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 nature-datagit 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/nature-data)<a href="https://agentmods.dev/skills/researai/deepscientist/nature-data"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/nature-data/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/researai/deepscientist/nature-data"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/nature-data.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00090 | $0.01436 |
| Opus 5.5 | $0.00036 | $0.00574 |
| Sonnet 5.5 | $0.00018 | $0.00287 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
nature-data 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.
This is a copy
92% identical to nature-data — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nature Data Availability Skill
This companion skill is adapted from Yuan1z0825/nature-skills/tree/main/nature-data.
See UPSTREAM_LICENSE.txt for the upstream MIT license.
DeepScientist integration
- Follow the shared interaction contract injected by the system prompt.
- Use this as a focused writing companion inside
write,review,rebuttal, orfinalizewhen the active issue is Data Availability, source data, repository selection, dataset citation, restricted data, or FAIR metadata. - Keep DeepScientist's evidence contract authoritative: draft availability text only from verified data inventory, repository records, artifact paths, or explicit unresolved fields.
- Do not invent DOIs, accession numbers, repositories, ethics approvals, access committees, licences, embargoes, or data-use conditions.
Use this skill to turn a manuscript's supporting data into a transparent, Nature-ready data availability package: statement text, repository plan, dataset citations, and missing-information flags.
The governing policy layer is Springer Nature / Nature Portfolio data policy. The implementation layer is FAIR data practice and DataCite-style citation metadata.
Chinese-user operating mode
When the user writes in Chinese, provides a Chinese manuscript note, or asks for "中文对应", "中英对照", "数据可用性声明", "数据获取声明", "原始数据", "数据存储库", or "受限数据":
- Accept Chinese input naturally, but draft the final submission-ready statement in English unless the user explicitly asks for Chinese only.
- Preserve a short Chinese explanation of unresolved decisions when it helps the author act.
- Translate intent, not wording. Chinese phrases such as "可向通讯作者索取" are usually too vague for Nature-style English unless the restriction and access process are specified.
- Convert Chinese repository/status descriptions into precise publication terms:
数据可用性声明->Data Availability;原始数据->raw data;处理后数据->processed data;源数据->source data;补充材料->Supplementary Information;受限数据->restricted data;合理请求->reasonable request, only with reason and review route. - Use
references/chinese-author-alignment.mdfor Chinese terminology, common CN-to-EN failure modes, and bilingual intake questions.
What ships with it
8 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.
- agents/openai.yaml 282 B
- references/chinese-author-alignment.md 4.9 KB
- references/fair-metadata-checklist.md 4.0 KB
- references/policy-principles.md 5.2 KB
- references/repository-and-identifiers.md 4.3 KB
- references/source-basis.md 5.3 KB
- references/statement-patterns.md 7.0 KB
- UPSTREAM_LICENSE.txt 1.0 KB
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 First seen · 129 lines · 90 tokens per session scan A 7753af00e23e
nature-data is a skill published in the GitHub repository ResearAI/DeepScientist (3,392 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 90 tokens to every session and 1,436 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to nature-data, differing in 18 lines, and is treated as a copy.
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