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 serejaris/kimi-skills --skill correlation-auditorgit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/correlation-auditor)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/correlation-auditor"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/correlation-auditor/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/serejaris/kimi-skills/correlation-auditor"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/correlation-auditor.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00071 | $0.01299 |
| Opus 5 | $0.00036 | $0.00649 |
| Sonnet 5 | $0.00014 | $0.00260 |
| Haiku 4.5 | $0.00007 | $0.00130 |
Grade A, and why
correlation-auditor 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 10d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
correlation-auditor
Correlation analysis toolkit — computes Pearson/Spearman correlation matrices and partial correlation matrices from tabular data, and automatically flags suspected spurious correlations caused by confounding variables.
Capabilities
| Feature | Description |
|---|---|
| Pearson Correlation Matrix | Linear correlation coefficients + p-values; suitable for continuous, approximately normal variables |
| Spearman Correlation Matrix | Rank correlation coefficients + p-values; suitable for nonlinear monotonic relationships or ordinal variables |
| Partial Correlation Matrix | Net correlations after controlling for all other variables (precision matrix method); reveals direct associations between variables |
| Spurious Correlation Detection | Automatically compares bivariate correlations with partial correlations and flags falsely significant correlations driven by confounders |
| Plain-Language Interpretation | Provides a readable summary of correlation strength, significance, and partial-correlation changes for each variable pair |
Quick Start
# Analyze correlations across all numeric columns
python3 scripts/correlation_explorer.py data.csv
# Analyze only specific columns
python3 scripts/correlation_explorer.py data.csv -f "age,income,spending,score"
# Compute Pearson only
python3 scripts/correlation_explorer.py data.csv -m pearson
# Save results to JSON
python3 scripts/correlation_explorer.py data.csv -o result.json
Detailed Usage
Basic Invocation
python3 scripts/correlation_explorer.py <data-file> [options]
Choosing the Correlation Method
# Compute both Pearson and Spearman (default)
python3 scripts/correlation_explorer.py data.csv -m all
# Pearson only
python3 scripts/correlation_explorer.py data.csv -m pearson
# Spearman only
python3 scripts/correlation_explorer.py data.csv -m spearman
Tuning Spurious-Correlation Detection Sensitivity
# Stricter: alert when the coefficient drops by 30%
python3 scripts/correlation_explorer.py data.csv -d 0.3
# More lenient: alert only when the coefficient drops by 70%
python3 scripts/correlation_explorer.py data.csv -d 0.7
# Use a 0.01 significance level
python3 scripts/correlation_explorer.py data.csv -a 0.01
What ships with it
2 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.
- 10d ago First seen · 140 lines · 71 tokens per session scan A 65721344746d
correlation-auditor is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,299 once invoked, about $0.0004 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 2026-08-31.
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