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kdense-data-viz-selected

kdense-data-viz-selected is a skill for Codex from Lzy599775/agent-auto-sci-skills. It costs 93 tokens per session (470 once invoked), scanned A, original, MIT.

A selected toolkit for exploring data, running statistical analysis, and creating scientific charts. It covers tools such as Matplotlib, Seaborn, NetworkX, Polars, and Dask, with guidance for publication-ready figures.

In plain words
What is it for?
Use it for exploratory data analysis, statistical tests, network diagrams, large-data processing, and scientific figures for papers or reports.
Why use it?
It helps organize analysis before chart-making and checks issues such as missing data, unusual values, units, uncertainty, and coverage.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 2repo 6d ago A scan 93 tokens original MIT

Good fit Use it for exploratory data analysis, statistical tests, network diagrams, large-data processing, and scientific figures for papers or reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected
View source ↗ Lzy599775/agent-auto-sci-skills lzy599775.github.io
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.

Any agent
npx skills add Lzy599775/agent-auto-sci-skills --skill kdense-data-viz-selected
Clone the repo
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skills

Made for: Codex.

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 kdense-data-viz-selected

README.md
[![agentmods](https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected/github.svg)](https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected)
Your own site
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected/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.

agentmods 80×15 button for kdense-data-viz-selected

Your own site · 80×15
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/kdense-data-viz-selected.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 470 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00093 $0.00470
Opus 5 $0.00046 $0.00235
Sonnet 5 $0.00019 $0.00094
Haiku 4.5 $0.00009 $0.00047

Measured 12d ago against content hash edfab2053bd6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

kdense-data-viz-selected 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 12d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (subskills/k-dense/exploratory-data-analysis/scripts/__init__.py, subskills/k-dense/exploratory-data-analysis/scripts/_capabilities.py, subskills/k-dense/exploratory-data-analysis/scripts/_common.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/kdense-data-viz-selected/SKILL.md · 52 lines

What it actually says

K-Dense Data/Viz Selected

This wrapper packages selected Data Analysis & Visualization skills from K-Dense-AI/scientific-agent-skills for Auto-sci-research.

Use it when a task needs:

  • exploratory data analysis;
  • statistical test selection and reporting;
  • assumption diagnostics, effect sizes, uncertainty, or power analysis;
  • Matplotlib, Seaborn, NetworkX, Polars, or Dask technical guidance;
  • publication-grade scientific figures.

Included Upstream Subskills

Located in subskills/k-dense/:

  • exploratory-data-analysis
  • statistical-analysis
  • matplotlib
  • seaborn
  • scientific-visualization
  • networkx
  • polars
  • dask

Local Adaptation

Use these upstream skills with Auto-sci-research rules:

  1. Start every figure from the claim it must support.
  2. Audit units, missingness, outliers, groups, and spatial/temporal coverage before statistical analysis.
  3. Use effect sizes and uncertainty, not only p-values.
  4. For bibliometric visuals, connect clusters and networks to field evolution, evidence gaps, and policy relevance.
  5. Export figures at journal-ready dimensions with colorblind-safe palettes and readable captions.

For domain-specific guidance, also read:

  • ../agent-auto-sci-data-viz/references/k_dense_data_viz_mapping.md
  • ../agent-auto-sci-data-viz/references/review_bibliometric_figure_system.md

Must Not Do

  • Do not make a figure that does not answer a manuscript claim.
  • Do not let visual attractiveness replace evidence.
  • Do not hide small sample size, missingness, or uncertainty.
  • Do not imply causality from descriptive charts.
Files

What ships with it

60 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.

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. 12d ago First seen · 52 lines · 93 tokens per session scan A edfab2053bd6

Subscribe to this mod's changes

kdense-data-viz-selected is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 93 tokens to every session and 470 once invoked, about $0.0005 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 2026-08-31.

Analytics and metricsScientificbibliometricscodex-skillsgeospatialliterature-reviewresearch-agenturban-health
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