Codeg is a collaborative workspace that brings multiple AI coding agents and their sessions into one searchable application. Developers use it to coordinate agents, delegate work, and review unattended coding tasks across desktop, server, Docker, and mobile clients. The catalogue entries are skills and instructions for workflows within Codeg.
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 xintaofei/codeg --skill exploratory-data-analysisgit clone --depth 1 https://github.com/xintaofei/codegWrote 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/xintaofei/codeg/exploratory-data-analysis)<a href="https://agentmods.dev/skills/xintaofei/codeg/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/exploratory-data-analysis/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/xintaofei/codeg/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/exploratory-data-analysis.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 | $0.00089 | $0.03346 |
| Opus 5.5 | $0.00036 | $0.01338 |
| Sonnet 5.5 | $0.00018 | $0.00669 |
| Haiku 4.5 | $0.00009 | $0.00335 |
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 1mo 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
97% identical to exploratory-data-analysis — 3 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploratory Data Analysis
Overview
Perform comprehensive exploratory data analysis (EDA) on scientific data files across multiple domains. This skill provides automated file type detection, format-specific analysis, data quality assessment, and generates detailed markdown reports suitable for documentation and downstream analysis planning.
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.
Reference file: references/chemistry_molecular_formats.md
2. Bioinformatics and Genomics Formats (50+ extensions)
Sequence data, alignments, annotations, variants, and expression data.
File types include: .fasta, .fastq, .sam, .bam, .vcf, .bed, .gff, .gtf, .bigwig, .h5ad, .loom, .counts, .mtx, and more.
Reference file: references/bioinformatics_genomics_formats.md
3. Microscopy and Imaging Formats (45+ extensions)
Microscopy images, medical imaging, whole slide imaging, and electron microscopy.
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.
- 1mo ago First seen · 444 lines · 89 tokens per session scan A 600ce47d3cc9
exploratory-data-analysis is a skill published in the GitHub repository xintaofei/codeg (3,887 stars, last pushed 5d ago), licensed Apache-2.0. It adds 89 tokens to every session and 3,346 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to exploratory-data-analysis, differing in 3 lines, and is treated as a copy.
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