dfdl_ref — community dfdl_ref, CodeAnatomy, community, ide skills

v1.0.0

About this Skill

Perfect for Data Analysis Agents needing comprehensive reference guides for DataFusion, PyArrow, DeltaLake, and UDF APIs. DataFusion + DeltaLake operations manual for this repo. DataFusion is the core query engine; DeltaLake provides the storage layer and integrates tightly via scan providers, schema bridging, and predic

paul-heyse paul-heyse
[0]
[0]
Updated: 2/24/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
26
Canonical Locale
en
Detected Body Locale
en

Perfect for Data Analysis Agents needing comprehensive reference guides for DataFusion, PyArrow, DeltaLake, and UDF APIs. DataFusion + DeltaLake operations manual for this repo. DataFusion is the core query engine; DeltaLake provides the storage layer and integrates tightly via scan providers, schema bridging, and predic

Core Value

Empowers agents to accurately implement DataFusion, PyArrow, DeltaLake, and UDF APIs by providing a detailed reference map, ensuring adherence to existing local patterns and avoiding unnecessary guesses, leveraging libraries like DataFusion and PyArrow.

Ideal Agent Persona

Perfect for Data Analysis Agents needing comprehensive reference guides for DataFusion, PyArrow, DeltaLake, and UDF APIs.

Capabilities Granted for dfdl_ref

Debugging API integrations with DataFusion and DeltaLake
Implementing efficient data processing pipelines using PyArrow
Validating UDF implementations against reference materials

! Prerequisites & Limits

  • Requires access to local environment for version and method probing
  • Limited to DataFusion, PyArrow, DeltaLake, and UDF APIs

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is dfdl_ref?

Perfect for Data Analysis Agents needing comprehensive reference guides for DataFusion, PyArrow, DeltaLake, and UDF APIs. DataFusion + DeltaLake operations manual for this repo. DataFusion is the core query engine; DeltaLake provides the storage layer and integrates tightly via scan providers, schema bridging, and predic

How do I install dfdl_ref?

Run the command: npx killer-skills add paul-heyse/CodeAnatomy. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for dfdl_ref?

Key use cases include: Debugging API integrations with DataFusion and DeltaLake, Implementing efficient data processing pipelines using PyArrow, Validating UDF implementations against reference materials.

Which IDEs are compatible with dfdl_ref?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for dfdl_ref?

Requires access to local environment for version and method probing. Limited to DataFusion, PyArrow, DeltaLake, and UDF APIs.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add paul-heyse/CodeAnatomy. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use dfdl_ref immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

dfdl_ref

Install dfdl_ref, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Operating rule: never guess DataFusion/DeltaLake/PyArrow/UDF APIs

When uncertain:

  1. Probe local environment (versions + available methods).
  2. Search the repo for how we already use it.
  3. Open the relevant reference file below (only the section you need).
  4. Implement using existing local patterns unless the plan says otherwise.

Reference map (open these files as needed)

  • Core DataFusion Python surfaces (IO, catalog, SQL, DataFrame API): reference/datafusion.md
  • "Best-in-class deployment gaps" (caching, stats, observability, planning knobs): reference/datafusion_addendum.md
  • Planning deep dive (logical/physical plan pipeline, introspection, optimization rules): reference/datafusion_planning.md
  • Rust UDF contracts (Scalar/UDAF/UDWF/Async/named args): reference/datafusion_rust_UDFs.md
  • Schema management + schema pitfalls: reference/datafusion_schema.md
  • DeltaLake ↔ DataFusion integration details: reference/deltalake_datafusion_integration.md
  • Advanced Rust integration (PyO3 packaging, wheels, CI, native module distribution): reference/datafusion_deltalake_advanced_rust_integration.md
  • DataFusionMixins trait (Delta snapshot schema + predicate parsing helpers): reference/deltalake_datafusionmixins.md
  • Plan combination (composing DataFusion plans via joins/unions/CTEs, Delta integration, parameterized queries, plan serialization): reference/datafusion_plan_combination.md
  • Rust LogicalPlan programmatic construction (LogicalPlanBuilder, Expr, schema/DFSchema, plan rewriting via TreeNode, extensibility, serialization): reference/Datafusion_logicplan_rust.md
  • DataFusion tracing (Rust community extension: execution spans, metrics capture, partial-result previews, rule-phase instrumentation, OpenTelemetry export): reference/datafusion-tracing.md
  • DeltaLake core (format/protocol, client APIs, 3-layer model): reference/deltalake.md

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