ReddRadar
Agent skill
ddgo
CLI tool for DuckDuckGo search, supporting result summaries and AI-driven answer extraction. Core Scenario: When the user needs to perform web searches or get AI-summarized answers from DuckDuckGo results.
Install this agent skill to your Project
npx add-skill https://github.com/x-cmd/skill/tree/main/data/x-cmd/ddgo
SKILL.md
ddgo - DuckDuckGo CLI Search
The ddgo module provides a powerful terminal-based interface for DuckDuckGo, enabling users to perform web searches, extract results in structured formats, and leverage AI for summarizing answers.
When to Activate
- When the user wants to perform a privacy-focused web search via CLI.
- When needing an AI-generated summary of search results for a specific query.
- When exporting search results to JSON for further processing.
- When performing site-specific searches (e.g.,
site:x-cmd.com).
Core Principles & Rules
- AI Integration: Use the
--aiflag to automatically select and summarize the most relevant search result. - Data-Friendly: Supports structured JSON output for script integration.
- Structured Viewing: Use
dump --appfor an interactive table view of search results.
Patterns & Examples
AI-Summarized Search
# Get an AI summary of search results for 'bash tips'
x ddgo --ai "bash best practices"
Site-Specific Search
# Search for 'jq' content on the x-cmd.com website
x ddgo dump --json "site:x-cmd.com jq"
Top Results
# Retrieve the top 10 search results for a query
x ddgo --top 10 "python threading tutorial"
Checklist
- Confirm if the user needs a general search or an AI-summarized answer.
- Verify if the results should be limited to a specific site or number.
- Check if JSON output is required for subsequent tasks.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
x-cmd/skill
pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
x-cmd/skill
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
x-cmd/skill
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
x-cmd/skill
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
x-cmd/skill
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
x-cmd/skill
astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Didn't find tool you were looking for?