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Found 173 Skills
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,' 'AI agents for GTM,' or 'revenue automation.' This skill covers technical GTM infrastructure from workflow design through agent orchestration.
Use when launching OCI compute instances, troubleshooting out-of-capacity or boot failures, optimizing compute costs, or handling instance lifecycle. Covers shape selection, capacity planning, service limits, and production incident resolution.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP and private clouds (OpenStack/VMware). Masters hybrid connectivity, workload placement optimization, edge computing, and cross-cloud automation. Handles compliance, cost optimization, disaster recovery, and migration strategies. Use PROACTIVELY for hybrid architecture, multi-cloud strategy, or complex infrastructure integration.
Cloud & AI FinOps advisory skill. Structured cost optimization using the FinOps Foundation framework. Covers AWS, Azure, GCP, OCI, AI inference, and data platforms (Databricks, Snowflake). Use for: cloud costs, cost optimization, cloud spend, AI costs, cloud bill, FinOps assessment, GreenOps, right-sizing, commitment strategy, tagging governance.
Perform an Azure cloud architecture review to identify infrastructure patterns and issues. Use when reviewing cloud configurations.
Audit token waste across agent systems (Claude Code, OpenClaw, Hermes, OpenCode). Detect idle burns, model misrouting, and config bloat with dollar savings.
Analyze and optimize AWS costs with recommendations for Reserved Instances, right-sizing, and resource cleanup. Use when reducing AWS spending, analyzing costs, or optimizing cloud infrastructure expenses.
Optimize Kubernetes costs through resource right-sizing, unused resource detection, and cluster efficiency analysis. Use for cost optimization, resource analysis, and capacity planning.
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure. Designs scalable large language model applications with focus on performance, cost efficiency, and safety.
Builds AI-native products using Dan Shipper's 5-product playbook and Brandon Chu's AI product frameworks. Use when implementing prompt engineering, creating AI-native UX, scaling AI products, or optimizing costs. Focuses on 2025+ best practices.
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.