ml4t-registry-system skillA
ml4t-registry-system is agent-read markdown (skill) from ml4t/skills: Content-addressed experiment tracking for ML trading models. Use when versioning models, features, or experiment artifacts for reproducibility..
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
# Experiment Registry
Without a registry, you overwrite the best model every time you retrain. Content-addressed storage - where hash(config) determines the storage path - makes every experiment reproducible and comparable without manual bookkeeping.
## The Problem
A quant runs 50 model configurations. Results go into `model_v2_final_FINAL.pkl`. Next week, a new run overwrites it. The team cannot answer: which hyperparameters produced the best IC? Was that before or after the feature change? Did we already try alpha=0.01? Without structured tracking, experiments are lost, repeated, and unverifiable.
## The Pattern
### WRONG
```python
import pickle
# Overwrite on every run - no history, no comparison, no provenance
model.fit(X_train, y_train)
with open("best_model.pkl", "wb") as f:
pickle.dump(model, f)
# Three weeks later: "Which config was this? What data did it use?"
```
### CORRECT
```python
import hashlib
import json
import sqlite3
from datetime import datetime
def config_hash(config: dict) -> str:
"""Deterministic hash of experiment config."""
blob = json.dumps(config, sort_keys=True).encode()
return hashlib.sha256(blob).hexdigest()[:12]
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-registry-system@git:20260901.c415df0mdr add ml4t/skills/ml4t-registry-system@sha256:c0f669d047fe7908Pin 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_4auifwxcbyvy4yv5)
1 badge views in 30 days
Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,839 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,880 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 (4839 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_4auifwxcbyvy4yv5 GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-registry-system GET https://markdownregistry.com/api/v1/blob/c0f669d047fe79085a0a46693f9d286ae595ea97de992938fa7844ff182bacd7
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.