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.
git clone --depth 1 https://github.com/Human-Agent-Society/CORALnpx agentmods add skills/human-agent-society/coral/deep-researchWrote 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.
[](https://agentmods.dev/skills/human-agent-society/coral/deep-research)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.02862 |
| Opus 5 | $0.00014 | $0.01431 |
| Sonnet 5 | $0.00006 | $0.00572 |
| Haiku 4.5 | $0.00003 | $0.00286 |
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.
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.
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 | — | — |
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.
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.
- 11d ago First seen · 192 lines · 28 tokens per session scan A a1f79e6224bb
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.
Other skills, from other repositories
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
new-app
Scaffold a new Atomic Agents project from scratch — create the directory, pyproject.toml, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs /atomic-agents:new-app.