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 kclemoveki/agentic-skills-eda --skill analyze-datasetgit clone --depth 1 https://github.com/kclemoveki/agentic-skills-edaWrote 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/kclemoveki/agentic-skills-eda/analyze-dataset)<a href="https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/analyze-dataset"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/analyze-dataset/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/kclemoveki/agentic-skills-eda/analyze-dataset"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/analyze-dataset.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 | $0.00036 | $0.02842 |
| Opus 5 | $0.00018 | $0.01421 |
| Sonnet 5 | $0.00007 | $0.00568 |
| Haiku 4.5 | $0.00004 | $0.00284 |
Grade A, and why
analyze-dataset 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Analyze Dataset
Generate a professional, reproducible Jupyter notebook that analyzes the dataset at $ARGUMENTS.
Step 0 — Look for sibling artifacts (composition with other skills)
Before reading the dataset, check the current working directory and the dataset's directory for artifacts produced by other skills in the suite. These are inputs that improve this skill's output when present:
quality_report_<dataset_stem>.md(produced by/quality-report): if it exists, read it. Use itsBloqueantes,Recomendaciones de limpieza, and dimension scores as authoritative guidance for the cleaning steps in Section 2 of the notebook. The report has already inspected the data — leverage that work. Cite findings from the report in the notebook's Section 2 markdown (e.g. "según el reporte de calidad,Daterequiere parseo a datetime").<dataset>.manifest.yaml(produced by/snapshot-data): if it exists, read it. Verify that the dataset's current SHA-256 matches the manifest. If they differ, print a clear notice in the notebook's Section 1: "⚠️ Dataset has changed since manifest was created (sha256 mismatch). Analysis is on the new version." If they match, mention the manifest as proof of reproducibility.
If neither exists, proceed normally — these are optional enhancers, not requirements.
Step 1 — Understand the data
Before writing any notebook cell:
- Read the file to understand format (CSV, Parquet, JSON, Excel)
- Identify columns, types, and cardinality
- Determine the nature of the data: temporal, categorical, numerical, mixed
- Probe edge cases for any column you plan to parse: run
df[col].unique()(ordf[col].value_counts().head(50)if cardinality is high) for every string column you intend to convert (dates, scores, mangled numerics, etc.). Identify ALL distinct patterns. Never trustdf.head()alone — the first 5 rows are not representative. - Decide which analyses make sense for THIS specific dataset. If a quality report from Step 0 is available, prioritize the cleaning issues it flagged.
What ships with it
2 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.
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.
- 10d ago First seen · 193 lines · 36 tokens per session scan A d4fb121a8cec
analyze-dataset is a skill published in the GitHub repository kclemoveki/agentic-skills-eda (2 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 2,842 once invoked, about $0.0002 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.
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