Loading...
Loading...
Found 3,858 Skills
Sets up a 3D CAD model viewer in a Dune app using Cognite Reveal via @cognite/dune-industrial-components/reveal. Use this skill whenever the user mentions 3D viewer, 3D visualization, reveal, CAD model, RevealProvider, RevealCanvas, Reveal3DResources, FDM 3D mapping, asset 3D model, loading a 3D model, or wants to display any Cognite 3D content in a Dune application — even if they don't explicitly say 'Reveal' or '3D viewer'. Do NOT manually wire up RevealProvider, RevealCanvas, or model-loading hooks without consulting this skill first.
Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
Monetize a DFlow integration by collecting a builder-defined fee on trades your app routes through the Trade API — either a fixed percentage (spot + PM) via `platformFeeBps`, or a probability-weighted dynamic fee (PM outcome tokens only) via `platformFeeScale`. Use when the user asks "how do I take a cut of trades?", "add a builder fee", "monetize my swap UI", "charge a platform fee", "how does platformFeeBps / platformFeeScale work?", or "where do my fees get paid?". Do NOT use to run a trade itself (use `dflow-spot-trading` or `dflow-kalshi-trading` — both also cover priority fees and sponsored / gasless flows).
Read market data for a known Kalshi prediction market on DFlow — orderbook, trades, top-of-book prices, candlesticks, forecast-percentile history, and Kalshi in-game live data — via one-shot REST snapshots, historical ranges, or live WebSocket streams. Use when the user asks "show me the orderbook for X", "get last hour of trades", "build a live price ticker", "stream orderbook depth", "pull 1-minute candles for the last day", "watch in-game scores for this sports market", or "alert me when the orderbook moves". Do NOT use to discover markets matching a criterion (use `dflow-kalshi-market-scanner`), to place orders (use `dflow-kalshi-trading`), or to read a user's own positions/P&L (use `dflow-kalshi-portfolio`).
Configure human-in-the-loop gating for AI agent review actions in Claude Code. Use when setting up a project where an agent may post PR reviews, comments, merges, or edit CI configuration, and you want a cryptographically auditable approval trail with Cedar-enforced gates.
Run cross-framework agent comparisons using evaluatorq from orqkit — compares any combination of agents (orq.ai, LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK) head-to-head on the same dataset with LLM-as-a-judge scoring. Use when comparing agents, benchmarking, or wanting side-by-side evaluation. Do NOT use when comparing only orq.ai configurations with no external agents (use run-experiment instead).
Nest.js framework expert specializing in module architecture, dependency injection, middleware, guards, interceptors, testing with Jest/Supertest, TypeORM/Mongoose integration, and Passport.js authentication. Use PROACTIVELY for any Nest.js application issues including architecture decisions, testing strategies, performance optimization, or debugging complex dependency injection problems. If a specialized expert is a better fit, I will recommend switching and stop.
Comprehensive NestJS framework guide with Drizzle ORM integration. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM. Covers controllers, providers, modules, middleware, guards, interceptors, testing, microservices, GraphQL, and database patterns.
Build MCP servers in Python with FastMCP to expose tools, resources, and prompts to LLMs. Supports storage backends, middleware, OAuth Proxy, OpenAPI integration, and FastMCP Cloud deployment. Prevents 30+ errors. Use when: creating MCP servers, or troubleshooting module-level server, storage, lifespan, middleware, OAuth, background tasks, or FastAPI mount errors.
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
Help users prioritize product roadmaps and backlogs. Use when someone is deciding what to build next, sequencing features, allocating resources across projects, handling stakeholder requests, or struggling with too many competing priorities.
React Three Fiber physics with Rapier - RigidBody, colliders, forces, joints, sensors. Use when adding physics simulation, collision detection, character controllers, or creating interactive physics-based experiences.