Explorer les skills

Free LLM API Setup

mnfst

Find free LLM API options and follow provider-specific key and endpoint setup instructions.

• llm
• api-configuration

Language Learning

SkillMedev

ai-agents skill for Claude AI. Grade A. Coaches a language learner with a CEFR-targeted plan built on comprehensible input, spaced repetition, and early speaking practice, including daily minimums, a session template, and a weekly schedule. Use when someone says "help me learn Spanish", "I keep forgetting vocabulary", "how do I get from A2 to B1", or "design my daily language routine". Do NOT use for general exam or academic study techniques - use study-system instead.

• ai-agents
• rust
• go

Using Blender

ellmos-ai

ai-agents skill for Claude AI. Grade A. General Blender workflow skill for AI agents working with .blend, .fbx, .obj, .glb, glTF, materials, scene inspection, bpy automation, headless Blender batch runs, export/reimport validation, previews, and optional Blender MCP control. Use when a task asks to open, inspect, create, automate, convert, optimize, render, or verify Blender or 3D asset files in a user-agnostic way.

• ai-agents
• python
• go

Caveman Workflow Discovery

Julius Brussee

Identify and label application LLM workflows for Caveman Cloud cost attribution.

• observability

Caveman Gateway Setup

Julius Brussee

Connect an application’s LLM calls to the Caveman Cloud gateway for measurement.

• observability

AI Evals Skills

AI Evals Course

Practical workflows for assessing and improving product-specific LLM evaluations.

• ai evaluation

Eval Audit

AI Evals Course

Audit an LLM evaluation pipeline for missing error analysis, unvalidated judges, data leakage and stale checks.

• llm evaluation
• ai evaluation

Managed Pentesting With Strix

usestrix

Run a managed pentest of a web app, API, repository, or local workspace on the app.strix.ai platform with the `strix cloud` CLI or REST API — no local Docker or LLM key needed.

• security
• testing

Ml Engineering

stas00

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference…

• machine-learning
• development