MLOps

Ai Image Generation

omer-metin

3D Modeling

omer-metin

starwave:requirements

ArjenSchwarz

Requirement Gathering

deep-reading-analyst

ginobefun

"Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis."

model-hierarchy

zscole

Cost-optimize AI agent operations by routing tasks to appropriate models based on complexity. Use this skill when: (1) deciding which model to use for a task, (2) spawning sub-agents, (3) considering cost efficiency, (4) the current model feels like overkill for the task. Triggers: "model routing", "cost optimization", "which model", "too expensive", "spawn agent".

data-pipeline-engineering

lv416e

"Use when building or modifying any data pipeline, before writing transformation logic - idempotent-first approach covering schema design, quality checks, incremental loads, CDC, and observability that ensures every step is repeatable and verifiable データパイプラインの構築や変更時、変換ロジックを書く前に使用 - スキーマ設計、品質チェック、増分ロード、CDC、オブザーバビリティを網羅する冪等性ファースト手法により、全ステップの再実行可能性と検証可能性を保証"

ai-evaluation-evals

oldwinter

Create AI evaluation plans with benchmarks, rubrics, and error analysis workflows.

fundraising-strategy

oldwinter

Plan early-stage fundraising strategy, investor pipeline, and process execution.

3d-web-experience

ranbot-ai

Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...

fundraising

oldwinter

"Plan and run an early-stage fundraising process and produce a Fundraising Pack (raise decision memo, round design brief, pitch narrative + deck outline, investor pipeline + tracker, outreach/follow-up scripts, diligence checklist). Use for fundraising, raising capital, venture capital, pitch deck, investor outreach, pre-seed, seed. Category: Career."

objection-destroyer

majesticlabs-dev

Create powerful pitch closings with market insights, proprietary advantages, founder mission, and subtle FOMO triggers in under 45 seconds.

audit-context-building

lv416e

Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

fundraising

oldwinter

"Plan and run an early-stage fundraising process and produce a Fundraising Pack (raise decision memo, round design brief, pitch narrative + deck outline, investor pipeline + tracker, outreach/follow-up scripts, diligence checklist). Use for fundraising, raising capital, venture capital, pitch deck, investor outreach, pre-seed, seed. Category: Career."

ai-evaluation-evals

oldwinter

Create AI evaluation plans with benchmarks, rubrics, and error analysis workflows.

dbt-coder

majesticlabs-dev

dbt (data build tool) patterns for model organization, incremental strategies, and testing.

testing-patterns

majesticlabs-dev

Pytest templates and patterns for ETL pipeline testing - unit, integration, data quality.

signum

heurema

Use when the user wants contract-first development — define correctness before coding, implement against a contract, audit with multiple models, and package proof artifacts.

unifuncs-deep-search

UniFuncs

使用 UniFuncs API 进行深度搜索，高速全面地搜索信息。当用户需要深度搜索、深搜、全面信息收集时使用。

reinforcement-learning

Aznatkoiny

Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: - Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) - Creating custom Gymnasium environments - Training, debugging, or evaluating RL agents - Setting up hyperparameter tuning for RL - Deploying RL models to production

deep-learning

Aznatkoiny

"Comprehensive guide for Deep Learning with Keras 3 (Multi-Backend: JAX, TensorFlow, PyTorch). Use when building neural networks, CNNs for computer vision, RNNs/Transformers for NLP, time series forecasting, or generative models (VAEs, GANs). Covers model building (Sequential/Functional/Subclassing APIs), custom training loops, data augmentation, transfer learning, and production best practices."

consulting-frameworks

Aznatkoiny

Core consulting thinking frameworks and methodologies for structuring business problems, communicating findings, analyzing strategy, building financial models, and designing operations. Use when any agent or command needs to apply MECE decomposition, pyramid principle, hypothesis-driven analysis, issue trees, SCR communication, Porter's Five Forces, TAM/SAM/SOM market sizing, value chain analysis, NPV/IRR decision criteria, build/buy/partner evaluation, RACI matrices, or any standard consulting framework. This skill provides procedural guidance — not just framework names, but how to apply them correctly.

smolagents

svngoku

Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

openclaw-setup

Aznatkoiny

Set up, install, configure, and deploy OpenClaw (formerly ClawdBot/MoltBot) — a personal AI assistant that runs on your own devices and connects to messaging channels. Use when users ask to "set up OpenClaw," "install ClawdBot," "install MoltBot," "deploy a personal AI assistant," "configure OpenClaw on Mac," "deploy OpenClaw to VPS," "set up OpenClaw on Hostinger," "connect OpenClaw to Telegram," "configure iMessage with OpenClaw," or any variation involving OpenClaw installation, gateway configuration, channel setup, Anthropic auth, or security hardening. Also triggers on "openclaw onboard," "openclaw doctor," "openclaw security audit," troubleshooting OpenClaw deployments, OpenClaw security, OpenClaw cost control, or ClawHub skills safety.

cpp-reinforcement-learning

Aznatkoiny

C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime