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-figuregit 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-figure)<a href="https://agentmods.dev/skills/researai/deepscientist/nature-figure"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/nature-figure/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-figure"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/nature-figure.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 | $0.00142 | $0.02644 |
| Opus 5.5 | $0.00057 | $0.01058 |
| Sonnet 5.5 | $0.00028 | $0.00529 |
| Haiku 4.5 | $0.00014 | $0.00264 |
Grade A, and why
nature-figure 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 2mo 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nature Figure Making Skill
This companion skill is adapted from Yuan1z0825/nature-skills/tree/main/nature-figure.
See UPSTREAM_LICENSE.txt for the upstream MIT license.
DeepScientist integration
- Follow the shared interaction contract injected by the system prompt.
- Use this for Nature-family or other high-impact journal figure work when the figure itself is a submission-grade deliverable, especially multi-panel or journal-export work.
- Keep
paper-plotas the faster default for simple structured bar, line, scatter, or radar figures from measured data; usenature-figurewhen the venue/export/review contract is the main constraint. - Keep
figure-polishavailable for final render-inspect-revise checks when a figure already exists and the remaining issue is local readability or surface quality. - Respect this skill's Python/R backend gate even in autonomous mode.
A guide for producing publication-quality scientific figures as a visual argument, not as isolated pretty plots. Every figure starts from a claim, an evidence hierarchy, and a review-risk check before code or aesthetics.
The older Python/matplotlib rules in this skill remain valid. The skill now also supports
R, especially ggplot2 + patchwork + ComplexHeatmap + ggrepel + svglite/cairo_pdf + ragg.
If the user provides a private plotting template collection, use it only as an internal
adaptation source and do not reveal its path, filenames, or provenance in user-facing output.
Color policy: prefer unified method families across all panels over maximal hue separation.
For dense Nature Machine Intelligence-style figure pages, use the low-saturation NMI pastel
family described in references/api.md and reserve green/red mainly for gains, drops, and other directional cues.
First move: figure contract before plotting
Before generating or editing code, establish the contract below.
Backend selection is a blocking gate. If the user has not explicitly chosen Python or R in the current request or provided a clearly language-specific input file/workflow, ask one concise question: Python or R? Then stop and wait for the user's answer. Do not generate mock data, write scripts, create figures, or choose Python/R by default. This overrides general autonomy/default-execution behavior for figure tasks.
What ships with it
14 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 261 B
- evals/evals.json 2.1 KB
- references/api.md 13 KB
- references/backend-selection.md 4.6 KB
- references/chart-types.md 8.7 KB
- references/common-patterns.md 9.6 KB
- references/design-theory.md 15 KB
- references/figure-contract.md 3.9 KB
- references/nature-2026-observations.md 5.5 KB
- references/qa-contract.md 4.6 KB
- references/r-template-index.md 3.8 KB
- references/r-workflow.md 5.2 KB
- references/tutorials.md 7.9 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.
- 2mo ago First seen · 198 lines · 142 tokens per session scan A b78feb089e4c
nature-figure is a skill published in the GitHub repository ResearAI/DeepScientist (3,408 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 142 tokens to every session and 2,644 once invoked, about $0.0006 per session on Opus 5.5. 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.
nature-figure compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| nature-figureResearAI/DeepScientist | Skill | 3.4k | 4mo ago | Apache-2.0 | A | 142 | Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | today | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |