agentmods.dev

viz

viz is a skill for Claude Code, Codex from matthewdigiuseppe/MStack. It costs 59 tokens per session (991 once invoked), scanned A, original, MIT.

A figure-making skill for R that creates publication-ready charts from research results, with one main claim per figure. It can provide PDF and PNG files plus the data used to make each figure.

In plain words
What is it for?
Use it for plots, coefficient charts, marginal-effects charts, and graphics prepared for papers or slides.
Why use it?
It helps turn statistical results into clear figures with meaningful titles and accessible colors, while keeping the source data available for checking.

Skill for Claude CodeCodex

Installs and runs on its own, but its text points at files inside its plugin — anything it tells you to read at a ${CLAUDE_PLUGIN_ROOT} path is only there once the plugin is installed. Installing the plugin gets both.

Part of the mstack plugin — 38 skills, 1 hook shipped together

not rated 14 7d ago A scan 59 tokens original MIT
View the source file matthewdigiuseppe/MStack
Install

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.

agentmods
npx agentmods add skills/matthewdigiuseppe/mstack/viz
Any agent
npx skills add matthewdigiuseppe/MStack --skill viz
Clone the repo
git clone --depth 1 https://github.com/matthewdigiuseppe/MStack

Made for: Claude Code, Codex.

Or install mstack, the plugin that ships this one along with the rest of its 38 skills, 1 hook.

Wrote 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.

agentmods badge for viz

README.md
[![agentmods](https://agentmods.dev/badge/skills/matthewdigiuseppe/mstack/viz.svg)](https://agentmods.dev/skills/matthewdigiuseppe/mstack/viz)
Your own site
<a href="https://agentmods.dev/skills/matthewdigiuseppe/mstack/viz"><img src="https://agentmods.dev/badge/skills/matthewdigiuseppe/mstack/viz.svg" alt="Measured on agentmods" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 991 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00059 $0.00991
Opus 5 $0.00030 $0.00495
Sonnet 5 $0.00012 $0.00198
Haiku 4.5 $0.00006 $0.00099

Measured 4d ago against content hash ffba524a6fa7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

viz 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 4d 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.

skills/viz/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/mstack:viz

Stage: analyze Voice: figure-designer (anchored to r-coding-skills)

When to invoke

After /mstack:analyze and /mstack:robustness produce stable results. Before /mstack:draft-section results. Figures lock the visual argument; once stable, the prose can describe them.

Procedure

  1. Load. output/models/, data/clean/analytic.rds. If target_journals in .mstack/config.yaml is set, match that journal's figure conventions (column width, color policy) — ask the user for the artwork specs when unknown rather than guessing them.

  2. Decide the figure set. A typical IPE / political science paper has 1–3 figures. Each is a claim:

    Figure type Claim it makes
    Coefficient plot "Here are the estimated effects with CIs."
    Marginal effects / predicted probability "Here is what the model implies at substantively interesting values."
    Heterogeneity plot "The effect varies across S as theory predicts."
    Specification curve "Findings are robust across reasonable design choices."
    Map "Spatial / contextual variation is X."
    Density / distribution "Here is the relevant variation in the data."

    Pick the figures that carry the headline. Skip figures that just decorate.

  3. Write code/03-figures.R. First copy the bundled theme — ${CLAUDE_PLUGIN_ROOT}/skills/viz/assets/theme_mstack.R → code/theme_mstack.R, once per paper, so the replication package is self-contained — and source() it: it provides theme_mstack(), Okabe–Ito color scales, and save_figure() (PDF + PNG + underlying CSV in one call). Conventions:

    • One ggplot object per figure, named fig_<n>_<descriptor>.
    • Title states the claim, not the variables (e.g., "Trade exposure raises protectionist vote share", not "Effect of trade on voting").
    • Subtitle / caption carries the methodological detail.
    • Colorblind-safe palette (viridisLite::viridis() or Okabe–Ito).
    • No chart-junk: no shadows, no 3D, no gradient fills, no excess gridlines.
    • Theme: theme_minimal() or a custom journal-style theme; consistent across figures.
    • Save each figure to both output/figures/<name>.pdf (vector, for the paper) and output/figures/<name>.png (raster, for slides / web). Use ggsave(..., width = X, height = Y, units = "in") with explicit dimensions; never dev.off() without dimensions.
    • Save underlying figure data to output/figures/<name>-data.csv so the figure is replicable from CSV alone.

Read the full file on GitHub · 74 lines

Files

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.

Changes

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.

  1. 4d ago First seen · 74 lines · 59 tokens per session scan A ffba524a6fa7

Subscribe to this mod's changes

viz is a skill published in the GitHub repository matthewdigiuseppe/MStack (14 stars, last pushed 7d ago), licensed MIT. It adds 59 tokens to every session and 991 once invoked, about $0.0003 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.

Analytics and metricsScientific
Related

Other skills, from other repositories

biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…

K-Dense-AI/scientific-agent-skills · 76 tokens

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…

K-Dense-AI/scientific-agent-skills · 83 tokens

nature-statistics

Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model…

Yuan1z0825/nature-skills · 139 tokens

evaluating-with-leakage-gates

Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or…

maziyarpanahi/openmed · 158 tokens

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…

maziyarpanahi/openmed · 205 tokens

mixed-precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

aiming-lab/AutoResearchClaw · 25 tokens