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Found 2,174 Skills
Guides agents to discover requirements and design a governed, secure borderless open data lakehouse with agentic AI integration. Use when designing a multi-product architecture that connects data silos to AI agents, joining data across clouds, or running federated queries across Google Cloud and external data sources, including on-premises or other cloud providers. Don't use for simple single-cloud data warehouses or non-AI workloads.
Guides agents through a structured 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to opinionated best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage buckets, Compute Engine MIGs, GKE, Cloud Run) to use as load balancer backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancer configurations. - Actuating deployments via Infrastructure Manager or bash scripts, including performing IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).
Design ObjectStack AI skills, tools, knowledge sources, conversations, model registry entries, and MCP integrations. Use when the user is adding `*.skill.ts` / `*.tool.ts`, configuring an LLM provider, wiring agent tools, or indexing ObjectStack data as a knowledge source for RAG. Agents themselves are platform-internal (`ask` / `build`) — third parties extend them via skills and tools, not by authoring `*.agent.ts`. Do not use for general LLM prompting questions unrelated to ObjectStack metadata.
Official LottieFiles motion design principles — timing curves, easing, choreography, Disney's 12 principles adapted for UI. Implementation-agnostic: works with CSS, GSAP, Framer Motion, Lottie. Supports 40+ agents.
(NS) Write and edit human-facing project documentation — README, docs/ guides, runbooks, and contributor-facing markdown. Use whenever the user asks for documentation, README updates, how-to guides, or docs/ content — even if they say "explain how to use" instead of "write docs". Stack-agnostic; follow project layout from AGENTS.md. Do NOT use for code comments, API docblocks in source, requirements.md, or SDD version artifacts (use PM skills).
Coordinate multi-agent swarms for parallel and pipeline workflows. Use when coordinating multiple agents, running parallel reviews, building pipeline workflows, or implementing divide-and-conquer patterns with subagents.
Orchestrate and coordinate coding workers in Herdr by spawning parallel Codex workers in git worktree tabs, tracking status and reports, and routing owned task, review, and pull-request lifecycles. Use only when the request explicitly mentions Herdr and asks to orchestrate, coordinate, fan out, parallelize, delegate, or route work among coding workers — or a prompt tells you that you are a Herdr worker reporting to an orchestrator. Never use it merely because a task involves parallel work, multiple agents, or git worktrees without Herdr being named; also not for single-agent Herdr control or general Herdr CLI questions, which the herdr skill covers.
Give first-time users a short, value-first Kitaru tour with the public returns-agent template. Use when someone has no agent or traces of their own, arrives from Kitaru onboarding, asks for a demo, tutorial, quickstart, or guided example, needs the public template cloned or prepared, wants the coding agent to prepare trace annotations before they judge sessions, or wants to experience Kitaru's value before learning the full investigation method. Prepare a three-session frontend review, let the human provide verdicts, turn one accepted finding into a deterministic evaluator, and finish with one approved bounded replay experiment. Route real agents, open-ended discovery, and production evidence to kitaru-investigation instead.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.
Microsoft Teams bots and AI agents - Claude/OpenAI, Adaptive Cards, Graph API
Give AI agents their own email inboxes using the AgentMail API. Use when building email agents, sending/receiving emails programmatically, managing inboxes, handling attachments, organizing with labels, creating drafts for human approval, or setting up real-time notifications via webhooks/websockets. Supports multi-tenant isolation with pods.