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 nimrodfisher/data-analytics-skills --skill programmatic-edagit clone --depth 1 https://github.com/nimrodfisher/data-analytics-skillsWrote 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/nimrodfisher/data-analytics-skills/programmatic-eda)<a href="https://agentmods.dev/skills/nimrodfisher/data-analytics-skills/programmatic-eda"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/programmatic-eda/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/nimrodfisher/data-analytics-skills/programmatic-eda"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/programmatic-eda.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 | $0.00520 |
| Opus 5.5 | $0.00014 | $0.00208 |
| Sonnet 5.5 | $0.00007 | $0.00104 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
programmatic-eda 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.
How it starts
The opening of the file, as written. The whole thing — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
- You receive a new dataset and need to understand its shape and quality before analysis
- An analysis produces surprising numbers and you want to verify the underlying data first
- A stakeholder asks "is this data reliable?" or "what's in this table?"
- You're about to run a model or statistical test and need data-quality assurance
Process
- Load and overview — run
scripts/data_overview.pyto get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents). - Null profile — run
scripts/null_profiler.py; compare output against thresholds inreferences/quality_thresholds.mdand flag columns above limits. - Outlier detection — run
scripts/outlier_detector.py(IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error? - Distribution summary — run
scripts/distribution_summary.pyfor descriptive stats and univariate histograms on each numeric column. - Correlation exploration — run
scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy. - EDA checklist sign-off — work through
references/eda_checklist.mdand confirm each item before declaring the dataset profiled. - Write findings — fill
assets/eda_report_template.mdwith full profiling output; distil top issues intoassets/findings_summary.md.
For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.
Inputs the skill needs
- Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
- Required: business context — what does one row represent?
- Optional: quality threshold overrides (defaults in
references/quality_thresholds.md) - Optional: columns to skip (PII, binary blobs, high-cardinality IDs)
Output
assets/eda_report_template.md(filled) — full profiling report with per-column statsassets/findings_summary.md(filled) — top 3–5 quality issues and recommended next steps- Console output / plots from scripts for interactive inspection
What ships with it
10 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/eda_report_template.md 2.6 KB
- assets/findings_summary.md 1.4 KB
- references/eda_checklist.md 3.5 KB
- references/pandas_polars_recipes.md 3.1 KB
- references/quality_thresholds.md 2.3 KB
- scripts/correlation_explorer.py 2.7 KB runs code
- scripts/data_overview.py 2.2 KB runs code
- scripts/distribution_summary.py 2.3 KB runs code
- scripts/null_profiler.py 2.2 KB runs code
- scripts/outlier_detector.py 2.8 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 · 33 lines · 36 tokens per session scan A c024fb196137
programmatic-eda is a skill published in the GitHub repository nimrodfisher/data-analytics-skills (485 stars, last pushed 25d ago), licensed MIT. It adds 36 tokens to every session and 520 once invoked, about $0.0001 per session on Opus 5.5. 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.
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