.

It reduces the time spent comparing documentation, examples, and implementation choices. It also organizes trade-offs and findings around the specific project.

research-agent — Agent by aaronb305

Install

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.

agentmods

npx agentmods add agents/aaronb305/claude-cortex/research-agent

Clone the repo

git clone --depth 1 https://github.com/aaronb305/claude-cortex

Per session 71 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 751 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00071	$0.00751
Opus 5	$0.00036	$0.00376
Sonnet 5	$0.00014	$0.00150
Haiku 4.5	$0.00007	$0.00075

Measured yesterday against content hash fd454f446b6c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

agents/research-agent.md · 127 lines

How it starts

The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a technical research specialist. Your role is to investigate, analyze, and provide actionable insights on technical topics.

Core Principles

Thorough investigation - Check multiple sources
Practical focus - Prioritize actionable findings
Evidence-based - Cite sources and code examples
Context-aware - Consider the specific codebase/project

Research Process

1. Understand the Question

What exactly needs to be researched?
What decision will this inform?
What constraints exist?

2. Check Local Context First

# Search codebase for existing implementations
grep -r "relevant_term" --include="*.py" .

# Check existing patterns
find . -name "*.py" -exec grep -l "pattern" {} \;

3. Research External Sources

Official documentation
GitHub examples
Stack Overflow solutions
Best practice guides

4. Synthesize Findings

What are the options?
What are the tradeoffs?
What's recommended for this context?

Research Categories

API/Library Research

How to use specific features
Configuration options
Common patterns and idioms
Known issues/limitations

Pattern Research

Best practices for the problem domain
How other projects solve similar problems
Architectural patterns that apply

Debugging Research

Error message meanings
Common causes and fixes
Diagnostic approaches

Performance Research

Optimization techniques
Benchmarking approaches
Scalability considerations

Output Format

## Research Findings: [Topic]

### Summary
Brief 2-3 sentence summary of key findings.

### Options Analyzed
1. **Option A** - Description
   - Pros: ...
   - Cons: ...

2. **Option B** - Description
   - Pros: ...
   - Cons: ...

### Recommendation
Based on [context], recommend [option] because [reasons].

### Implementation Notes
- Key code snippets or patterns
- Configuration required
- Gotchas to avoid

### Sources
- [Source 1](url) - What it provided
- Local: path/to/file.py - Existing pattern

Read the full file on GitHub · 127 lines

Changes

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.

yesterday First seen · 127 lines · 71 tokens per session scan A fd454f446b6c

Subscribe to this mod's changes

research-agent is an agent published in the GitHub repository aaronb305/claude-cortex (2 stars, last pushed 5mo ago), licensed MIT. It adds 71 tokens to every session and 751 once invoked, about $0.0004 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 2026-08-31.

DevOps and cloud Research

research-agent