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Found 8,058 Skills
Audit product logic and feature flows for impact, ROI, and efficiency. Use when user asks to "review product logic", "audit feature flow", "evaluate ROI", or needs assessment of user value vs implementation cost.
Comprehensive infrastructure engineering covering DevOps, cloud platforms, FinOps, and DevSecOps. Platforms: AWS (EC2, Lambda, S3, ECS, EKS, RDS, CloudFormation), Azure basics, Cloudflare (Workers, R2, D1, Pages), GCP (GKE, Cloud Run, Cloud Storage), Docker, Kubernetes. Capabilities: CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins), GitOps, infrastructure as code (Terraform, CloudFormation), container orchestration, cost optimization, security scanning, vulnerability management, secrets management, compliance (SOC2, HIPAA). Actions: deploy, configure, manage, scale, monitor, secure, optimize cloud infrastructure. Keywords: AWS, EC2, Lambda, S3, ECS, EKS, RDS, CloudFormation, Azure, Kubernetes, k8s, Docker, Terraform, CI/CD, GitHub Actions, GitLab CI, Jenkins, ArgoCD, Flux, cost optimization, FinOps, reserved instances, spot instances, security scanning, SAST, DAST, vulnerability management, secrets management, Vault, compliance, monitoring, observability. Use when: deploying to AWS/Azure/GCP/Cloudflare, setting up CI/CD pipelines, implementing GitOps workflows, managing Kubernetes clusters, optimizing cloud costs, implementing security best practices, managing infrastructure as code, container orchestration, compliance requirements, cost analysis and optimization.
LangGraph parallel execution patterns. Use when implementing fan-out/fan-in workflows, map-reduce over tasks, or running independent agents concurrently.
Implementation workflows and decision trees for Frappe Whitelisted Methods (REST APIs). Use when determining HOW to implement API endpoints: public vs authenticated, permission patterns, error handling, response formats, client integration. Triggers: how do I create API, build REST endpoint, frappe.call pattern, API permission check, guest API, secure endpoint.
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
Expert in automating Excel workflows using Node.js (ExcelJS, SheetJS) and Python (pandas, openpyxl).
Create standalone debugging interfaces that reveal the internal workings of complex systems through interactive visualization. Use when the user wants to understand how something works, debug internal state, visualize data flow, see what happens when they interact with the system, or build a debug panel for any complex mechanism. Triggers on requests like "I don't understand how this works", "show me what's happening", "visualize the state machine", "build a debug view for this", "help me see the data flow", "make this transparent", or any request to understand, debug, or visualize internal system behavior. Applies to state machines, rendering systems, event flows, algorithms, animations, data pipelines, CSS calculations, database queries, or any system with non-obvious internal workings.
Deployment procedures and CI/CD pipeline configuration for Python/React projects. Use when deploying to staging or production, creating CI/CD pipelines with GitHub Actions, troubleshooting deployment failures, or planning rollbacks. Covers pipeline stages (build/test/staging/production), environment promotion, pre-deployment validation, health checks, canary deployment, rollback procedures, and GitHub Actions workflows. Does NOT cover Docker image building (use docker-best-practices) or incident response (use incident-response).
Core LifeOS skill for research, synthesis, and Notion storage workflows
Guides users through setting up Tauri GitHub Actions CI/CD pipelines and workflows for automated building, testing, and releasing cross-platform desktop applications.
General strategies for using ToolUniverse effectively with 10000+ scientific tools. Covers tool discovery, multi-hop queries, comprehensive research workflows, disambiguation, evidence grading, and report generation. Use when users need to research any scientific topic, find biological data, explore drug/target/disease relationships, or need guidance on how to use ToolUniverse tools wisely.
Auto-generates code flow diagrams from Python module analysis. Detects when architecture diagrams become stale (code changed, diagram didn't). Use when: creating new modules, reviewing PRs for architecture impact, or checking diagram freshness. Generates mermaid diagrams showing imports, dependencies, and module relationships.