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 text-classificationgit 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/text-classification)<a href="https://agentmods.dev/skills/run-llama/vibe-llama/text-classification"><img src="https://agentmods.dev/badge/skills/run-llama/vibe-llama/text-classification.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00510 |
| Opus 5 | $0.00026 | $0.00255 |
| Sonnet 5 | $0.00011 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
Classify files according to specific rules 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Texts and Files Classification
Quick start
- Define classification rules:
from llama_cloud.types import ClassifierRule
# Define classification rules (natural language descriptions)
rules = [
ClassifierRule(
type="invoice",
description="Documents that are invoices for goods or services, containing line items, prices, and payment terms",
),
ClassifierRule(
type="contract",
description="Legal agreements between parties, containing terms, conditions, and signatures",
),
ClassifierRule(
type="receipt",
description="Proof of payment documents, typically shorter than invoices, showing items purchased and amount paid",
),
]
- Create the classification client and run the job:
from llama_cloud_services.beta.classifier.client import ClassifyClient
# Initialize client
# Note: the beta client differs in usage slightly compared to other clients in llama-cloud-services
classifier = ClassifyClient.from_api_key(api_key)
# Classify a PDF directly (parsing happens implicitly)
result = await classifier.aclassify_file_path(
rules=rules,
file_input_path="document.pdf",
)
# Access classification results
classification = result.items[0].result
print(f"Predicted Type: {classification.type}")
print(f"Confidence: {classification.confidence:.2%}")
print(f"Reasoning: {classification.reasoning}")
For more detailed code implementations, see REFERENCE.md.
Requirements
The llama_cloud_services package must be installed in your environment (with it come the pydantic and llama_cloud packages):
pip install llama_cloud_services
And the LLAMA_CLOUD_API_KEY must be available as an environment variable:
export LLAMA_CLOUD_API_KEY="..."
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
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.
- 8d ago First seen · 85 lines · 53 tokens per session scan A 3dd25e1e6b6a
Classify files according to specific rules is a skill published in the GitHub repository run-llama/vibe-llama (178 stars, last pushed 10mo ago), licensed MIT. It adds 53 tokens to every session and 510 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 2026-08-30.
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