ml-data-pipeline-architecture skillA
ml-data-pipeline-architecture is agent-read markdown (skill) from terrylica/cc-skills: Patterns for efficient ML data pipelines using Polars, Arrow, and ClickHouse. TRIGGERS - data pipeline, polars vs pandas, arrow format.
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
# ML Data Pipeline Architecture Patterns for efficient ML data pipelines using Polars, Arrow, and ClickHouse. **ADR**: [2026-01-22-polars-preference-hook](/docs/adr/2026-01-22-polars-preference-hook.md) (efficiency preferences framework) > **Note**: A PreToolUse hook enforces Polars preference. To use Pandas, add `# polars-exception: <reason>` at file top. > **Self-Evolving Skill**: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues. ## When to Use This Skill Use this skill when: - Deciding between Polars and Pandas for a data pipeline - Optimizing memory usage with zero-copy Arrow patterns - Loading data from ClickHouse into PyTorch DataLoaders - Implementing lazy evaluation for large datasets - Migrating existing Pandas code to Polars --- ## 1. Decision Tree: Polars vs Pandas ``` Dataset size? ├─ < 1M rows → Pandas OK (simpler API, richer ecosystem) ├─ 1M-10M rows → Consider Polars (2-5x faster, less memory) └─ > 10M rows → Use Polars (required for memory efficiency) Operations? ├─ Simple transforms → Either works …
Read the whole file at its exact version.
How to install
mdr add terrylica/cc-skills/ml-data-pipeline-architecture@git:20260506.c488415mdr add terrylica/cc-skills/ml-data-pipeline-architecture@sha256:478ee1faf9068128Pin 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_opl5umjh4jdcydo4)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260506.c488415 latest | 2026-05-06 | c488415 | 10,839 B | A | view · diff |
| git:20260401.b664eaf | 2026-04-01 | b664eaf | 10,907 B | A | view · diff |
| git:20260401.6be22af | 2026-04-01 | 6be22af | 10,685 B | A | view · diff |
| git:20260220.1e9f460 | 2026-02-20 | 1e9f460 | 10,132 B | A | view · diff |
| git:20260131.6c12174 | 2026-01-31 | 6c12174 | 10,100 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 (10839 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
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Source
terrylica/cc-skills · 74 stars · license MIT · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_opl5umjh4jdcydo4 GET https://markdownregistry.com/api/v1/resolve?ref=terrylica/cc-skills/ml-data-pipeline-architecture GET https://markdownregistry.com/api/v1/blob/478ee1faf90681285b45dc4e9210fd8ebc171f373b34ef10af80432e68afa3b9
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