Research

Community

OutThisLife

research

Look it up before answering or building — this project's documents of record first, then external prior art. Use when the user says research, look it up, sleuth GitHub, "what do other people do", "is that a thing", "are you sure", or asks for something that has obviously been solved before.

Overview

Publisher	OutThisLife
Repository	`brooklyn-skills`
Skill name	`research`
Stars	200
Forks	6
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by OutThisLife on GitHub. Read the source before you install it.

Installation

Install the Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/OutThisLife/brooklyn-skills/tree/main/skills/research
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/OutThisLife/brooklyn-skills.git /tmp/brooklyn-skills
mkdir -p .claude/skills
cp -r /tmp/brooklyn-skills/skills/research .claude/skills/research

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Research

Two halves, in this order: the documents of record for this project, then prior art outside it. Answering from memory and calling it research is the thing this skill is here to stop.

Match the shape of the ask:

A question ("is this possible", "are we doing this right", "don't touch code"): look it up, report, stop. No edits until told to go (→ audit-only).
Part of the work ("research and fix", "sleuth GitHub and iterate until it's right"): research inside the loop. Look, apply, re-check, and say what each pass was based on — don't halt the loop to file a report.

Pick a depth and say it

Depth	For	Shape
quick	one fact, one API, one flag	a source or two, answer in a paragraph
medium	"how do people do this", choosing an approach	3-6 sources, compare, recommend
very thorough	architecture, redesign, "this has to be solved somewhere"	survey the field, name the canon, ROI-ordered options

Fan out with parallel subagents for breadth. Give each one a scope, a Thoroughness: level, Do not edit files., and a return contract: exact paths with line numbers and short quotes for code, or URL, technique, license, and applicability for outside repos.

Documents of record first

Exhaust what the project already states before searching the web.

Tickets and specs: the tracker issue, the PRD or spec, QA feedback, the Slack or Discord thread that started it — whatever this project treats as the record.
The repo's own planning docs: plans/, docs/, ADRs, AGENTS.md, CONTEXT.md, README.
Figma dev mode when the question is visual. The design is the source of truth (→ ui-system).
The repo itself: git log -S<symbol> and git log -p for why the code got this way, and the vendored source in node_modules or site-packages instead of a remembered API surface.

When two documents disagree, say so instead of silently picking one. "Double check against X, Y, Z" means re-read each one and answer per document, not re-skim your own diff.

Then prior art

Someone has almost certainly solved this. Go find them and take it.

GitHub: gh search repos, gh search code, gh api. Read the real source, not the README. Note the license before recommending a lift.
arXiv and papers for anything on agents, generation, simulation, scheduling, or scoring.
Vendor docs and changelogs for API truth. Tutorials, blogs, Reddit, HN, and YouTube for what people actually hit in practice.

Report what is reusable, which pattern to copy, and what to leave behind.

Grade the evidence

Rank the sources and mark which tier a claim sits in:

Vendor-documented, or the shipping source code. Strongest.
Maintainer statements, papers, primary repos.
Blogs, tutorials, forum posts, model memory. Weakest, flag them as such.

Every non-obvious claim carries a URL. With no URL, write "unverified" rather than stating it flat.

Don't

Answer from memory and call it research. "Are you sure?" and "did you research?" mean you already got caught doing it.
Call something impossible or unsupported without opening the docs or source.
Stop at the first hit when the question was "how do people do this".
Hand back a link dump. The deliverable is the recommendation, with the sources under it.
Drop the research on the floor. When it drives a change, the PR description carries what was found and why (→ pr-update). When it's a big survey with no PR yet, write it into the repo's planning docs so it outlives the chat.

Frequently asked questions

What does the Research AI skill do?

Why use Research on TypingMind?

Because you install it once and use it with any model. Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OutThisLife/brooklyn-skills/tree/main/skills/research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Research?

As many as you like. As long as a model supports skills, you can use Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Research AI skill free?

Yes. It is published on GitHub by OutThisLife under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Research

Pick a depth and say it

Documents of record first

Then prior art

Grade the evidence

Don't

Frequently asked questions

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