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/param087/agent-ml-skills/exploratory-data-analysisnpx skills add param087/agent-ml-skills --skill exploratory-data-analysisgit clone --depth 1 https://github.com/param087/agent-ml-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/param087/agent-ml-skills/exploratory-data-analysis)<a href="https://agentmods.dev/skills/param087/agent-ml-skills/exploratory-data-analysis"><img src="https://agentmods.dev/badge/skills/param087/agent-ml-skills/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 | $0.00056 | $0.00763 |
| Opus 5 | $0.00028 | $0.00381 |
| Sonnet 5 | $0.00011 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00076 |
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 4d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exploratory Data Analysis (EDA)
Overview
EDA is the disciplined first pass over a dataset: understand shape, types, distributions, missingness, relationships, and red flags before writing a single model. Skipping it is the #1 cause of silent modeling failures (leakage, broken splits, garbage features).
When to use
- A new dataset just landed.
- You're asked to "look at", "profile", "explore", or "summarize" data.
- A model underperforms and you need to understand the inputs.
Workflow
Follow this order. Do not jump to modeling until every step is answered.
- Shape & types — rows, columns, dtypes, memory. Are numeric columns actually numeric?
- Missingness — per-column null counts and patterns (MCAR/MAR/MNAR). Is missingness itself predictive?
- Target analysis — distribution of the target (class balance / skew). This decides metrics and resampling.
- Univariate — distributions of each feature (histograms, value counts, describe()).
- Bivariate — feature vs target relationships; correlation matrix for numeric.
- Leakage scan — features too perfectly correlated with the target, IDs, timestamps, or post-outcome columns.
- Cardinality & outliers — high-cardinality categoricals, extreme values.
Reference snippet
import pandas as pd
df = pd.read_csv("data.csv")
# 1. Shape & types
print(df.shape)
print(df.dtypes.value_counts())
print(df.memory_usage(deep=True).sum() / 1e6, "MB")
# 2. Missingness
miss = df.isna().mean().sort_values(ascending=False)
print(miss[miss > 0])
# 3. Target (classification example)
print(df["target"].value_counts(normalize=True))
# 4-5. Numeric summary + correlations with target
num = df.select_dtypes("number")
print(num.describe().T)
print(num.corr()["target"].sort_values(ascending=False))
# 6. Leakage red flag: |corr| ~ 1.0 with target
corr_t = num.corr()["target"].drop("target").abs()
print("LEAKAGE SUSPECTS:", corr_t[corr_t > 0.95].index.tolist())
For a fast automated first look, ydata-profiling (ProfileReport(df)) or df.describe(include="all") is acceptable — but never let a tool replace the manual leakage scan.
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.
- 4d ago First seen · 71 lines · 56 tokens per session scan A f2fa14cfbcb7
exploratory-data-analysis is a skill published in the GitHub repository param087/agent-ml-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 56 tokens to every session and 763 once invoked, about $0.0003 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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