What is deep-research?

A setup guide for a multi-step research workflow that uses the hyperresearch command-line tool. It installs the workflow into a project and passes research requests to it.

Use it for research reports, literature reviews and multi-source analysis where checked citations are required.

It avoids starting a deep research process without checking that the required tool is installed or that the project has the needed files. It also makes clear that this guide is only the bootstrap, not the research pipeline itself.

deep-research — Skill for Claude Code and Codex by jordan-gibbs

About the project

Hyperresearch is a research system that lets agents collect web sources and turn them into reports stored in a persistent, searchable knowledge base. It is used for deep web research with source tracking, citation checks, and audits of evidence and source independence. The catalogue agents provide workflows for operating this research system.

jordan-gibbs/hyperresearch · 4,411 stars · on GitHub · hyperresearch.ai

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 jordan-gibbs/hyperresearch --skill deep-research

Clone the repo

git clone --depth 1 https://github.com/jordan-gibbs/hyperresearch

Made for: Claude Code, Codex.

Or install hyperresearch, the plugin that ships this one along with the rest of its 1 skill.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/jordan-gibbs/hyperresearch/deep-research/github.svg)](https://agentmods.dev/skills/jordan-gibbs/hyperresearch/deep-research)

Your own site

<a href="https://agentmods.dev/skills/jordan-gibbs/hyperresearch/deep-research"><img src="https://agentmods.dev/badge/skills/jordan-gibbs/hyperresearch/deep-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/jordan-gibbs/hyperresearch/deep-research"><img src="https://agentmods.dev/badge/skills/jordan-gibbs/hyperresearch/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,199 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.

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 5.1	$0.00104	$0.01199
Opus 5.5	$0.00042	$0.00480
Sonnet 5.5	$0.00021	$0.00240
Haiku 4.5	$0.00010	$0.00120

Measured 1mo ago against content hash dcb993145238, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deep-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.

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.

skills/deep-research/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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:

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:

Read the full file on GitHub · 118 lines

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.

agents/openai.yaml 309 B

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.

1mo ago Changed · +54 lines · +12 tokens per session dcb993145238
1mo ago First seen · 64 lines · 92 tokens per session scan A 9e1ff753f572

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository jordan-gibbs/hyperresearch (4,411 stars, last pushed 5d ago), licensed MIT. It adds 104 tokens to every session and 1,199 once invoked, about $0.0004 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 2026-09-25.

Claude Code Research agents agentskills codex deep-research deep-research-agent

deep-research next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
deep-researchjordan-gibbs/hyperresearch	Skill	4.4k	5d ago	MIT	A	104	Claude Code, Codex
cocoplus-configSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	56	Claude Code
cocotraceSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	22	Claude Code
context-addSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	48	Claude Code, Codex
lean-reviewSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	44	Claude Code
ops-demoSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	46	Claude Code
bloom-crystallizeSnowflake-Labs/cocoplus	Skill	727	2d ago	MIT	A	39	Claude Code