outlook-automation
Automate Outlook tasks via Rube MCP (Composio): emails, calendar, contacts, folders, attachments. Always search tools first for current schemas.
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Automate Outlook tasks via Rube MCP (Composio): emails, calendar, contacts, folders, attachments. Always search tools first for current schemas.
Senior PM agent with 6 knowledge domains, 30+ frameworks, 12 templates, and 32 SaaS metrics with formulas. Pure Markdown, zero scripts.
To create new CLI skills following Anthropic's official best practices with zero manual configuration. This skill automates brainstorming, template application, validation, and installation processes while maintaining progressive disclosure patterns and writing style standards.
-Automatically convert documentation websites, GitHub repositories, and PDFs into Claude AI skills in minutes.
Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.
Orquestrador unificado de canais sociais — coordena Instagram, Telegram e WhatsApp em um unico fluxo de trabalho. Publicacao cross-channel, metricas unificadas, reutilizacao de conteudo por formato, agendamento sincronizado e gestao centralizada de campanhas em todos os canais simultaneamente.
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
Automate Cal.com tasks via Rube MCP (Composio): manage bookings, check availability, configure webhooks, and handle teams. Always search tools first for current schemas.
Automate Calendly scheduling, event management, invitee tracking, availability checks, and organization administration via Rube MCP (Composio). Always search tools first for current schemas.
Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.
Linux system troubleshooting workflow for diagnosing and resolving system issues, performance problems, and service failures.
Use when downloading test logs, artifacts, or outputs.zip from EngFlow build invocations. Use when investigating CockroachDB CI test failures hosted on mesolite.cluster.engflow.com.
Analyze DRT cluster health for a given time range. Reconstructs the operations timeline, checks CockroachDB metrics (availability, latency, storage, changefeeds, jobs, goroutines, admission control, LSM, KV prober) and logs for anomalies, correlates findings with disruptive operations to distinguish expected side-effects from real bugs. Use when asked to "analyze DRT", "check cluster health", "what happened on the DRT cluster", "DRT health report", investigate DRT issues, or review DRT operations. Also use when the user mentions a DRT cluster name (drt-scale, drt-chaos, drt-large, etc.) in the context of health or operations.
Reduce an unoptimized-query-oracle test failure log to the simplest possible reproduction case. Use when you have unoptimized-query-oracle*.log files from a failed roachtest and need to find the minimal SQL to reproduce the bug.
Orchestrate a multi-phase implementation workflow for this repository with artifact files under .ai/<project-name>/<letter>/ using Codex subagents instead of shell-spawned child processes. Use when the user wants one prompt to drive context gathering, planning, plan assessment, implementation, build verification, and review with persistent artifacts, clear phase handoffs, and a thin parent thread. Prefer spawn_agent/send_input/wait_agent, keep heavy pre-build work delegated when possible, and avoid pulling timed-out phases back into the main session.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows