What is leakage-check?

A command that checks machine-learning preprocessing and data-splitting code for target leakage. Target leakage means that information about the answer accidentally enters the model's inputs.

Use it to audit scalers, encoders, imputers, train-validation-test splits, and features that reveal the target.

It catches unsafe transformations and features that can make test results falsely high, such as fitting a scaler on all data before splitting it.

leakage-check — Command for Claude Code by yeaight7/agent-powerups

Install

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.

Clone the repo

git clone --depth 1 https://github.com/yeaight7/agent-powerups

Made for: Claude Code.

Or install machine-learning-ops, the plugin that ships this one along with the rest of its 7 skills, 4 commands, 3 agents.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/yeaight7/agent-powerups/leakage-check/github.svg)](https://agentmods.dev/commands/yeaight7/agent-powerups/leakage-check)

Your own site

<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/leakage-check"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/leakage-check/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/leakage-check"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/leakage-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 220 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00010	$0.00220
Opus 5.5	$0.00004	$0.00088
Sonnet 5.5	$0.00002	$0.00044
Haiku 4.5	$0.00001	$0.00022

Measured 1mo ago against content hash 1bcf450eac10, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

leakage-check 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 1mo 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.

plugins/machine-learning-ops/commands/leakage-check.md · 18 lines

What it actually says