ml4t-validate-data skillA
ml4t-validate-data is agent-read markdown (skill) from ml4t/skills: Systematic data quality validation before modeling. Use when checking for gaps, outliers, stale prices, or schema violations in datasets..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Validate Data
Unvalidated data silently corrupts models - a single unadjusted stock split can make a momentum signal look 10x stronger than reality.
## The Problem
Financial data arrives with missing values, duplicate timestamps, unadjusted corporate actions, stale prices, and impossible OHLC relationships. Using raw data without checks means your model trains on artifacts. A 50% overnight return that is actually a 2:1 split will dominate any feature that touches price changes. You will not see this in your loss function - the model happily fits the noise.
## The Pattern
### WRONG
```python
import polars as pl
# Trust the data, start modeling immediately
df = pl.read_parquet("prices.parquet")
features = df.with_columns(ret=pl.col("close").pct_change())
model.fit(features) # Trained on splits, gaps, duplicates
```
### CORRECT
```python
import polars as pl
def validate_ohlcv(df: pl.DataFrame) -> dict[str, int]:
"""Run standard OHLCV quality checks. Returns issue counts."""
issues = {}
# OHLC consistency: high >= low, close within [low, high]
issues["high_lt_low"] = df.filter(pl.col("high") < pl.col("low")).height
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-validate-data@git:20260901.c415df0mdr add ml4t/skills/ml4t-validate-data@sha256:50a64b8bbbcc161bPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_nig5djubjaarfsoz)
1 badge views in 30 days
Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,256 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,266 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (4256 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_nig5djubjaarfsoz GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-validate-data GET https://markdownregistry.com/api/v1/blob/50a64b8bbbcc161b8cd5f781f55062722874ef76b83c9f237e46fde9c9c15ade
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.