fal-platform
Provides access to platform-specific APIs for model management and usage tracking on FAL.
Install
mkdir -p .claude/skills/fal-platform-netbarros && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18697" && unzip -o skill.zip -d .claude/skills/fal-platform-netbarros && rm skill.zipInstalls to .claude/skills/fal-platform-netbarros
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Platform APIs for model management, pricing, and usage trackingKey capabilities
- →Manage models
- →Track pricing
- →Monitor usage
- →Interact with platform APIs
- →Access model management features
- →Analyze usage costs
How it works
This skill provides guidance and patterns for interacting with FAL platform APIs. It helps users understand how to work with model management, pricing, and usage tracking functionalities.
Inputs & outputs
When to use fal-platform
- →Managing FAL models
- →Tracking model usage costs
- →Monitoring platform API metrics
About this skill
Fal Platform
Overview
Platform APIs for model management, pricing, and usage tracking
When to Use This Skill
Use this skill when you need to work with platform apis for model management, pricing, and usage tracking.
Instructions
This skill provides guidance and patterns for platform apis for model management, pricing, and usage tracking.
For more information, see the source repository.
When not to use it
- →When not working with FAL platform APIs
- →When model management, pricing, or usage tracking is not needed
- →When interacting with other platforms
Limitations
- →Limited to FAL platform APIs
- →Does not cover other platform functionalities
How it compares
This skill centralizes the knowledge and patterns for FAL platform API interactions, offering specific guidance for model management, pricing, and usage tracking, unlike general API documentation that might lack specific use-case patterns.
Compared to similar skills
fal-platform side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| fal-platform (this skill) | 0 | 4mo | No flags | Beginner |
| llava | 7 | 8mo | Review | Advanced |
| cocoindex | 6 | 9mo | Review | Intermediate |
| ai-multimodal | 9 | 6mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by netbarros
View all by netbarros →You might also like
llava
zechenzhangAGI
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
cocoindex
cocoindex-io
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
ai-multimodal
mrgoonie
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
rag-implementation
wshobson
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
rdkit
K-Dense-AI
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
pyhealth
davila7
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).