Cloudinary Automation

Cloudinary Automation is a skill for Claude Code, Codex from openteams-lab/openteams. It costs 29 tokens per session (1,987 once invoked), scanned A, original, Apache-2.0.

A Cloudinary connection for managing hosted images, videos, and other files. Cloudinary is a media service that stores assets and can organize, transform, and monitor them.

In plain words
What is it for?
It helps organize folders, search for assets, manage upload presets and transformations, look up resources, and monitor Cloudinary usage.
Why use it?
It removes repetitive media-library work, such as creating folders, finding assets, configuring upload presets, and checking usage.

Skill for Claude CodeCodex

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

Good fit It helps organize folders, search for assets, manage upload presets and transformations, look up resources, and monitor Cloudinary usage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openteams-lab/openteams/cloudinary-automation
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 openteams-lab/openteams --skill cloudinary-automation
Clone the repo
git clone --depth 1 https://github.com/openteams-lab/openteams

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 Cloudinary Automation

README.md
[![agentmods](https://agentmods.dev/badge/skills/openteams-lab/openteams/cloudinary-automation/github.svg)](https://agentmods.dev/skills/openteams-lab/openteams/cloudinary-automation)
Your own site
<a href="https://agentmods.dev/skills/openteams-lab/openteams/cloudinary-automation"><img src="https://agentmods.dev/badge/skills/openteams-lab/openteams/cloudinary-automation/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.

agentmods 80×15 button for Cloudinary Automation

Your own site · 80×15
<a href="https://agentmods.dev/skills/openteams-lab/openteams/cloudinary-automation"><img src="https://agentmods.dev/badge/skills/openteams-lab/openteams/cloudinary-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,987 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Memory Poisoning · line 51
    Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.
    Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
How audits are shown
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 $0.00029 $0.01987
Opus 5 $0.00015 $0.00993
Sonnet 5 $0.00006 $0.00397
Haiku 4.5 $0.00003 $0.00199

Measured 8d ago against content hash 64c90fdcccc3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

Cloudinary Automation 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.

assets/skills/cloudinary-automation/SKILL.md · 186 lines

How it starts

The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cloudinary Automation

Automate Cloudinary media management workflows -- create folders, configure upload presets, look up assets, manage transformations, search folders, and monitor usage -- all through natural language.

Toolkit docs: composio.dev/toolkits/cloudinary


Setup

  1. Add the Rube MCP server to your environment: https://rube.app/mcp
  2. Connect your Cloudinary account when prompted (API key auth via Composio)
  3. Start issuing natural language commands for Cloudinary automation

Core Workflows

1. Organize Assets with Folders

Create folder structures for organizing hosted images, videos, and raw files.

Tool: CLOUDINARY_CREATE_FOLDER

Key parameters:

  • folder -- full path of the new asset folder (required), e.g., images/events/2023

Supporting tools:

  • CLOUDINARY_SEARCH_FOLDERS -- search folders by name, path, or creation date using Lucene-like expressions
    • expression -- search filter (e.g., name:sample AND path:events)
    • max_results -- 1-500 results (default 50)
    • sort_by -- list of sort objects (e.g., [{"created_at": "desc"}])
    • next_cursor -- pagination cursor
  • CLOUDINARY_GET_RESOURCES_BY_ASSET_FOLDER -- list assets within a specific folder

Example prompt:

"Create a folder called 'marketing/campaigns/spring-2026' in Cloudinary"


2. Configure Upload Presets

Define centralized upload behavior including target folder, allowed formats, transformations, tags, and overwrite rules.

Tool: CLOUDINARY_CREATE_UPLOAD_PRESET

Key parameters:

  • name -- preset name (auto-generated if omitted)
  • folder -- target folder path for uploads (e.g., samples/)
  • allowed_formats -- comma-separated list (e.g., jpg,png,webp)
  • tags -- comma-separated tags to apply (e.g., marketing,thumbnail)
  • transformation -- incoming transformation (e.g., c_limit,w_500)
  • eager -- eager transformations to generate on upload (e.g., c_fill,g_face,h_150,w_150)
  • unsigned -- allow unsigned uploads (true/false)
  • overwrite -- overwrite existing assets with same public_id (cannot be true when unsigned=true)
  • resource_type -- image, video, or raw (default image)
  • unique_filename -- append random suffix to avoid collisions (default true)
  • use_filename -- use original filename (default false)
  • moderation -- moderation type: manual, webpurify, aws_rek, etc.
  • auto_tagging -- confidence threshold 0.0-1.0 for AI auto-tagging
  • notification_url -- webhook URL for upload notifications

Read the full file on GitHub · 186 lines

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 · 186 lines · 29 tokens per session scan A 64c90fdcccc3

Subscribe to this mod's changes

Cloudinary Automation is a skill published in the GitHub repository openteams-lab/openteams (612 stars, last pushed 2d ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,987 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 2026-09-03.

Related

Other skills, from other repositories

feature-demo-recording

Record a demo video of a web feature from a real browser. Two modes -- a NARRATED film where measured voiceover drives the timeline (designed slides, subtitles, punch-in camera, rendered from an HTML timeline), and a SILENT evidence clip for a PR or a QA pass. Use when the user asks to record a video, demo, or screen…

kirodotdev/KiroCrew · 90 tokens

image-authoring

Author images and diagrams as code — SVG, Pillow, Excalidraw, mermaid. Load when asked to draw, illustrate, or make an image, icon, logo, poster, or diagram.

kirodotdev/KiroCrew · 45 tokens

pptx-maker

Generate or restyle a PowerPoint deck. Use when the user wants to create or edit a .pptx presentation, build slides from text or a URL, or design a reusable slide style.

kirodotdev/KiroCrew · 43 tokens

artifacts

Persist, version, and iterate on LLM-generated UI (widgets, HTML, markdown). Load when the user wants to save, find, update, or iterate on a previously-rendered widget — anything that should outlive the chat scrollback.

kirodotdev/KiroCrew · 52 tokens

gpt-image-skill

A skill for generating or editing images through OpenAI's GPT Image API, a programming interface for requesting image operations. It uses a bundled Python script and supports configuration such as model, size, quality, and output file.

feiskyer/claude-code-settings · 115 tokens

nanobanana-skill

An image-generation and image-editing skill that uses Google Gemini through the Nanobanana tool. It covers prompts, image sizes, output files, models, resolutions, and required setup.

feiskyer/claude-code-settings · 109 tokens