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Found 6,718 Skills
Use when seeking analogous solutions from other domains, when stuck on a problem and need fresh perspectives, or when evaluating whether approaches from field X might apply to field Y. Requires structured problem statement.
Image generation and editing using Google Gemini's Nano Banana Pro (gemini-3-pro-image-preview) model. Use when user requests: "Generate an image", "Create an image", "Make me a picture", "Draw", "Edit that image", "Change the color", "Remove background", "Add transparency", "Modify this image", "Make it transparent", "Change the style", "Add text to image", or any image creation/manipulation task. Supports text-to-image generation, image editing, multi-turn conversations, and transparency extraction via difference matting technique.
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
Identify, categorize, and prioritize technical debt. Trigger with "tech debt", "technical debt audit", "what should we refactor", "code health", or when the user asks about code quality, refactoring priorities, or maintenance backlog.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTTP, Tornado, and more.
Checks a GitHub pull request for unresolved review comments, failing status checks, and incomplete PR descriptions. Waits for pending checks to complete, categorizes issues as actionable or informational, and optionally fixes and resolves them. Use when the user wants to check a PR, address review feedback, or prepare a PR for merge.
Create custom tools for Vapi voice assistants including function tools, API request tools, transfer call tools, end call tools, and integrations with Google Calendar, Sheets, Slack, and more. Use when adding capabilities to voice agents, building tool servers, or integrating external APIs.
Mise development environment manager (asdf + direnv + make replacement). Capabilities: tool version management (node, python, go, ruby, rust), environment variables, task runners, project-local configs. Actions: install, manage, configure, run tools/tasks with mise. Keywords: mise, mise.toml, tool version, runtime version, node, python, go, ruby, rust, asdf, direnv, task runner, environment variables, version manager, .tool-versions, mise install, mise use, mise run, mise tasks, project config, global config. Use when: installing runtime versions, managing tool versions, setting up dev environments, creating task runners, replacing asdf/direnv/make, configuring project-local tools.
Self-Evolving Agent: Given a goal, it autonomously learns and iteratively improves until completion. Integrates superpowers workflow discipline.
Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
Use this skill BEFORE implementing any new feature. This is NON-NEGOTIABLE for scope control. Use when evaluating features during brainstorming, planning new functionality, branches approach size limits (1000/1500/2000 lines, 15/25/30 commits). Do not use when feature is already approved and in progress. DO NOT use when: simple bug fixes with clear scope.