utility-pro_skill

This skill enables AI-enhanced CLI mastery, structured data transformations, and fast forensic-style debugging to boost terminal productivity.
  • Python

7

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

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npx veilstrat add skill yuniorglez/gemini-elite-core --skill utility-pro

  • SKILL.md4.9 KB

Overview

This skill turns a terminal into a high-performance utility toolbelt for modern engineering workflows. It focuses on AI-augmented CLI practices, structured data manipulation, and fast forensic search to convert raw text and logs into actionable results. The goal is safer, faster, reproducible transformations across monorepos and APIs.

How this skill works

The skill combines Rust-powered tools (ripgrep, fd, bat, eza) with structured shells (Nushell) and data transformers (jq, yq) to treat streams and files as queryable tables. It provides tactical patterns for search, multi-file refactor, and API debugging by composing small, auditable commands into repeatable pipelines. Safety checks, dry-runs, and .gitignore-aware searches are built into recommended workflows.

When to use it

  • Hunting bugs or TODOs across large monorepos with minimal noise
  • Performing multi-file symbol renames or mass refactors safely
  • Inspecting and transforming JSON/YAML streams as structured tables
  • Debugging HTTP APIs with rich, colorized request/response output
  • Replacing slow legacy grep/find pipelines with fast, safe tools
  • Automating repeatable terminal workflows into scripts or Nushell functions

Best practices

  • Prefer ripgrep (rg) over grep; it respects .gitignore and is much faster
  • Treat JSON/YAML as tables in Nushell instead of fragile text parsing
  • Use dry-runs and small samples before running destructive commands like sed or rm
  • Favor non-capturing groups and named captures in RegEx to improve readability and performance
  • Compose single-purpose tools (xh | jq | rg) into auditable pipelines
  • Keep transformations idempotent and add verification steps for automation

Example use cases

  • Export all TODO comments to a JSON table for triage using rg and Nushell
  • Rename a component across .tsx files with fd + sed while previewing changes
  • Extract active project IDs from a nested JSON payload using jq
  • POST a debug payload to an API with xh and follow redirects with headers and typed fields
  • Replace slow 'ls | grep' ad-hoc checks with fd or eza filters in CI checks

FAQ

Nushell treats JSON/CSV/YAML as structured tables, making many transformations simpler, safer, and easier to reason about than text-only pipelines.

When should I still use sed or awk?

Use sed/awk for compact, text-centric edits where structured parsing is unnecessary or unavailable, but prefer Nushell/jq for JSON/YAML and ripgrep/fd for search and file selection.

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utility-pro skill by yuniorglez/gemini-elite-core | VeilStrat