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Found 6,669 Skills
Search agentmemory for past observations, sessions, and learnings about a topic. Use when the user says "recall", "remember", "what did we do", or needs context from past sessions.
List recent git commits that are linked to agent sessions, optionally filtered by branch or repo. Use when the user asks "show agent commits", "what has the agent shipped", or wants a list of commits with their session context.
DingTalk Workspace CLI (dws) — cross-platform tool for managing DingTalk enterprise data (contacts, calendars, docs, todos, AI tables, chat) via command line and AI agents
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
The first Outlook calendar CLI built for AI agents on personal Microsoft 365 accounts — with offline conflict... Trigger phrases: `what's on my calendar today`, `find me an hour next week`, `do I have any conflicts`, `what meetings haven't I responded to`, `prep me for my next meeting`, `schedule a meeting on my Outlook calendar`, `use outlook-calendar`, `run outlook-calendar`.
Use when the user asks to "create an evaluator", "create evals", "create a scenario", "write a test scenario", "design a test case", "test my agent", "build eval coverage", "plan a test suite", "create red team tests", "set up test profiles", "configure conditional actions", "write a conditional action evaluator", "build a deterministic test", "design an IVR test", "IVR navigation test", "write a unit test for a voice agent", "build a regression test", "scripted scenario", "scripted voice test", "structured evaluator", "exact flow test", "sequential conditions", "fixed sequence test", or "run evals". Covers individual evaluator design, suite coverage strategy, test profiles, mock-tool data design, conditional actions (deterministic / unit test / regression / IVR navigation flows), and best practices for workflow / red-team / edge-case / deterministic test types.
Use when the user asks to "create an agent", "set up an agent", "add my agent to Cekura", "configure my voice agent", "connect my agent", "set up mock tools", "add tools to my agent", "upload knowledge base", "configure integration", "connect VAPI", "connect Retell", "connect LiveKit", "connect ElevenLabs", "add dynamic variables", or needs to onboard a voice AI agent onto the Cekura platform. Covers the full agent setup flow: collecting context, creating the agent, configuring the provider integration, setting up mock tools, uploading knowledge base files, and adding dynamic variables.
Use to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions.
Loads documents fully into the main agent's context so the agent can answer questions, summarize, or work with that content in subsequent turns. Use whenever the user wants to ingest, read, study, review, absorb, or pull in documents — especially when they say things like "load these docs", "read all of these", "ingest this folder", "pull in these PDFs", "load all docs in X", or paste a list of file paths/URLs and ask you to read them. Handles local files (text, code, markdown, PDFs, notebooks, images), entire folders (recursively), and remote URLs. The skill is single-turn — once the agent reports "DONE", it deactivates until the user invokes it again.
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For deployment, use agent-platform-deploy.
Run a spec-driven agent loop where coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and pass a review gate before they count as done. Use when the user mentions "loop factory", a "spec-driven loop", an "agent factory", wants repeatable/reviewable agent work, or when a repo has a factory/specs/inbox or factory/specs/active directory. Also covers installing and scaffolding the loop-factory CLI into a project.
Set up a lightweight Google Alerts-style coverage tracker for any number of keywords. Creates a tracker config with each keyword and what it actually means, then hands recurrence to the user's agent harness.