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Found 803 Skills
Develop Custom SCAPI endpoints for B2C Commerce. Use when creating REST APIs, defining api.json routes, writing schema.yaml (OAS 3.0), or building headless commerce integrations. Covers cartridge structure, endpoint implementation, and OAuth scope configuration.
Patch, extend, or explain DatoCMS front-end integration code inside an existing web project (Next.js App Router, Nuxt, SvelteKit, Astro, plus React/Vue/Svelte component usage). Use for targeted, per-concern work — adding a draft mode endpoint, wiring Preview Links / Visual Editing flows, fixing Content Link overlays, tuning real-time preview updates/subscriptions, setting up cache-tag invalidation/revalidation flows (Next.js revalidateTag or CDN purge by tags), adding robots/sitemap wiring, or hooking up crawler-safe search integration. Also the go-to skill for framework component/hook wiring with react-datocms, vue-datocms, @datocms/svelte, and @datocms/astro: Image/SRCImage/datocms-image, StructuredText, VideoPlayer (React/Vue/Svelte), SEO/meta helpers (renderMetaTags/toHead/Seo), QuerySubscription/QueryListener realtime patterns, ContentLink components, and Site Search (React/Vue). Prefer this skill whenever the user is modifying a live codebase one concern at a time, asking a framework-specific API question, or mixing several front-end concerns in the same patch.
Omi AI wearable platform help — open-source AI necklace for all-day conversation capture (in-person + online meetings), Developer API (`api.omi.me/v1/dev`, Bearer token, 100 req/min), app marketplace with webhook integrations, memories/conversations/action-items endpoints. Use when setting up an Omi wearable for meeting capture, building a custom Omi app or integration, troubleshooting Bluetooth disconnects or transcription accuracy, connecting Omi to Slack or CRM via webhooks, comparing Omi to Plaud or Limitless for in-person recording, or accessing Omi's API to export conversations and action items. Do NOT use for choosing between software-only note-takers without wearable needs (use /sales-note-taker).
[QianWen] Configure authentication (API keys, endpoints). TRIGGER when: setting up QIANWEN_API_KEY, troubleshooting 401/auth errors, when another skill reports missing credentials, or user explicitly invokes this skill by name (e.g. use qianwen-ops-auth). DO NOT TRIGGER when: non-auth Qwen tasks, general API usage questions.
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check deployment status, verify serving endpoints, or clean up resources by undeploying models and deleting endpoints. Use when asked to deploy models on Agent Platform, list available Model Garden models, check if a model is deployable, query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval` skill).
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.
Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
Expert FastAPI developer specializing in production-ready async REST APIs with Pydantic v2, SQLAlchemy 2.0, OAuth2/JWT authentication, and comprehensive security. Deep expertise in dependency injection, background tasks, async database operations, input validation, and OWASP security best practices. Use when building high-performance Python web APIs, implementing authentication systems, or securing API endpoints.
Migrates REST APIs to GraphQL incrementally with schema stitching, REST datasources, and gradual endpoint migration. Use when users request "migrate to GraphQL", "REST to GraphQL", "GraphQL wrapper", or "API modernization".
FastAPI framework mechanics and advanced patterns. Use when configuring middleware, creating dependency injection chains, implementing WebSocket endpoints, customizing OpenAPI documentation, setting up CORS, building authentication dependencies (JWT validation, role-based access), implementing background tasks, or managing application lifespan (startup/shutdown). Does NOT cover basic endpoint CRUD or repository/service patterns (use python-backend-expert) or testing (use pytest-patterns).