Open Science is a local-first, model-agnostic workbench for reproducible scientific research. Scientists use its AI agents, Python and R execution, data connectors, and traceable outputs for tasks such as literature review, analysis, simulation, and visualization across macOS, Windows, and Linux.
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 aipoch/open-science --skill figure-stylegit clone --depth 1 https://github.com/aipoch/open-scienceWrote 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/aipoch/open-science/figure-style)<a href="https://agentmods.dev/skills/aipoch/open-science/figure-style"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/figure-style/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/aipoch/open-science/figure-style"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/figure-style.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 | $0.00091 | $0.04831 |
| Opus 5.5 | $0.00036 | $0.01932 |
| Sonnet 5.5 | $0.00018 | $0.00966 |
| Haiku 4.5 | $0.00009 | $0.00483 |
Grade A, and why
figure-style 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 27d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- figure-style — 91% identical, 172 lines differ
- figure-style — 89% identical, 166 lines differ
- figure-style — 86% identical, 190 lines differ
- figure-style — 83% identical, 178 lines differ
How it starts
The opening of the file, as written. The whole thing — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Publication-Grade Figure Rules
A checklist for correct, legible, internally-consistent scientific figures. This
skill does not impose a visual house style — frame, font, and palette are
parameters. Before plotting, follow the Notebook call below and invoke
apply_figure_style().
Open-Science Notebook call
Every notebook_execute request whose code uses a function named in this skill
includes this skill ID:
{ "kernelSkillIds": ["figure-style"], "code": "apply_figure_style()\n..." }
kernelSkillIds contains the skill ID; function calls belong in code. This
request is complete as written: call the named functions directly and do not add
an import or discovery step.
§0 Scope
Load trigger (same rule as the system prompt's "Publication-grade plots" section): this skill is for final-deliverable figures — those shipping in a report, paper, or export, or saved as an artifact the user will keep — not for exploratory/intermediate plots (quick looks, EDA, sanity checks), which are drawn plainly without it. Once loaded, "every plot" below means every plot you render toward the deliverable.
§1–§3, §8, and §9 are correctness — they apply to every plot, in every
context, and have no aesthetic content. §4–§7 are guidance — defaults that
produce a clean result but that a deliberate alternative can override
(individual rules inside §4–§7 that state a factual/perceptual invariant — e.g.
§4.4 semantic-zero centering, §4.5 CVD, §6.9 leader anchoring — still bind). On
its own, this skill is the inner tier (make one plot good); figure-composer
and paper-narrative supply multi-panel and whole-paper context.
§1 Data fidelity & self-consistency
1.1 Excluded rows. A row marked excluded or flagged in the source data is either omitted entirely or drawn with a visually distinct open/hatched marker and named in the key. It never enters a summary statistic plotted alongside the included rows.
1.2 Comparable conditions only. Arms measured under non-comparable conditions (different N, epoch budget, initialization, protocol) are not plotted as visual peers. Separate them with a facet break or a marker on the label, and state the difference once in the caption.
What ships with it
2 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.
- 27d ago Changed · +12 lines 85817cda5f54
- 1mo ago Changed f2838d98f5ea
- 1mo ago Changed · +19 lines c8345dd1af43
- 1mo ago First seen · 379 lines · 91 tokens per session scan A ccf752423515
figure-style is a skill published in the GitHub repository aipoch/open-science (5,301 stars, last pushed today), licensed Apache-2.0. It adds 91 tokens to every session and 4,831 once invoked, about $0.0004 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.
Other skills, from other repositories
journal-club-ppt
Instructions for turning a scientific paper PDF into a journal-club presentation or literature report. They require reconstructing the paper's scientific logic before creating the slides.
figure-style
Correctness and legibility checklist for publication figures, plus a matplotlib sidecar. Load before plotting anything and call applyfigurestyle() (role-mapped font ladder, outward ticks, frameless legends, 300-dpi saves, CJK-safe fonts). Covers data fidelity, label budgets, axis/colour/type rules, chart choice by…
research-integrity-audit
A research-integrity review of a scientific manuscript's figures and reported numbers. It looks for possible image reuse or alteration and numerical patterns that may indicate data inconsistencies.
analysis-workflow
Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data…
audit-biomedical-paper-evidence
A reviewer for life-science and biomedical papers, PDFs, figures, captions, and screenshots. It examines the scientific question, experiments, evidence, causality, and limitations.
literature-review
Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.