AI Generation

Summarize conversations into handoff documents for seamless agent-to-agent transitions. Compacts current conversation context into a structured document saved to the OS temp directory, enabling fresh agents to resume work without re-reading full chat history Automatically suggests relevant skills the next agent should invoke based on conversation content Avoids duplication by referencing existing artifacts (PRDs, plans, ADRs, issues, commits, diffs) by path or URL rather than repeating their content Redacts sensitive data including API keys, passwords, and personally identifiable information before handoff Tailors handoff focus based on user-provided arguments describing the next session's goals

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Teach the user a new skill or concept, within this workspace.

Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse…

Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein…

End-to-end deployment, evaluation, and management of AI agents on Microsoft Foundry. Covers the complete agent lifecycle: creation from starter samples, containerization and ACR push, hosted or prompt agent deployment, invocation, batch evaluation, and prompt optimization Includes specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace analysis, troubleshooting, and dataset curation from production traces Supports project and resource provisioning, RBAC management, quota tracking, and model deployment with intelligent routing across regions and SKUs Requires .foundry/agent-metadata.yaml as the source of truth for environment-specific configuration, datasets, and evaluation test cases

Unified access to Azure AI services: Search, Speech, OpenAI, and Document Intelligence. AI Search supports full-text, vector, hybrid, and semantic search with AI enrichment capabilities like entity extraction and OCR Speech service enables speech-to-text transcription (real-time and batch), text-to-speech with neural voices, speaker diarization, and custom models MCP server integration provides direct tool access via azure__search and azure__speech commands; falls back to CLI and SDK when MCP is unavailable Includes OpenAI model access, DALL-E image generation, embeddings, and Document Intelligence for form extraction and OCR

Configure Azure API Management as an AI Gateway for models, MCP tools, and agents with built-in governance policies. Supports semantic caching (60-80% cost savings), token rate limiting, content safety filtering, and jailbreak detection across AI backends Add Azure OpenAI, AI Foundry models, or convert existing APIs to MCP tools as managed backends with load balancing Includes five core policy categories: authentication, semantic cache lookup, token limits, content safety, and token metrics for observability Requires Azure CLI for configuration and testing; integrates with managed identity for secure backend access

Ultra-compressed communication mode cutting token usage ~75% while preserving technical accuracy. Supports six intensity levels (lite, full, ultra, wenyan-lite, wenyan-full, wenyan-ultra) triggered by user keywords like "caveman mode," "less tokens," or /caveman command Drops articles, filler words, and hedging while keeping all technical substance and code blocks unchanged; fragments and short synonyms replace verbose phrasing Persists across all responses in a session until explicitly disabled with "stop caveman" or "normal mode" Auto-disables for security warnings, irreversible action confirmations, and multi-step sequences where compression risks ambiguity; resumes after clarity achieved

Quick reference for ADK Python patterns: agents, tools, callbacks, and state management. Covers agent creation with model and instruction configuration, basic tool definition via FunctionTool, and callback patterns for state initialization and lifecycle hooks Includes built-in tool imports, agent orchestration (SequentialAgent, ParallelAgent, LoopAgent), and state management through CallbackContext ADK 2.0 Workflow API available as opt-in experimental feature for graph-based pipelines with conditional routing and parallel processing; requires Python 3.11+ and explicit user consent before use Requires an existing scaffolded project; use agents-cli scaffold create or scaffold enhance before writing agent code

Complete workflow for scaffolding, building, evaluating, and deploying agents using Google's Agent Development Kit. Covers all seven phases: understanding requirements, studying reference samples, scaffolding projects, implementing agent logic, evaluation, deployment, and observability Includes mandatory Phase 0 clarification questions (problem statement, APIs, safety constraints, deployment preference) and a spec template to document decisions before coding Provides phase-specific CLI commands ( agents-cli scaffold create , agents-cli run , agents-cli eval run , agents-cli deploy ) and cross-references to related skills for deep dives into code patterns, evaluation methodology, and infrastructure Enforces code preservation (modify only targeted segments, never change models or config without explicit request) and systematic debugging (reproduce, localize, fix one thing, verify, guard)

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini Enterprise", "publish to Gemini…

Build and deploy GitHub Copilot SDK applications to Azure with flexible model configuration. Three scaffolding paths: create new greenfield projects, add SDK services to existing repos, or deploy existing SDK apps with Azure infrastructure Supports three model configurations: GitHub's default models, specific GitHub models via discovery, or bring-your-own-model (BYOM) on Azure with DefaultAzureCredential authentication Includes complete templates with Express/TypeScript API, React/Vite frontend, Bicep infrastructure, Docker support, and token management scripts Deploy workflow uses azure-prepare, azure-validate, and azure-deploy steps; requires Docker and respects existing AGENTS.md configuration in user repos

Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks…

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the…

Edit images on RunComfy — this skill is a smart router that matches the user's intent to the right edit model in the RunComfy catalog. Picks Nano Banana Edit…

Edit images with Google Nano Banana 2 (image-to-image edit endpoint) on RunComfy. Documents Nano Banana Edit's strengths (preserve subject identity, swap…

Edit images with Flux 1 Kontext Pro (Black Forest Labs' precise local image-edit model) on RunComfy — bundled with the model's documented prompting patterns so…