About
The c-network skill provides modern CLI tools for network debugging and API testing, featuring doggo for DNS lookups and httpie for human-readable HTTP requests. It enables DNS queries with various record types and nameservers, plus intuitive HTTP interactions with JSON support and file transfers. Use this skill when you need to troubleshoot network issues or test APIs directly from your terminal with cleaner output than traditional tools.
Quick Install
Claude Code
Recommendednpx skills add daxaur/openpaw -a claude-code/plugin add https://github.com/daxaur/openpawgit clone https://github.com/daxaur/openpaw.git ~/.claude/skills/c-networkCopy and paste this command in Claude Code to install this skill
Documentation
Networking
doggo (DNS client)
# Basic DNS lookup
doggo example.com
# Specific record type
doggo example.com MX
doggo example.com AAAA
doggo example.com TXT
doggo example.com NS
doggo example.com CNAME
# Use specific nameserver
doggo example.com --nameserver 1.1.1.1
doggo example.com --nameserver 8.8.8.8
# DNS over HTTPS
doggo example.com --class IN --type A --nameserver https://cloudflare-dns.com/dns-query
# JSON output
doggo example.com --json
httpie (HTTP client)
Human-friendly alternative to curl:
# GET request
http GET api.example.com/users
# POST with JSON body
http POST api.example.com/users name=John [email protected]
# Headers
http GET api.example.com Authorization:"Bearer token123"
# Download file
http --download https://example.com/file.zip
# Form upload
http --form POST api.example.com [email protected]
# With auth
http -a user:password GET api.example.com/protected
# Follow redirects
http --follow GET example.com
# Show only response headers
http --headers GET example.com
# Verbose (show request + response)
http --verbose GET example.com
Guidelines
- Use
doggofor DNS debugging instead ofdigornslookup - Use
http(httpie) for API testing instead of curl — output is colorized and formatted - For POST requests, httpie auto-detects JSON vs form data
key=valuesends as JSON string,key:=123sends as JSON number
GitHub Repository
Frequently asked questions
What is the c-network skill?
c-network is a Claude Skill by daxaur. Skills package instructions and resources that Claude loads on demand, so Claude can perform c-network-related tasks without extra prompting.
How do I install c-network?
Use the install commands on this page: add c-network to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does c-network belong to?
c-network is in the Other category, tagged dns, http, networking and api.
Is c-network free to use?
Yes. c-network is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.
