DeepScientist is a local research studio that manages the cycle from baseline experiments through research findings and paper-ready outputs. Researchers use it to organize autonomous scientific investigations, review progress, and take control when needed. The catalogue add-ons provide workflows and agent integrations for running research projects with it.
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.
npx skills add ResearAI/DeepScientist --skill scoutgit clone --depth 1 https://github.com/ResearAI/DeepScientistWrote 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/researai/deepscientist/scout)<a href="https://agentmods.dev/skills/researai/deepscientist/scout"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/scout.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00026 | $0.01615 |
| Opus 5 | $0.00013 | $0.00807 |
| Sonnet 5 | $0.00005 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
scout 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 7d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scout
Use this skill when the quest does not yet have a stable research frame. The goal is to make the task frame concrete enough that a heavier stage can start with confidence.
Match signals
Use scout when:
- the user goal is still ambiguous
- the dataset or split contract is unclear
- the primary metric is unclear
- no trustworthy baseline has been identified
- the paper or repo neighborhood is still thin
- the quest was resumed after a long pause and framing needs reconstruction
- the next stage is blocked by ambiguity rather than by implementation
Do not use scout when:
- the user already fixed the paper, baseline, dataset, metric contract, and scope
- the quest already has a validated baseline and is ready for ideation or execution
- the real blocker is execution or verification rather than framing
One-sentence summary
Resolve only the minimum framing unknowns that change the next anchor, then stop once baseline or idea becomes durable and obvious.
Control workflow
- Reconstruct the current frame from durable state. Make the current task, metric contract, baseline status, and blockers explicit before searching.
- Identify the minimum unknowns.
Keep only the unknowns that materially block
baseline,idea, or both. - Search only the unresolved neighborhood. Reuse memory and local evidence first, then search the smallest paper, repo, and benchmark surface that can change the next anchor.
- Make the evaluation contract and baseline direction explicit. End with a small decision-facing baseline shortlist rather than a broad literature dump.
- Record the next anchor or blocker and stop on clarity. The right output is a durable frame, not search exhaustion.
AVOID / pitfalls
- Do not let
scoutbecome endless exploration. - Do not keep searching once the next anchor is already clear.
- Do not guess the metric, split, or baseline identity when local evidence is still ambiguous.
- Do not ask the user ordinary technical questions before checking local evidence first.
- Do not repeat the same wide search from scratch when existing survey notes, memory, or durable quest files already narrow the space.
- Do not write long paper summaries that do not change the next stage.
- Do not inflate novelty when the apparent gap is already closed by straightforward scaling, standard engineering, or a strong recent paper.
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.
- 7d ago First seen · 182 lines · 26 tokens per session scan A 5c607cfa6db4
scout is a skill published in the GitHub repository ResearAI/DeepScientist (3,315 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 1,615 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.
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