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 ailabs-393/ai-labs-claude-skills --skill data-analystgit clone --depth 1 https://github.com/ailabs-393/ai-labs-claude-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/ailabs-393/ai-labs-claude-skills/data-analyst)<a href="https://agentmods.dev/skills/ailabs-393/ai-labs-claude-skills/data-analyst"><img src="https://agentmods.dev/badge/skills/ailabs-393/ai-labs-claude-skills/data-analyst/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/ailabs-393/ai-labs-claude-skills/data-analyst"><img src="https://agentmods.dev/badge/skills/ailabs-393/ai-labs-claude-skills/data-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00063 | $0.02220 |
| Opus 5 | $0.00032 | $0.01110 |
| Sonnet 5 | $0.00013 | $0.00444 |
| Haiku 4.5 | $0.00006 | $0.00222 |
Grade A, and why
data-analyst 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 12d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analyst
Overview
This skill provides comprehensive capabilities for data analysis workflows on CSV datasets. It automatically analyzes missing value patterns, intelligently imputes missing data using appropriate statistical methods, and creates interactive Plotly Dash dashboards for visualizing trends and patterns. The skill combines automated missing value handling with rich interactive visualizations to support end-to-end exploratory data analysis.
Core Capabilities
The data-analyst skill provides three main capabilities that can be used independently or as a complete workflow:
1. Missing Value Analysis
Automatically detect and analyze missing values in datasets, identifying patterns and suggesting optimal imputation strategies.
2. Intelligent Imputation
Apply sophisticated imputation methods tailored to each column's data type and distribution characteristics.
3. Interactive Dashboard Creation
Generate comprehensive Plotly Dash dashboards with multiple visualization types for trend analysis and exploration.
Complete Workflow
When a user requests complete data analysis with missing value handling and visualization, follow this workflow:
Step 1: Analyze Missing Values
Run the missing value analysis script to understand the data quality:
python3 scripts/analyze_missing_values.py <input_file.csv> <output_analysis.json>
What this does:
- Detects missing values in each column
- Identifies data types (numeric, categorical, temporal, etc.)
- Calculates missing value statistics
- Suggests appropriate imputation strategies per column
- Generates detailed JSON report and console output
Review the output to understand:
- Which columns have missing data
- The percentage of missing values
- The recommended imputation method for each column
- Why each method was recommended
Step 2: Impute Missing Values
Apply automatic imputation based on the analysis:
python3 scripts/impute_missing_values.py <input_file.csv> <analysis.json> <output_imputed.csv>
What ships with it
7 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.
- 12d ago First seen · 299 lines · 63 tokens per session scan A 3f48d785567f
data-analyst is a skill published in the GitHub repository ailabs-393/ai-labs-claude-skills (445 stars, last pushed 10mo ago), licensed MIT. It adds 63 tokens to every session and 2,220 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 2026-08-30.
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