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Found 1,568 Skills
Review local AI-agent transcripts into a personal Org casebook.
Frame a messy supply-chain pain point as a specific, owned decision; determine whether AI is appropriate; and select a bounded delegation model and use case. Use when leaders, planners, consultants, or transformation teams need to scope an AI-agent opportunity, compare candidate use cases, define human approval boundaries, or decide that process, data, or governance must be fixed before AI.
Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.
DeepWorkPlan — turn any repo AI-first and run Deep Work Plans. Routes to create, execute, refine, resume, status, verify, and repo-onboarding sub-skills based on intent. Use when the developer wants to plan, execute, manage, or verify structured multi-task work, or make a repository AI-agent-ready.
Analyze and instrument repositories for Traceway observability. Use when the user wants to plan, add, migrate, or verify Traceway or OpenTelemetry monitoring for backend, browser, full-stack, mobile or iOS, or AI-agent software, including project topology and user-approved setup-plan creation. Backends use OTLP/HTTP; browser frontends and independently released mobile clients use separate Traceway SDK projects. Accept either an existing project token and instance URL or an organization setup token (`tws_...`) and URL; with no token, analyze first and guide setup-token acquisition. Treat any `tws_` token as a setup token.
UNNC AIM team's repository and code standards. Covers repository naming (competition / aim- academic-year / reusable), branch and Conventional Commits rules, AI-agent git behavior (never commit / push unless the user explicitly asks in the current task; never call the gh CLI without explicit permission; never add an AI Co-Authored-By trailer; suggest a commit message at the end of a code-changing task), and formatting standards for Python (autopep8 / PEP 8), C++ (clang-format / clang-tidy), TypeScript (Prettier + ESLint, pnpm), Go (gofmt), CMake, and Markdown / YAML / JSON, with ready-to-copy canonical rule files. Use when creating or naming a repository, validating a ROS2 package.xml name, creating a branch, writing a git commit message, on any task that could involve committing, pushing, or calling gh, composing a competition workspace (git submodules under src/), or setting up Python, C++ (.clang-format / setup.cfg / .clang-tidy), TypeScript (Prettier / ESLint / pnpm) or Go (gofmt) tooling, styling a CMakeLists.txt or Markdown / YAML / JSON files, or applying the team's recommended git configuration.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Compact the current conversation into a handoff document for another agent to pick up.
Chart a route through a foggy problem — turn a loose idea into a map of investigation tickets and resolve them one at a time until the way to the goal is clear.
This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the Google ADK (Agent Development Kit) skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.