by jordan-gibbs
Convert Claude Code or Codex into the most intelligent Deep Research Agent. Collect, search, and synthesize web research into a persistent, searchable wiki that builds on itself. Hosted API + MCP: hyperresearch.ai
# Add to your Claude Code skills
git clone https://github.com/jordan-gibbs/hyperresearchGuides for using ai agents skills like hyperresearch.
Last scanned: 5/26/2026
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hyperresearch is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by jordan-gibbs. Convert Claude Code or Codex into the most intelligent Deep Research Agent. Collect, search, and synthesize web research into a persistent, searchable wiki that builds on itself. Hosted API + MCP: hyperresearch.ai. It has 4,326 GitHub stars.
Yes. hyperresearch passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/jordan-gibbs/hyperresearch" and add it to your Claude Code skills directory (see the Installation section above).
hyperresearch is primarily written in Python. It is open-source under jordan-gibbs on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh hyperresearch against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Hyperresearch turns Claude Code into a deep research agent, and it's #1 on the DRACO deep research benchmark, ahead of Claude Research, ChatGPT, Gemini and Perplexity. It runs in OpenAI Codex too. A tier-adaptive 16-step pipeline takes one prompt and produces an adversarially-audited report with full source provenance. Every source it reads lands in a persistent, searchable vault, so each session starts smarter than the last.
Don't want to run it locally? Hyperresearch is the hosted version: the same pipeline, with no Claude Code or Codex install needed.
[!NOTE] New: hyperresearch runs on OpenAI Codex. One command installs the full pipeline for Codex: entry skill, step procedures, the subagent roster as Codex custom agents, and a Stop hook that keeps Codex from skipping steps. Same vault, same ship gate as on Claude Code.
pip install hyperresearch && hyperresearch install . --target codex codex --sandbox workspace-write -c sandbox_workspace_write.network_access=true # then: $hyperresearch <question>Codex support ships in the next PyPI release; until then install from GitHub with
pip install git+https://github.com/jordan-gibbs/hyperresearch. Details and what differs on Codex: Codex.
Read a full report before you install anything. These are unedited Deep runs from the hosted version, and you don't need to log in:
These are the same kind of Deep runs that put Hyperresearch at #1 on DRACO. Method and per-task scores are on the benchmark page.
premier scale profile targets 100–130 in the width sweep alone; citation chasing and gap-fill fetches more than double what actually lands in the corpus.hpr scholar search hits OpenAlex, Crossref, CORE, DOAB, ClinicalTrials.gov, SEC EDGAR and FRED through one client layer and returns a single list deduplicated by DOI and title. Books, trials and filings come back alongside papers, each tagged so the pipeline knows which is which. The humanities and social sciences are covered on purpose, not as an afterthought.run resume picks up at the exact step where it died.Browse the vault by DOI, venue, citation count, retraction status, or retrieved manuscript version with scholarly metadata filters.
Works in Claude Code and OpenAI Codex: same pipeline, same vault.
cd your-project
pip install hyperresearch
hyperresearch install # Claude Code, then: /hyperresearch <anything>
hyperresearch install . --target codex # OpenAI Codex, then: $hyperresearch <anything>
--target all installs both side by side. Codex sessions need write access and network, see below.
Prefer a plugin or a single skill? Each route below installs one bootstrap skill, deep-research, that sets the pipeline up in the current project on first use. It still needs pip install hyperresearch.
| Claude Code | OpenAI Codex | |
|---|---|---|
| Plugin | /plugin marketplace add jordan-gibbs/hyperresearch/plugin install hyperresearch@hyperresearch |
codex plugin marketplace add jordan-gibbs/hyperresearchcodex plugin add hyperresearch@hyperresearch |
| Skill (skills.sh) | npx skills add jordan-gibbs/hyperresearch -a claude-code |
npx skills add jordan-gibbs/hyperresearch -a codex |
| Invoke | /hyperresearch:deep-research <question> (plugin)/deep-research <question> (skill) |
$hyperresearch:deep-research <question> (plugin)$deep-research <question> (skill) |
Or just ask for deep research in plain words; the skill triggers on its description.
Python 3.11–3.14.
Power users:
hyperresearch install --globalmakes/hyperresearchreachable from every Claude Code session anywhere, at the cost of ~15 lines in every session's system reminder. Per-project install (above) keeps unrelated CC sessions clean.
The repo is a Claude Code plugin marketplace, a Codex plugin marketplace, and a skills.sh source, and all three ship the same skill, deep-research. It checks for the hyperresearch CLI, runs hyperresearch install for the agent it is running in (--target codex under Codex), and hands off to the pipeline. The pipeline itself still comes from the Python package.
If Claude Code does not pick up the newly installed skills and subagents, restart it in the same directory and run /hyperresearch <question>. Codex discovers skills at session start, so on first use the bootstrap reads the freshly installed entry skill directly; later sessions can start with $hyperresearch <question>.
The same pipeline runs on the OpenAI Codex CLI.
cd your-project
pip install hyperresearch && hyperresearch install . --target codex
This installs the entry skill at .agents/skills/hyperresearch/, the step procedures under .hyperresearch/codex/steps/, the subagents as custom agents in .codex/agents/, a Stop hook in .codex/hooks.json, and a short block in AGENTS.md. --target all installs the Claude Code and Codex versions side by side. --global --target codex puts the entry skill in ~/.agents/skills/ and the agents in ~/.codex/agents/.
Research needs to write files and reach the network, and Codex allows neither by default. Start the session with both enabled, then invoke the skill:
codex --sandbox workspace-write -c sandbox_workspace_write.network_access=true
# then, in the session:
$hyperresearch <anything>
# non-interactive:
codex exec --sandbox workspace-write -c sandbox_workspace_write.network_access=true \
--dangerously-bypass-hook-trust "\$hyperres