Classify files according to specific rules

Classify files according to specific rules is a skill for Claude Code, Codex from run-llama/vibe-llama. It costs 53 tokens per session (510 once invoked), scanned A, original, MIT.

Instructions for classifying documents and files with LlamaCloud, a service that uses rules to assign categories such as invoices or contracts. It includes Python examples for sending files to the classifier.

In plain words
What is it for?
Use it to define document categories and classify PDFs or other file inputs through the LlamaCloud services package.
Why use it?
It gives a defined way to sort documents by their contents instead of handling each file manually.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to define document categories and classify PDFs or other file inputs through the LlamaCloud services package.

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Install with agentmods
npx agentmods add skills/run-llama/vibe-llama/text-classification
Install

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.

Any agent
npx skills add run-llama/vibe-llama --skill text-classification
Clone the repo
git clone --depth 1 https://github.com/run-llama/vibe-llama

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for Classify files according to specific rules

README.md
[![agentmods](https://agentmods.dev/badge/skills/run-llama/vibe-llama/text-classification.svg)](https://agentmods.dev/skills/run-llama/vibe-llama/text-classification)
Your own site
<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>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 510 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 3dd25e1e6b6a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

documentation/skills/text-classification/SKILL.md · 85 lines

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

Read the full file on GitHub · 85 lines

Files

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.

Changes

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.

  1. 8d ago First seen · 85 lines · 53 tokens per session scan A 3dd25e1e6b6a

Subscribe to this mod's changes

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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