# Defuddle

> Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.

- Skill: `techwavedev/defuddle` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/defuddle`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/defuddle/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/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:

```bash
defuddle parse <url> --md
```

Save to file:

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

Extract specific metadata:

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

## Output formats

| Flag | Format |
|------|--------|
| `--md` | Markdown (default choice) |
| `--json` | JSON with both HTML and markdown |
| (none) | HTML |
| `-p <name>` | Specific metadata property |

---

<!-- AGI-INTEGRATION-START -->

## 🧠 AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Qdrant Memory Integration

Before executing complex tasks with this skill:
```bash
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:
```bash
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

<!-- AGI-INTEGRATION-END -->