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Found 910 Skills
TRON (java-tron) - account model, DPoS, resources, system contracts, TVM, TRC-10/TRC-20, DEX, APIs, events, TronGrid.
Analyze and optimize Xano workspace performance. Use when the user wants to find slow endpoints, trace execution bottlenecks, deep-dive request stacks, or understand why their Xano API is slow. Also use when the user mentions "performance," "slow endpoint," "bottleneck," "stack trace," or "optimization."
Security detection use cases for identifying threats across network, endpoint, identity, cloud, application, and email vectors. Use for building detection rules, analyzing security events, and threat hunting operations.
Эксперт по API reference документации. Используй для создания справочников по API, описания endpoints и примеров запросов.
Use when a user needs to actually use or build on Zeko: bridge with Bridge CLI or Bridge SDK, get testnet funds, find the right Zeko and Mina endpoints, run GraphQL or curl queries, understand sequencer and archive-node roles, or build zkApps on Zeko with o1js or OCaml. This skill is for public user and builder workflows, especially terminal-driven and non-browser automation flows.
Build code-first notification workflows with @novu/framework. Use when defining workflows in TypeScript (Zod / JSON Schema / Class Validator), composing channel steps (email, SMS, push, chat, in-app) with action steps (delay, digest, custom), exposing Step Controls for non-technical teammates, rendering React/Vue/Svelte Email templates, hosting the Bridge Endpoint inside Next.js, Express, NestJS, Remix, Nuxt, SvelteKit, H3, or AWS Lambda, syncing to Novu Cloud via CLI / GitHub Actions, securing production with HMAC, or implementing translations, hydration, multi-channel orchestration, and LLM-powered notification logic in code.
Transcribe audio with StepFun's stepaudio-2.5-asr — an SSE endpoint (NOT /v1/audio/transcriptions) with 32K context, ~85-101x RTF on long audio, and a single-call ceiling around 30 minutes (no client-side chunking). Use when transcribing Chinese / English audio with StepFun, when long-form recordings (5-30 min) need to land in one request, when migrating from step-asr / step-asr-1.1, or when hitting the misleading `model stepaudio-2.5-asr not supported` error (which actually means wrong endpoint). Triggers on 阶跃 ASR, StepFun ASR, stepaudio-2.5-asr, 转录, 语音识别, 长音频转写, 语音转文字. For TTS with the sibling stepaudio-2.5-tts model, use the stepfun-tts skill instead.
Runpod CLI for managing GPU/CPU workloads from the terminal — pods, serverless endpoints, templates, network volumes, Hub deploys, models, SSH, and file transfer (send/receive). Use for terminal/CI/scripting, Hub browse/deploy, SSH setup, `doctor`, or when the Runpod MCP tools are not connected. For structured tool calls in an MCP-enabled session, prefer runpod-mcp.
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, selecting base models from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3 usage. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pg_duckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.
Curated research collection on adaptation strategies for agentic AI systems, covering agent and tool adaptation methods with RL, SFT, and DPO approaches
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.