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Found 1,242 Skills
Describes how blockchain analytics platforms work in practice, typical use cases (markets, compliance, law enforcement, tax, market integrity), tool layers like visualizers and tracers, and limitations of heuristic attribution. Use when the user asks about blockchain analytics for AML, transaction monitoring, forensic tracing, institutional ops, or taint-style analysis at a high level.
Educational map of transaction-centric compliance screening—transfer as the atomic unit, deposit vs withdrawal direction, single and CSV import, transaction list and detail views, per-transfer screening, rescreen, and STR-style exports. Use when the user asks how monitoring UIs treat tx hashes, directions, or regulatory reporting hooks—not for legal filing advice or evading reporting.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
HODLMM Move-Liquidity & Auto-Rebalancer — withdraw from drifted bins, re-deposit around the current active bin. Includes autonomous monitoring loop.
Use when monitoring account analytics, tracking growth metrics, or measuring content performance
Enthu.AI platform help — contact center conversation intelligence with auto QA scorecards, agent coaching, compliance monitoring, and speech analytics. Use when setting up Enthu.AI QA scorecards for call center agents, calls not being scored or transcribed correctly, agents not seeing coaching insights from their calls, Enthu.AI integration with Aircall or RingCentral not syncing, comparing Enthu.AI vs Gong or CallMiner for contact center QA, or configuring sentiment analysis and keyword tracking. Do NOT use for building a general coaching program (use /sales-coaching) or reviewing a specific call transcript (use /sales-call-review).
Use this skill when the user asks to "set up monitoring", "configure observability", "onboard new service", "create saved view", "set up notifications", "configure webhook", "set up Slack integration", "outgoing webhook", "automation action", "webhook for alerts", "create view", "saved view", "view folder", "organize dashboards", "install integration", "configure extension", "contextual data", "connect external service", "create notification connector", "set up email alerts", "configure PagerDuty", "notification routing", "deploy extension", "test webhook", "notification preset", "test notification", "webhook actions", or wants to set up, configure, or manage the observability stack for a service or team.
Implement Syncfusion Angular Circular Gauge component for radial data visualization. Use this when building circular gauges for dashboards, monitoring displays, performance metrics, speedometers, or KPI indicators. Covers installation, axes configuration, pointer types (needle, range bar, marker), ranges, styling, animations, and user interactions.
Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy patterns.
Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
Use for Core Location implementation patterns - authorization strategy, monitoring strategy, accuracy selection, background location