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 skills/maxvaega/skillkit/pdf-extractornpx skills add maxvaega/skillkit --skill pdf-extractorgit clone --depth 1 https://github.com/maxvaega/skillkitWhat 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 | $0.00012 | $0.00349 |
| Opus 5 | $0.00006 | $0.00175 |
| Sonnet 5 | $0.00002 | $0.00070 |
| Haiku 4.5 | $0.00001 | $0.00035 |
Grade A, and why
pdf-extractor 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.
What it actually says
PDF Extractor Skill
This skill provides tools for extracting text and metadata from PDF documents and converting them to different formats.
Available Scripts
extract.py
Extracts text and metadata from PDF files.
Input:
{
"file_path": "/path/to/document.pdf",
"pages": "all" | [1, 2, 3]
}
Output:
{
"text": "Extracted text content...",
"metadata": {
"title": "Document Title",
"author": "Author Name",
"pages": 10
}
}
convert.sh
Converts PDF files to different formats (text, markdown, etc.).
Input:
{
"input_file": "/path/to/input.pdf",
"output_format": "txt" | "md" | "html"
}
parse.py
Parses structured data from PDF forms and tables.
Input:
{
"file_path": "/path/to/form.pdf",
"extract_tables": true,
"extract_forms": true
}
Usage Example
from skillkit import SkillManager
manager = SkillManager()
result = manager.execute_skill_script(
skill_name="pdf-extractor",
script_name="extract",
arguments={"file_path": "document.pdf", "pages": "all"}
)
if result.success:
print(result.stdout)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 78 lines · 12 tokens per session scan A 9b8c2ed4f334
pdf-extractor is a skill published in the GitHub repository maxvaega/skillkit (147 stars, last pushed 7mo ago), licensed MIT. It adds 12 tokens to every session and 349 once invoked, about $0.0001 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 2026-08-30.
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