deep-research

deep-research is a skill for Claude Code, Codex from Human-Agent-Society/CORAL. It costs 28 tokens per session (2,862 once invoked), scanned A, original, Apache-2.0.

A research workflow for investigating a problem before writing code. It uses web searches and organised notes to record sources, findings, and experiments.

In plain words
What is it for?
Use it to research possible techniques, save source material, compare approaches, and record what worked or failed before changing an implementation.
Why use it?
It reduces the risk of coding from incomplete knowledge, especially when the problem involves an unfamiliar technical field.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python .coral/public/skills/organize-files/scripts/resolve_links.py {shared_dir}/notes/ --new <new-slug>.

Good fit Use it to research possible techniques, save source material, compare approaches, and record what worked or failed before changing an implementation.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/Human-Agent-Society/CORAL
agentmods
npx agentmods add skills/human-agent-society/coral/deep-research

Made for: Claude Code, Codex.

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.

agentmods badge for deep-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-agent-society/coral/deep-research/github.svg)](https://agentmods.dev/skills/human-agent-society/coral/deep-research)
Your own site
<a href="https://agentmods.dev/skills/human-agent-society/coral/deep-research"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/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.

agentmods 80×15 button for deep-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/human-agent-society/coral/deep-research"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,862 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.02862
Opus 5 $0.00014 $0.01431
Sonnet 5 $0.00006 $0.00572
Haiku 4.5 $0.00003 $0.00286

Measured 11d ago against content hash a1f79e6224bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/check_grounding.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

coral/template/skills/deep-research/SKILL.md · 192 lines

How it starts

The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research

Research the problem thoroughly before writing code. Understand what's known, what's been tried, and what approaches exist.

When to Use

  • Starting a new task or problem
  • Stuck after multiple evals without improvement
  • Pivoting to a fundamentally different approach
  • The problem involves domain-specific knowledge you're unfamiliar with

Notes Directory Structure

notes/
├── index.md              ← table of contents for research/ and experiments/
├── raw/                  ← saved web pages, paper excerpts (immutable, never edit)
├── research/             ← your synthesized findings (link back to raw/)
│   └── _coverage.md      ← the research coverage ledger (dimensions × covered/partial/missing)
└── experiments/          ← eval reflections and results (written by reflect heartbeat)

Process

1. Understand the Problem — and Map the Research Space

Read the task description and key files. Identify what's being optimized, what the constraints are, and what makes it hard. Check coral log and {shared_dir}/notes/ for prior work.

Then decompose the problem into 4–8 research dimensions — the distinct things a team would need to understand to win this task. Derive them from the task, don't pull them from a fixed list. Useful starting prompts (not a required set): prior art / SOTA methods, mechanism or theory, implementation / libraries, the evaluation & grader surface, failure modes, adjacent fields. Drop the ones that don't apply; add task-specific ones that do.

Record them in the coverage ledger at {shared_dir}/notes/research/_coverage.md — the team's map of what's been researched and what hasn't. If it doesn't exist, create it with every dimension missing; if it does, read it first and target the gaps rather than re-covering what's done:

# Research Coverage — <task name>
<!-- Owned by the research team. Update on every research pass. Dimensions are
     derived from THIS task, not a fixed list. Status: covered | partial | missing -->

| Dimension (what to understand)  | Status  | Note                                | Last touched |
|---------------------------------|---------|-------------------------------------|--------------|
| Prior art / SOTA methods        | missing | —                                   | —            |
| Failure modes of approach X     | missing | —                                   | —            |
| Evaluation surface / grader     | missing | —                                   | —            |

Read the full file on GitHub · 192 lines

Files

What ships with it

5 files 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.

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.

  1. 11d ago First seen · 192 lines · 28 tokens per session scan A a1f79e6224bb

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository Human-Agent-Society/CORAL (965 stars, last pushed 2d ago), licensed Apache-2.0. It adds 28 tokens to every session and 2,862 once invoked, about $0.0001 per session on Opus 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 2026-08-30.