foxctl Data Operations

Read and write repository files while querying JSON/YAML content with jq.

3|Updated Apr 14, 2026
One-click install
npx skills add https://github.com/joshka0/foxctl --skill foxctl-data-operations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: foxctl Data Operations
Source: https://github.com/joshka0/foxctl/tree/main/configs/skills-condensed/foxctl-data
Command: npx skills add https://github.com/joshka0/foxctl --skill foxctl-data-operations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the friction of manually opening files and transforming structured data by letting you read/write repository files and run deterministic jq queries over JSON or YAML.

Core Features & Use Cases

  • File I/O: Read files, write content, list directories, generate trees, and find files by pattern.
  • Structured Data Processing: Query and transform JSON/YAML inputs using jq, including YAML-to-JSON style workflows.
  • Large File Handling: Offloads large file contents into CAS to keep operations efficient.

Quick Start

Ask your agent: read README.md and return the filtered JSON fields defined by this jq query: ".sections | map({title, summary})".

Frequently Asked Questions about foxctl Data Operations

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query and filter specific fields from a JSON file in my repository?▼

To query and filter specific fields from a JSON file, you can use jq-based querying to extract structured data. This allows you to run deterministic jq queries over JSON content directly from repository files to retrieve targeted fields.

Can I use jq to transform YAML configurations into JSON?▼

Yes, you can transform YAML configurations into JSON workflows using jq-based querying with optional yaml_input parsing. This enables deterministic querying and transformation of YAML structured data alongside standard JSON processing.

What is the best way to read repository files and extract codebase context?▼

The best way to read repository files and extract codebase context is through automated file I/O operations. You can read files, list directories, and generate trees to retrieve context without manually opening them.

How do you handle querying large structured datasets without slowing down operations?▼

To handle querying large structured datasets efficiently, large file contents are offloaded into CAS-backed storage. This content-addressable storage mechanism keeps file read, write, and query operations fast and efficient.

Does this approach support writing transformed data back into repository files?▼

Yes, this approach supports writing transformed data back into repository files using fs/write operations. You can read structured inputs, apply jq transformations, and write the modified content back to the repository.

What limitations exist when using jq for repo automation and file I/O?▼

A key limitation is that jq-based querying is designed for JSON and YAML formats, meaning it cannot parse unstructured text or other file types. Operations are constrained to reading, writing, and querying supported structured data.