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Found 89 Skills
Build with Aurora DSQL - manage schemas, execute queries, and handle migrations with DSQL-specific requirements. Use when developing a scalable or distributed database/application or user requests DSQL.
Design UI pages in Subframe. Use when building new UI, iterating on existing UI, exploring design options, or to get a visual starting point to refine in the Subframe design tool. Don't write UI code directly - design first, then implement with /subframe:develop.
Research agent for external documentation, best practices, and library APIs via MCP tools
Manage MCP servers in Nuxt - setup, create, customize with middleware, review, and troubleshoot
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
Checks if a specific service is available at a given address.
Use this skill when querying Tarkov game data via MCP tools. Provides optimal query patterns, data relationships, and best practices for the tarkov-dev and eft-wiki MCP servers.
Agent skill for authentication - invoke with $agent-authentication
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
Agent Mail inbox monitoring. Check pending messages, HELP_REQUESTs, and recent completions. Triggers: "inbox", "check mail", "any messages", "show inbox", "pending messages", "who needs help".
Multi-agent orchestration workflow for deep research: Split a research objective into parallel sub-objectives, run sub-processes using Claude Code non-interactive mode (`claude -p`); prioritize installed skills for network access and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + summary of key conclusions/recommendations". Applicable scenarios: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-agent parallel research/multi-process research".
Use the Docyrus Architect MCP tools to manage data sources, fields, enums, apps, and query data in the Docyrus platform. Use when the user asks to create, update, delete, or query data sources, fields, enum options, or apps via the docyrus-architect MCP server. Also use when building reports, dashboards, or performing data analysis that requires querying Docyrus data sources with filters, aggregations, formulas, pivots, or child queries.