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 eddiebelaval/squire --skill data-analyzergit clone --depth 1 https://github.com/eddiebelaval/squireWrote 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/eddiebelaval/squire/data-analyzer)<a href="https://agentmods.dev/skills/eddiebelaval/squire/data-analyzer"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/data-analyzer/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/eddiebelaval/squire/data-analyzer"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/data-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 314 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00019 | $0.03093 |
| Opus 5 | $0.00010 | $0.01546 |
| Sonnet 5 | $0.00004 | $0.00619 |
| Haiku 4.5 | $0.00002 | $0.00309 |
Grade A, and why
Data Analyzer 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 7d 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 — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analyzer
Expert data analysis agent that processes structured and unstructured datasets to extract meaningful insights, identify patterns, detect anomalies, and generate data-driven recommendations. Specializes in exploratory data analysis, statistical testing, correlation analysis, and insight storytelling.
This skill applies rigorous analytical frameworks, statistical methods, and data visualization best practices to transform raw data into actionable intelligence. Perfect for business analytics, research validation, performance analysis, and decision support.
Core Workflows
Workflow 1: Exploratory Data Analysis (EDA)
Objective: Understand dataset structure, quality, and preliminary patterns
Steps:
-
Data Profiling
- Dataset dimensions (rows, columns)
- Column types and formats
- Data completeness (missing values, nulls)
- Unique values and cardinality
- Data ranges and distributions
- Generate summary statistics (mean, median, mode, std dev)
-
Data Quality Assessment
- Missing data patterns (MCAR, MAR, MNAR)
- Duplicate records
- Outliers and anomalies
- Data consistency issues
- Format and type mismatches
- Document data quality issues with severity ratings
-
Univariate Analysis
- Distribution analysis for each variable
- Identify skewness and kurtosis
- Detect outliers (IQR, Z-score methods)
- Visualize distributions (histograms, box plots, density plots)
-
Bivariate Analysis
- Correlation analysis (Pearson, Spearman)
- Scatter plots for continuous variables
- Cross-tabulations for categorical variables
- Identify strong relationships and dependencies
-
Multivariate Analysis
- Correlation matrices
- Dimensionality assessment
- Feature importance preliminary analysis
- Cluster tendency analysis
-
Initial Insights
- Key patterns and trends
- Surprising findings
- Hypotheses for further investigation
- Data limitations and caveats
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.
- 7d ago First seen · 403 lines · 19 tokens per session scan A 3f754127f07b
Data Analyzer is a skill published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 25d ago), licensed MIT. It adds 19 tokens to every session and 3,093 once invoked, about $0.0001 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 2026-09-03.
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