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/danielrosehill/claude-data-analyst-plugin/correlation-analysisnpx skills add danielrosehill/Claude-Data-Analyst-plugin --skill correlation-analysisgit clone --depth 1 https://github.com/danielrosehill/Claude-Data-Analyst-pluginWrote 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/danielrosehill/claude-data-analyst-plugin/correlation-analysis)<a href="https://agentmods.dev/skills/danielrosehill/claude-data-analyst-plugin/correlation-analysis"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-data-analyst-plugin/correlation-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 | $0.00057 | $0.00474 |
| Opus 5 | $0.00028 | $0.00237 |
| Sonnet 5 | $0.00011 | $0.00095 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
correlation-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 3d 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.
What it actually says
Correlation Analysis
Produce a first-pass correlation report for a dataset in a folder.
Inputs
- Path to a dataset file (CSV, TSV, Parquet, XLSX) or folder containing one.
- Optional: correlation method (
pearsondefault,spearmanfor non-linear/ranked,kendallfor small-n or many ties). - Optional: target variable — if given, rank all other numeric columns by absolute correlation to it.
Recommended CLI tooling
duckdb— fastest way to load mixed formats and runCORR(x, y)in SQL.uv run --with pandas --with scipy python -c '...'— for Spearman/Kendall and heatmap export.csvstat(csvkit) — quick column types and null counts before correlating.
Procedure
- Load and profile: identify file format, row count, and column dtypes. Drop or flag non-numeric columns.
- Null/variance screen: exclude columns with >50% nulls or zero variance — note them in the report.
- Compute correlation matrix using the chosen method.
- Rank pairs by absolute correlation. Report:
- Top 5 positive (r > 0.3)
- Top 5 negative (r < -0.3)
- Any suspicious pairs (|r| > 0.95 — likely duplicates or derived columns)
- Caveats: correlation is not causation; check for confounders; Pearson assumes linearity; small-n results are unreliable (flag if n < 30).
Output
Write a markdown report next to the dataset (<dataset>-correlations.md) with:
- Method used and sample size
- Ranked pair table
- Flagged column exclusions
- One-paragraph plain-English summary of the strongest relationships
If a --target was given, lead with a ranked list of predictors of that target.
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.
- 3d ago First seen · 43 lines · 57 tokens per session scan A a54ddfe025f1
correlation-analysis is a skill published in the GitHub repository danielrosehill/Claude-Data-Analyst-plugin (11 stars, last pushed 4mo ago), licensed MIT. It adds 57 tokens to every session and 474 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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