Defuddle

Defuddle

Use Defuddle CLI to extract clean readable content from web pages. Prefer over WebFetch for standard web pages — it removes navigation, ads, and clutter, reducing token usage.

When to Use

• Use when the user provides a normal webpage URL to read, summarize, or analyze.
• Prefer it over noisy page-fetch approaches when token efficiency matters.
• Use for docs, articles, blog posts, and similar public web content.

If not installed: npm install -g defuddle

Usage

Always use --md for markdown output:

defuddle parse <url> --md

Save to file:

defuddle parse <url> --md -o content.md

Extract specific metadata:

defuddle parse <url> -p title
defuddle parse <url> -p description
defuddle parse <url> -p domain

Output formats

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags defuddle <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns