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 run-llama/vibe-llama --skill pdf-processinggit clone --depth 1 https://github.com/run-llama/vibe-llamaWrote 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/run-llama/vibe-llama/pdf-processing)<a href="https://agentmods.dev/skills/run-llama/vibe-llama/pdf-processing"><img src="https://agentmods.dev/badge/skills/run-llama/vibe-llama/pdf-processing/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/run-llama/vibe-llama/pdf-processing"><img src="https://agentmods.dev/badge/skills/run-llama/vibe-llama/pdf-processing.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 | $0.00045 | $0.00272 |
| Opus 5.5 | $0.00018 | $0.00109 |
| Sonnet 5.5 | $0.00009 | $0.00054 |
| Haiku 4.5 | $0.00005 | $0.00027 |
Grade A, and why
PDF Processing 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.
What it actually says
PDF Processing
Quick start
Extract text:
from llama_cloud_services import LlamaParse
parser = LlamaParse(
parse_mode="parse_page_with_agent",
model="openai-gpt-4-1-mini",
high_res_ocr=True,
adaptive_long_table=True,
outlined_table_extraction=True,
output_tables_as_HTML=True,
result_type="markdown",
project_id=project_id,
organization_id=organization_id,
)
result = parser.parse("./my_file.pdf")
documents = result.get_markdown_documents(split_by_page=True)
full_text = ""
for document in documents:
full_text += document.text + "\n\n---\n\n"
For more detailed code implementations, see REFERENCE.md.
Requirements
The llama_cloud_services package must be installed in your environment:
pip install llama_cloud_services
And the LLAMA_CLOUD_API_KEY must be available as an environment variable:
export LLAMA_CLOUD_API_KEY="..."
What ships with it
1 file 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.
- 1mo ago First seen · 51 lines · 45 tokens per session scan A a1003f4f3637
PDF Processing is a skill published in the GitHub repository run-llama/vibe-llama (175 stars, last pushed 11mo ago), licensed MIT. It adds 45 tokens to every session and 272 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 2026-08-30.
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