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 skills add DANG-ai/SKILLER --skill pdfgit clone --depth 1 https://github.com/DANG-ai/SKILLERWrote 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/dang-ai/skiller/pdf)<a href="https://agentmods.dev/skills/dang-ai/skiller/pdf"><img src="https://agentmods.dev/badge/skills/dang-ai/skiller/pdf/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/dang-ai/skiller/pdf"><img src="https://agentmods.dev/badge/skills/dang-ai/skiller/pdf.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 | $0.00050 | $0.01805 |
| Opus 5.5 | $0.00020 | $0.00722 |
| Sonnet 5.5 | $0.00010 | $0.00361 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
pdf 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 295 lines · 50 tokens per session scan A 38d8559d4899
pdf is a skill published in the GitHub repository DANG-ai/SKILLER (7 stars, last pushed 2mo ago), with no licence file. It adds 50 tokens to every session and 1,805 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
llm-wiki
Build and maintain a persistent knowledge base (LLM Wiki) using native backend tools. Supports ingesting PDFs, Markdown, and TXT files, and querying or linting the data. Use this whenever the user wants to add, retrieve, or manage their own local knowledge base and documents.
financial-document-parser
Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
evo-sales-pivot-analysis
End-to-end demographic analysis pipeline that extracts population data from PDF, reads income data from Excel, merges datasets, computes range-based income quartiles, generates four openpyxl pivot table objects, and exports a five-sheet Excel workbook.
evo-pdf-excel-extraction
Extracts tabular data from PDF files using pdfplumber and reads Excel files using pandas/openpyxl, returning normalized pandas DataFrames with proper data types.
evo-mass-report-output
Orchestrates the full pipeline from STL parsing to JSON mass report output. Coordinates parsing, component analysis, volume computation, unit conversion, and mass calculation.
evo-pdf-form-filler
Fills PDF AcroForm fields (text and checkboxes) using pypdf, sets NeedAppearances flag, and writes the output PDF. Handles checkbox state detection and proper value updates.