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.
npx agentmods add commands/witt3rd/claude-plugins/scrape-urlgit clone --depth 1 https://github.com/witt3rd/claude-pluginsWhat 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 | $0.00000 | $0.00864 |
| Opus 5 | $0.00000 | $0.00432 |
| Sonnet 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 2d 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.
- 2d 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 10mo 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
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.