Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/witt3rd/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/witt3rd/claude-plugins/scrape-url)<a href="https://agentmods.dev/commands/witt3rd/claude-plugins/scrape-url"><img src="https://agentmods.dev/badge/commands/witt3rd/claude-plugins/scrape-url/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/witt3rd/claude-plugins/scrape-url"><img src="https://agentmods.dev/badge/commands/witt3rd/claude-plugins/scrape-url.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00864 |
| Opus 5.5 | $0.00000 | $0.00346 |
| Sonnet 5.5 | $0.00000 | $0.00173 |
| Haiku 4.5 | $0.00000 | $0.00086 |
Grade A, and why
scrape-url scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scrape URL and Create Note
Scrape content from a URL and create a comprehensive knowledge graph note using question-oriented content synthesis.
Usage
/scrape-url <url> [note_filename]
Process
Step 1: Content Acquisition (Web-Specific)
Scrape the URL using mcp__firecrawl__firecrawl_scrape:
- Format: markdown
- Include main content only
- Extract all text, examples, code snippets
Analyze page metadata:
- Extract title, author, publication date if available
- Identify domain/source (blog, documentation, academic, etc.)
- Note any special formatting or structure
Read the scraped content:
- This is the raw content to be synthesized
Step 2: Universal Content Processing
Follow the shared content synthesis pipeline documented in _shared_content_synthesis.md:
-
Apply Question-Oriented Content Synthesis
- Phase 1: Central Question Discovery
- Phase 2: Domain Question Extraction
- Phase 3: Specific and Atomic Question Decomposition
- Phase 4: Progressive Answer Development
-
Generate note structure and metadata
- Determine filename based on central question
- Assign 3-6 tags mixing dimensions
- Create web-specific YAML frontmatter
-
MOC Integration
- Read TOPICS.md to understand MOC structure
- Identify appropriate MOC(s) for this note
- Update MOC file(s) with wikilink and description
-
Relationship Discovery
- Search for related notes in knowledge graph
- Establish bidirectional relationships
- Update related notes
Web-Specific Note Structure
---
tags: [domain, technology, content-type]
source: <original_url>
date_added: <ISO_date>
---
# Note Title (Based on Central Question)
## Central Question
**Question**: [The single overarching question the content addresses]
**Executive Summary**: 2-3 paragraphs previewing key insights and how the content resolves the central question.
## Part I: [Domain Question 1]
### [Specific Question 1.1]
**Question**: [Clear, specific question from this section]
**Answer**: [Comprehensive response including:
- Direct answer to the question
- Supporting evidence from article (specific quotes, examples, data)
- Technical details and concrete information
- Implications and connections to broader themes]
### [Specific Question 1.2]
**Question**: [Next specific question]
**Answer**: [Evidence-based response...]
## Part II: [Domain Question 2]
### [Specific Question 2.1]
**Question**: [Clear question]
**Answer**: [Comprehensive response with article evidence...]
[Continue with additional parts and sections as needed]
## Resolution: [Answer to Central Question]
Synthesize domain insights to definitively resolve the central question posed at the beginning.
## Related Concepts
### Prerequisites
- [[prerequisite]] - Why needed first
### Related Topics
- [[related]] - Connection explanation
### Extends
- [[base_concept]] - What this builds upon
## References
- [Article Title](<original_url>) - Brief description
- Accessed: <date>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 133 lines · 0 tokens per session scan A a7cc7c5d0aca
scrape-url is a command published in the GitHub repository witt3rd/claude-plugins (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 864 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.