Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/legendtkl/agentic-skill-routernpx agentmods add skills/legendtkl/agentic-skill-router/skill-119Wrote 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/skills/legendtkl/agentic-skill-router/skill-119)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-119"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-119/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/skills/legendtkl/agentic-skill-router/skill-119"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-119.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 | $0.00058 | $0.01152 |
| Opus 5 | $0.00029 | $0.00576 |
| Sonnet 5 | $0.00012 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
skill-119 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 8d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Reader
Extract comprehensive content from PDF files including text, tables, images, and metadata using industry-standard Python libraries.
When to Use This Skill
Use this skill when:
- Analyzing brand documents, design briefs, or company materials in PDF format
- Extracting structured data from reports, invoices, or forms with tables
- Retrieving images, logos, or graphics embedded in PDFs
- Converting PDF content into markdown or structured formats for further processing
- Investigating PDF metadata, page layouts, or document structure
How It Works
This skill combines two powerful libraries:
- pdfplumber - Excels at text extraction with layout preservation and table detection
- PyMuPDF - Provides fast image extraction and comprehensive metadata access
Quick Start
Install Dependencies
pip install pdfplumber pymupdf pillow
Extract from PDF
python scripts/extract_pdf.py <path/to/document.pdf> --output-dir ./output
Output Structure
The script creates the following output:
output/
├── extracted_text.md # All text content with page numbers
├── extracted_tables.json # Structured table data
├── metadata.json # PDF metadata and page info
└── images/ # Extracted images
├── page_1_image_1.png
├── page_2_image_1.jpeg
└── ...
Usage in Claude Code
When a user provides a PDF file for analysis:
- Check dependencies: Verify pdfplumber and PyMuPDF are installed
- Run extraction script: Execute
scripts/extract_pdf.pywith the PDF path - Analyze output: Read the generated markdown and JSON files
- Present findings: Summarize key content, tables, and images found
Example workflow:
# Step 1: Extract PDF content
python scripts/extract_pdf.py "brand_document.pdf" --output-dir ./brand_analysis
# Step 2: Read extracted text
cat ./brand_analysis/extracted_text.md
# Step 3: Analyze tables
cat ./brand_analysis/extracted_tables.json
# Step 4: Check images
ls ./brand_analysis/images/
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.
- 8d ago First seen · 186 lines · 58 tokens per session scan A 02a9e72c46bc
skill-119 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 1,152 once invoked, about $0.0003 per session on Opus 5. 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 2026-09-03.
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