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Found 172 Skills
Orchestrate multi-phase development workflows with strict role separation between implementers and validators. Automatically executes plans using separate subagents for implementation, validation, and fixing with auto-retry loops. Use when building complex systems requiring (1) Multi-step sequential or parallel development phases, (2) Automated validation with typecheck/build/tests after each phase, (3) Auto-retry fix loops until validation passes, (4) Complete execution after single user approval. Triggers include "implement this multi-phase plan", "build a system with phases", "create [complex system] following this architecture", "automate development workflow with validation", or any request for orchestrated development with multiple phases and quality checks. NOT for simple single-file tasks or exploratory coding.
Catlass Operator End-to-End Development Orchestrator. Based on ascend-kernel (csrc/ops), it connects catlass design, catlass-operator-code-gen and ascendc sub-skills to complete the closed loop from project initialization to documentation, precision, and performance. Keywords: Catlass, end-to-end, ascend-kernel, operator development, workflow orchestration.
Ask which skill or process is suitable for the current scenario; it is the router for all skills in this repository.
Draft and update feature issues with clear problem framing, scoped requirements, repository-valid labels, and explicit confirmation before publishing.
Identifies and manages execution dependencies between agent skills by analyzing their inputs and outputs. Use when building multi-step agent workflows to ensure skills are executed in the correct order and that all required data is available.
Agent skill for github-modes - invoke with $agent-github-modes
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.
Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.
Creates Cursor-specific AI subagents with isolated context for complex multi-step workflows. Use when creating subagents for Cursor editor specifically, following Cursor's patterns and directories (.cursor/agents/). Triggers on "cursor subagent", "cursor agent".
Distributed task queue system for Python enabling asynchronous execution of background jobs, scheduled tasks, and workflows across multiple workers with Django, Flask, and FastAPI integration.
Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Gemini CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".