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Found 1,196 Skills
Provides project management, task tracking, team coordination, and project delivery capabilities. Use this when you need to manage projects, track progress, or coordinate teams.
Meta-skill for pplx-sdk development. Orchestrates code review, testing, scaffolding, SSE streaming, and Python best practices into a unified workflow. Use for any development task on this project.
Template-driven workflow coordinator with minimal state tracking. Executes command chains from workflow templates OR unified PromptTemplate workflows. Supports slash-command and DAG-based execution. Triggers on "flow-coordinator", "workflow template", "orchestrate".
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
[Implementation] ⚡⚡ Implement a feature automatically ("trust me bro")
Эксперт AutoML. Используй для automated machine learning, hyperparameter tuning и model selection.
Orchestrates translation of motion designer video specifications into working Remotion code by coordinating specialized agents. Acts as pipeline coordinator that delegates to remotion-scaffold, remotion-animation, remotion-composition, and remotion-component-gen. Use when you have a complete VIDEO_SPEC.md and need full Remotion implementation.
Routes tasks to skills in skill-db and skill-library using semantic discovery. Triggers on specialized skill requirements, domain-specific tasks, or explicit skill requests. Uses skill-discovery, mcp-skillset, and skill-rag-router for semantic matching.
Execute tasks from TODO file - Generic task runner [/todo-task-run xxx]
Full RPI lifecycle orchestrator. Research → Plan → Pre-mortem → Crank → Vibe → Post-mortem. One command, sequential skill invocations with human gates and hands-free validation. Triggers: "rpi", "full lifecycle", "end to end", "research to production".
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Execute complex tasks with intelligent workflow management and cross-session persistence. Use when managing large projects, tracking progress across sessions, or orchestrating multi-phase work.