Stat Eda

What it does

When your agent uses it

• The user has a dataset and needs to understand its structure
• Detect anomalies
• Prepare data for further analysis — even if they say what does this data look like
• Find interesting patterns

Example prompts

• “what does this data look like”
• “find interesting patterns”
• “clean this data”
• “/stat-eda”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Split BEFORE explore (see IRON LAW above)
2. 2Missing data pattern matters more than count: MCAR is safe to impute; MNAR (e.g. high-income respondents skip income question) requires…
3. 3Simpson's paradox check: If a trend holds in the aggregate but reverses within subgroups, the aggregate trend is misleading. Always…
4. 4Data leakage in features: A feature that perfectly correlates with the target is usually derived FROM the target (e.g. "refund_amount"…

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Stat Eda loads about 954 tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 240 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~100

When it runs · the whole SKILL.md, loaded when a task matches

~954

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 240 words, ~954 tokens.

name

stat-eda

description

Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.

metadata.category

WP-21 設計/資訊/傳播/公衛

metadata.tags

data-analysis, eda, statistics, visualization

IRON LAW: Perform EDA Only AFTER Train/Test Split — Or You Leak the Future

Agents know "do EDA first." But they almost always do EDA on the FULL
dataset before splitting. This is information leakage: you've seen the
test set's distributions, outliers, and correlations, and your subsequent
modeling choices (feature scaling, outlier treatment, imputation strategy)
are now informed by data the model shouldn't see. Split first, then EDA
only on the training set. Apply the same transformations to the test set
without re-examining it.

Exception: data quality checks (nulls, dtypes, duplicates) CAN run on
the full dataset since they don't inform model hyperparameters.

# EDA Report: {Dataset Name}

## Dataset Overview
- Rows: {N}, Columns: {N}
- Date range: {if applicable}
- Key columns: {description}

## Data Quality
| Issue | Columns Affected | Count/% | Action |
|-------|-----------------|---------|--------|
| Missing values | {cols} | {N / %} | {drop / impute / investigate} |
| Outliers | {cols} | {N} | {cap / remove / keep} |
| Duplicates | — | {N} | {remove} |

## Key Statistics
| Variable | Mean | Median | Std | Min | Max | Distribution |
|----------|------|--------|-----|-----|-----|-------------|
| {var} | ... | ... | ... | ... | ... | {normal/skewed/bimodal} |

## Key Findings
1. {insight with supporting data}
2. {insight}
3. {insight}

## Recommendations
- {next analysis step or data issue to resolve}

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in stat-eda of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/missing-data.md

Open the folder on GitHubat commit 4e7f4f8