research

research is a skill for Claude Code from study8677/repobrain. It costs 32 tokens per session (105 once invoked), scanned A, original, MIT.

A research assistant that performs a multi-step investigation of a topic and returns a detailed research result.

In plain words
What is it for?
Use it for deep research on a subject. The available description does not specify particular research sources or output formats.
Why use it?
It reduces the work of collecting and organizing information when a topic needs more than a quick answer.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the repobrain plugin — 4 skills, 7 commands, 1 hook shipped together

Good fit Use it for deep research on a subject. The available description does not specify particular research sources or output formats.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/study8677/repobrain/research
About the project

RepoBrain is a repository knowledge engine that lets coding agents ask grounded questions about a codebase and receive answers linked to source files and lines. It is for developers using Claude Code, Cursor, Codex, Windsurf, and other AI coding tools. Catalogue add-ons extend its commands, instructions, skills, rules, plugin, and hook workflows.

study8677/repobrain · 1,346 stars · on GitHub · deepwiki.com

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.

Any agent
npx skills add study8677/repobrain --skill research
Clone the repo
git clone --depth 1 https://github.com/study8677/repobrain

Made for: Claude Code.

Or install repobrain, the plugin that ships this one along with the rest of its 4 skills, 7 commands, 1 hook.

Wrote 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.

agentmods badge for research

README.md
[![agentmods](https://agentmods.dev/badge/skills/study8677/repobrain/research/github.svg)](https://agentmods.dev/skills/study8677/repobrain/research)
Your own site
<a href="https://agentmods.dev/skills/study8677/repobrain/research"><img src="https://agentmods.dev/badge/skills/study8677/repobrain/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.

agentmods 80×15 button for research

Your own site · 80×15
<a href="https://agentmods.dev/skills/study8677/repobrain/research"><img src="https://agentmods.dev/badge/skills/study8677/repobrain/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 105 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.00105
Opus 5.5 $0.00013 $0.00042
Sonnet 5.5 $0.00006 $0.00021
Haiku 4.5 $0.00003 $0.00011

Measured 1mo ago against content hash 973cd811f3eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (tools.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

engine/repobrain_engine/skills/research/SKILL.md · 15 lines

What it actually says

Research Skill

This skill provides capabilities to perform deep research on a topic.

Usage

When asked to research a topic, use the deep_research tool. This tool will simulate a multi-step research process.

Capabilities

  • deep_research(topic: str) -> str: Performs a simulated comprehensive research task.
Files

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.

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.

  1. 1mo ago First seen · 15 lines · 32 tokens per session scan A 973cd811f3eb

Subscribe to this mod's changes

research is a skill published in the GitHub repository study8677/repobrain (1,346 stars, last pushed 3d ago), licensed MIT. It adds 32 tokens to every session and 105 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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