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 Boom5426/Nature-Paper-Skills --skill figure-stylegit clone --depth 1 https://github.com/Boom5426/Nature-Paper-SkillsWrote 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/boom5426/nature-paper-skills/figure-style)<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/figure-style"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/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/boom5426/nature-paper-skills/figure-style"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/figure-style.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 | $0.00226 | $0.04516 |
| Opus 5 | $0.00113 | $0.02258 |
| Sonnet 5 | $0.00045 | $0.00903 |
| Haiku 4.5 | $0.00023 | $0.00452 |
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 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.
This is a copy
91% identical to figure-style — 137 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 — 358 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. Once loaded, call apply_figure_style() before plotting.
Dependencies
The prose checklist (§0–§9) is dependency-free: it is guidance you apply by hand.
The kernel.py helpers require matplotlib, numpy, and scipy
(scipy.stats.t, used by bar_with_points(errorbar="ci95") for the
t-distribution 95% CI). In a plain skill install the helpers are not
auto-loaded by a host kernel; load them explicitly before calling
apply_figure_style() and the other helpers, e.g. exec(open("kernel.py").read())
or import the module (import kernel).
§0 Scope
Load trigger: 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-planner
supplies per-figure planning (one claim per figure, panel roles) and
nature-figure supplies the full multi-panel, whole-paper production workflow.
§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.
What ships with it
1 file 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 Changed · +5 lines 01f9621c74ff
- 12d ago First seen · 353 lines · 226 tokens per session scan A baca87c795c1
figure-style is a skill published in the GitHub repository Boom5426/Nature-Paper-Skills (490 stars, last pushed 7d ago), licensed MIT. It adds 226 tokens to every session and 4,516 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to figure-style, differing in 137 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…