ml4t-case-study-development skillA
ml4t-case-study-development is agent-read markdown (skill) from ml4t/skills: Stage-gated research workflow from hypothesis through data prep, feature engineering, modeling, and backtest. Use when developing a trading strategy end-to-end with disciplined gate checks..
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
# Case Study Development Workflow
A case study is not just a pipeline; it is a sequence of research decisions. Clean artifacts are necessary, but the real question is whether each stage earned the right to move to the next one.
## The Problem
A researcher puts data loading, feature engineering, model training, and backtesting in a single notebook. It runs once and produces a Sharpe ratio. Later, they change the label horizon and must re-run everything. The model section silently reads stale features. The backtest uses predictions from a previous run. Nobody knows which config produced the final result. The pipeline is unreproducible and the results are untrustworthy.
## The Pattern
### WRONG
```python
import lightgbm as lgb
import polars as pl
# Everything in one notebook - no artifact boundaries, no config
data = pl.read_parquet("etf_data.parquet")
features = data.select(["momentum_12m", "volatility_20d"])
labels = data["close"].pct_change(21).shift(-21)
model = lgb.LGBMRegressor().fit(features, labels) # Full-sample train!
predictions = model.predict(features)
# Backtest on training predictions with no cost model
cumulative_return = (predictions * labels).cumsum()
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-case-study-development@git:20260901.c415df0mdr add ml4t/skills/ml4t-case-study-development@sha256:86a4927cf91df2d0Pin 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_7w5oxhgjeyjxmopx)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 5,424 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 5,398 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 (5424 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_7w5oxhgjeyjxmopx GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-case-study-development GET https://markdownregistry.com/api/v1/blob/86a4927cf91df2d078297138b2e90220addaf437e9e740069d373b96186361fc
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