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:

/hyperresearch <their research request>

3B. OpenAI Codex

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

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:

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:

1. Read .agents/skills/hyperresearch/SKILL.md in full (for example cat .agents/skills/hyperresearch/SKILL.md).
2. 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.