ml4t-compute-features skillA
ml4t-compute-features is agent-read markdown (skill) from ml4t/skills: Systematic feature computation across multiple assets with group-aware operations. Use when computing technical or fundamental features for a panel of securities..
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
# Compute Features
Computing features across a panel of assets requires group-aware operations - a global rolling mean mixes Apple's history with Tesla's. Every windowed statistic must be computed per symbol.
## The Problem
Features computed with global statistics bleed information across assets. A z-score computed over the full dataframe uses one symbol's volatility to normalize another. Worse, using `.mean()` on the full column leaks future data from late-arriving symbols into early rows.
## The Pattern
### WRONG
```python
import polars as pl
# Global statistics mix symbols and leak future
df = df.with_columns(
mom_zscore=(pl.col("returns") - pl.col("returns").mean())
/ pl.col("returns").std()
)
```
### CORRECT
```python
import polars as pl
# Per-symbol rolling window - no cross-contamination, no lookahead
df = df.sort("symbol", "timestamp").with_columns(
mom_zscore=(
(pl.col("returns") - pl.col("returns").rolling_mean(504).shift(1)) # 504 ≈ 2 trading years
/ pl.col("returns").rolling_std(504).shift(1)
).over("symbol")
)
```
## Windowed Aggregations
Three window types, each with different use cases:
```python
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-compute-features@git:20260901.c415df0mdr add ml4t/skills/ml4t-compute-features@sha256:1edc559800ab0333Pin 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_gjcih6yh47i4m4wy)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,067 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,073 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 (4067 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_gjcih6yh47i4m4wy GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-compute-features GET https://markdownregistry.com/api/v1/blob/1edc559800ab03336bb8a22e35edd605e0a512174fb1331fbf9fb40765f53328
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