synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 agentmods add skills/synthetic-sciences/openscience/exploratory-data-analysisnpx skills add synthetic-sciences/openscience --skill exploratory-data-analysisgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/exploratory-data-analysis)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/exploratory-data-analysis.svg" alt="Measured on agentmods" 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 | $0.00071 | $0.03461 |
| Opus 5 | $0.00036 | $0.01731 |
| Sonnet 5 | $0.00014 | $0.00692 |
| Haiku 4.5 | $0.00007 | $0.00346 |
Grade A, and why
exploratory-data-analysis 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 yesterday.
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
8 near-identical copies found in the catalogue:
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
- exploratory-data-analysis — 91% identical, 26 lines differ
How it starts
The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploratory Data Analysis
Overview
Perform exploratory data analysis (EDA) on scientific data files across multiple domains. Match the depth and output to the request: a narrow calculation should stay a narrow calculation, while a requested full audit can include broader quality assessment and documentation.
Scope and output contract
- The user's requested scope and output format take precedence over this workflow.
- Do not create a report, figure, artifact, directory, or sidecar file by default.
- Do not write to disk unless the user requested a saved deliverable or the task inherently requires one.
- For a bounded question, inspect only the necessary data and answer inline with the decisive calculation and caveats.
- Recommend a visualization only when it materially clarifies the result; generate one only when requested or necessary for the requested deliverable.
Key Capabilities:
- Automatic detection and analysis of 200+ scientific file formats
- Comprehensive format-specific metadata extraction
- Data quality and integrity assessment
- Statistical summaries and distributions
- Visualization recommendations
- Downstream analysis suggestions
- Markdown report generation
When to Use This Skill
Use this skill when:
- User provides a path to a scientific data file for analysis
- User asks to "explore", "analyze", or "summarize" a data file
- User wants to understand the structure and content of scientific data
- User needs a comprehensive report of a dataset before analysis
- User wants to assess data quality or completeness
- User asks what type of analysis is appropriate for a file
Supported File Categories
The skill has comprehensive coverage of scientific file formats organized into six major categories:
1. Chemistry and Molecular Formats (60+ extensions)
Structure files, computational chemistry outputs, molecular dynamics trajectories, and chemical databases.
File types include: .pdb, .cif, .mol, .mol2, .sdf, .xyz, .smi, .gro, .log, .fchk, .cube, .dcd, .xtc, .trr, .prmtop, .psf, and more.
What ships with it
8 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.
- assets/report_template.md 3.4 KB
- references/bioinformatics_genomics_formats.md 21 KB
- references/chemistry_molecular_formats.md 22 KB
- references/general_scientific_formats.md 15 KB
- references/microscopy_imaging_formats.md 18 KB
- references/proteomics_metabolomics_formats.md 15 KB
- references/spectroscopy_analytical_formats.md 18 KB
- scripts/eda_analyzer.py 21 KB runs code
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.
- yesterday First seen · 453 lines · 71 tokens per session scan A b446837f2c9a
exploratory-data-analysis is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 71 tokens to every session and 3,461 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
meta-paper-write
Use this meta-skill instead of answering directly when the current user asks to draft or produce a new academic/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure/table placeholders, drafting, length checks…
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-section-author
Write one publication-style research-paper section as a bounded, citation-grounded LaTeX fragment from a writing plan, outline, citation plan, and optional figure/table context.
meta-arxiv-daily-digest-deck
Fetch the day's top arXiv submissions in a chosen category, write a structured per-paper digest, render the digest as a PPTX deck (one slide per paper), and persist the digest to long-term memory. Use for a daily 'arxiv morning briefing' — manual fire or cron-scheduled.
paper-quality-gate
Deterministic pre-compile gate for meta-paper-write. Enforces length/citation verdicts and rejects unsupported empirical-result claims when no user evidence was supplied.
paper-latex-sanitizer
Deterministically normalize safe LaTeX punctuation and replace unsupported forecast magnitudes with explicit placeholders before meta-paper-write publication gates run.