coding-agents-skills

Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 SecNumCloud). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

Build GenAI and agentic applications with the genai-tk toolkit (https://github.com/tclatos/genai-tk) — a YAML-driven wrapper over LangChain, LangGraph, and 100+ LLM providers. Use this skill whenever the user mentions genai-tk, genai_tk, the GenAI Toolkit, `cli init`, `LangchainAgent`, `get_llm`/`get_embeddings`, `RetrieverFactory`/`ManagedRetriever`, the four bundled agent frameworks (ReAct, Deep, Deer-flow, SmolAgents), the OpenSandbox Docker integration, the `model_id@provider` identifier format, the `global_config()`/`OmegaConfig` system with `app_conf.yaml` and `:merge`, BAML structured extraction, SkillsMiddleware, writing or editing the toolkit's YAML profiles (langchain.yaml, deerflow.yaml, llm.yaml, retrievers.yaml), composing retrievers (vector/bm25/ensemble/reranked/pg_hybrid/zero_entropy), or extending the CLI with `CliTopCommand`. Trigger even when the user only says "the toolkit" in context.

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.

Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.

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).

Build AI agents with LangChain framework. Use when building agents, tools, memory, MCP integrations, RAG pipelines, multi-agent systems, or any LLM-powered applications using LangChain or LangGraph in Python or TypeScript.