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Found 841 Skills
Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Build custom AI search monitoring tools for competitive AEO analysis. Covers API access, scraping architecture, legal compliance, and cost estimation.
Build a Solana wallet monitoring bot (inflows/outflows, threshold alerts) with safe rate limits and privacy guardrails. Use for treasury monitoring, whale tracking, or security alerts.
Complete development skill set for the ABE Framework, providing a full-stack solution for modern Go HTTP RESTful API application development. Core features include: modular engine architecture, standardized controller route registration, global and route-level middleware system, dependency injection container (supporting global and request-level scopes), multi-language internationalization (i18n) support, access control system based on JWT and Casbin, asynchronous event bus mechanism, high-performance goroutine pool management, extensible plugin mechanism, configuration management system (supporting multi-layer configuration priority), GORM database integration, structured logging system, form validation framework, scheduled task scheduling (Cron), CORS cross-domain support, etc. Suitable for scenarios such as building enterprise-level web services, microservice architecture applications, API gateways, and backend management systems. The framework adopts a loose-coupling design, supports the UseCase business logic pattern, provides a complete error handling mechanism and performance monitoring capabilities, helping enterprises quickly build stable and maintainable distributed application systems.
Find and read academic papers: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), and read with structured analysis (3-level strategy). Use when: finding papers, reading a paper, related work, citation analysis, research trends, SOTA results, datasets. Do NOT use for generating literature survey reports (use research-survey), generating research ideas (use research-ideation), writing a paper's Related Work section (use paper-writing), comparing/ranking research ideas (use research-ideation), or planning paper structure (use paper-planning).
Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.
Grafana Cloud cost management — usage monitoring, cost attribution by label, usage alerts, invoice management, and optimization strategies. Covers Adaptive Metrics (cardinality reduction), Adaptive Logs (log filtering), cost attribution labels, and the FOCUS-compliant billing application. Use when analyzing Grafana Cloud spending, setting up cost alerts, attributing costs to teams, reducing metric/log cardinality, or forecasting observability budgets.
Enables a multi-region AWS CloudTrail trail with S3 log storage, CloudWatch Logs integration, and CloudWatch Logs Insights queries for security monitoring and compliance auditing. Use when setting up centralized API activity logging across all AWS regions.
Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.