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/nirholas/three.ws/data-analysisnpx skills add nirholas/three.ws --skill data-analysisgit clone --depth 1 https://github.com/nirholas/three.wsWrote 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/nirholas/three.ws/data-analysis)<a href="https://agentmods.dev/skills/nirholas/three.ws/data-analysis"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/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 | $0.00028 | $0.01084 |
| Opus 5 | $0.00014 | $0.00542 |
| Sonnet 5 | $0.00006 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
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 yesterday.
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
When to use this skill
Use when the user asks about:
- Analyzing numerical data (prices, volumes, metrics)
- Calculating statistics (averages, percentiles, correlations)
- Identifying trends or anomalies in data
- Comparing performance across time periods or assets
- Presenting data in a clear, structured format
Analysis Framework
1. Data Understanding
Before analyzing, assess the data:
- Source: Where does the data come from? Is it reliable?
- Time range: What period does the data cover?
- Granularity: Daily, hourly, per-block?
- Completeness: Are there gaps or missing data points?
- Units: USD, ETH-denominated, percentage, raw count?
- Adjustments needed: Inflation adjustment, normalization, outlier handling?
2. Descriptive Statistics
Compute baseline statistics:
- Central tendency: Mean, median, mode — median is more robust for skewed crypto data
- Dispersion: Standard deviation, range, interquartile range (IQR)
- Distribution shape: Skewness (crypto returns are typically negatively skewed) and kurtosis (fat tails are common)
- Percentiles: 5th, 25th, 50th, 75th, 95th — useful for setting expectations
Present as a summary table:
| Metric | Value |
|---|---|
| Mean | X |
| Median | Y |
| Std Dev | Z |
| Min | A |
| Max | B |
| Count | N |
3. Trend Analysis
Identify and quantify trends:
- Moving averages: 7-day, 30-day, 90-day to smooth noise
- Growth rates: Period-over-period percentage change (daily, weekly, monthly)
- CAGR: Compound Annual Growth Rate for longer-term performance
- Trend direction: Classify as uptrend, downtrend, or sideways based on moving average slopes
- Trend strength: How consistent is the trend? R-squared of linear regression
4. Comparative Analysis
When comparing across entities or time periods:
- Normalize data: Convert to percentage change from a common starting point for fair comparison
- Relative performance: Calculate alpha (excess return) relative to a benchmark (BTC, ETH, or market index)
- Correlation matrix: How closely do the compared items move together?
- Ratio analysis: Asset A / Asset B ratio to identify relative value trends
- Ranking: Order by performance metric with percentile rankings
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.
- yesterday First seen · 116 lines · 28 tokens per session scan A 368290790a14
data-analysis is a skill published in the GitHub repository nirholas/three.ws (110 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 1,084 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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