Deep Research

CommunityPopular

jordan-gibbs

deep-research

Deep research with hyperresearch, for Claude Code and OpenAI Codex. Use when the user asks for deep research, a research report, a literature review, or a multi-source analysis with verified citations. Checks that the hyperresearch CLI is installed, sets it up in the current project for the agent you are running in, then hands off to the hyperresearch pipeline (a tier-adaptive 16-step pipeline with a persistent source vault). Not for quick lookups one or two searches can answer.

Overview

Publisher	jordan-gibbs
Repository	`hyperresearch`
Skill name	`deep-research`
Stars	3.8K
Forks	374
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by jordan-gibbs on GitHub. Read the source before you install it.

Installation

Install the Deep 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/jordan-gibbs/hyperresearch/tree/main/skills/deep-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/jordan-gibbs/hyperresearch.git /tmp/hyperresearch
mkdir -p .claude/skills
cp -r /tmp/hyperresearch/skills/deep-research .claude/skills/deep-research

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

Use it in TypingMind

Enable Deep 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 Deep 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 Deep 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.

Deep research with hyperresearch

This skill is a bootstrap. The research pipeline itself is an entry skill plus 16 step procedures and a set of subagents, which the hyperresearch Python package renders and installs into the project. This file does not contain the pipeline. Do not try to run the research from here, and do not answer the research question from your own knowledge: the pipeline is the deliverable.

1. Check the CLI

Run:

bash
hyperresearch --version

If the command is not found, stop and tell the user:

hyperresearch is not installed. Install it with pip install hyperresearch (Python 3.11 to 3.14), then ask again.

You may run pip install hyperresearch yourself only if the user says to.

2. Pick your branch

You know which agent you are. Follow exactly one branch.

Claude Code (you have Skill and Task tools): section 3A.
OpenAI Codex (you edit files with apply_patch, spawn custom agents, and skills are invoked as $name): section 3B.

If you genuinely cannot tell, look at the working directory: a .codex/ or .agents/ directory and no .claude/ means Codex; the reverse means Claude Code. Still unsure: ask the user which one they are using.

3A. Claude Code

If .claude/skills/hyperresearch/SKILL.md does not exist in the working directory, run:

bash
hyperresearch install . --json

This creates the vault (.hyperresearch/, research/), adds a short block to CLAUDE.md, and installs the entry skill, the 16 step skills, the subagents and a PreToolUse hook under .claude/. It is safe to re-run; it no-ops on files that are already current. Tell the user in one line what was installed.

Then invoke the installed router with the user's research request, verbatim:


Skill(skill: "hyperresearch", args: "<the user's research request>")

From then on, follow the hyperresearch skill. It owns the query, the tier choice, and every step.

If the hyperresearch skill is not available yet (Claude Code loads new subagents at session start, and some versions do the same for skills), tell the user that setup is done and ask them to restart Claude Code in this directory and run:


/hyperresearch <their research request>

3B. OpenAI Codex

If .agents/skills/hyperresearch/SKILL.md does not exist in the working directory, run:

bash
hyperresearch install . --target codex --json

This creates the vault (.hyperresearch/, research/), adds a short block to AGENTS.md, and installs the entry skill at .agents/skills/hyperresearch/, the step procedures under .hyperresearch/codex/steps/, the custom agents under .codex/agents/, and a Stop hook in .codex/hooks.json. It is safe to re-run. Tell the user in one line what was installed.

If the install (or any later hyperresearch fetch) fails with a permission or network error, the session is sandboxed read-only or offline. Stop and tell the user to restart Codex with a writable workspace and network access, for example:

bash
codex --sandbox workspace-write -c sandbox_workspace_write.network_access=true

Then hand off. Codex discovers skills when a session starts, so a skill installed a moment ago may not be listed yet. The robust path is to read the entry skill directly and follow it:

Read .agents/skills/hyperresearch/SKILL.md in full (for example cat .agents/skills/hyperresearch/SKILL.md).
Follow it with the user's research request, verbatim, as the query. It is the same procedure $hyperresearch <request> would load. It owns the query, the tier choice, and every step, and each step tells you which procedure file under .hyperresearch/codex/steps/ to read next.

If $hyperresearch already appears in your skill list, invoking it with the request is equivalent. In later sessions the user can start a run directly with $hyperresearch <question>.

Two things differ from Claude Code, and the entry skill covers both: there is no browser lane (blocked fetches stay queued and are listed for the user at the end), and the Stop hook, once Codex trusts the project's hooks, will not let the session finish while a run is mid-pipeline.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB

Frequently asked questions

What does the Deep Research AI skill do?

Why use Deep Research on TypingMind?

Because you install it once and use it with any model. Deep 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 Deep Research in TypingMind?

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

Which AI models can use Deep 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 Deep Research?

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

Is the Deep Research AI skill free?

Yes. It is published on GitHub by jordan-gibbs 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

Deep research with hyperresearch

1. Check the CLI

2. Pick your branch

3A. Claude Code

3B. OpenAI Codex

Bundled files

Frequently asked questions

Related AI Skills

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