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Found 6,717 Skills
Guide developers through creating a Slack app or agent using the Slack CLI and Bolt (JS or Python). Handles prerequisites, sandbox setup, authentication, project creation from templates, and local development.
Add an in-app feedback and bug-report loop that captures a screenshot plus network/console logs, emails the reporter, launches a Cursor Cloud Agent with autoCreatePr, and emails again when the PR opens and when it merges. Use when asked to add feedback, bug reporting, user reports that open PRs, Cursor automation for feedback, or a feedback-to-PR workflow.
Creates and maintains concise AGENTS.md and CLAUDE.md project instruction files. Use when asked to create AGENTS.md, update AGENTS.md, maintain agent docs, set up CLAUDE.md, document repository agent conventions, or keep coding-agent instructions minimal and reference-backed.
Build, test, publish, invoke, schedule, and update Railcode managed agents with the Railcode CLI. Use when creating an organization or personal managed agent, editing an agent manifest (JSON or YAML), running a draft or saved agent, investigating a run, managing its cron schedule, running it from Slack (@Railcode $agent), pairing it with a companion app, processing files in its sandbox, or using personal connectors (Gmail, Slack, ...) on behalf of one owner. Do not use for static Railcode apps, in-app LLM tool loops (llm.generate({ tools }) — see create-railcode-app), or general organization administration.
Delegate bounded domain analysis, research, independent review, or specialist execution to a focused sub-agent when the user requests a specialist or a specialized perspective, method, or evidence base would materially improve a separable subtask and current policy permits delegation. Do not use for ordinary work, vague expert role-play, tightly coupled tasks, or to transfer the parent agent's responsibility for verification.
Create or complete missing repository-level Codex guidance, especially AGENTS.md, using verified project evidence and the narrowest useful scope. Use for new or substantially incomplete guidance. Use kf-maintain-guidance to reduce, reorganize, or update established guidance. Do not use merely because work begins in a repository, or for ordinary feature implementation or bug fixing.
Solve an interactive hidden-rule ARC-AGI-3 game from its game ID. Contains the complete playing doctrine (predict before every action, graded claims, one-page notes, optional executable-rules search) plus the `arc` harness the agent drives. Use whenever asked to solve, play, or continue an ARC-AGI-3 game.
Update an existing AI agent on a live LiteLLM proxy. Ask for the agent_id and what to change (model, description, MCP servers), then call PATCH /v1/agents/{agent_id}.
Map plain-language Nexor requirements to exact configuration and build it through Nexor MCP tools. Covers arbitrary lead metadata, notifications, CRM sync, field/status events, qualification gates, workflow tools and hooks, webhooks, jobs, cloud/scheduled functions, cadence, channel and knowledge-base assignment, multi-agent systems, and transfers. Use when a customer asks how an agent can receive or use custom lead information, describes custom behavior, an integration, routing, or a multi-agent build. Discover account configuration before questions, decompose variables, stages, and boundaries, require plan review and sign-off before mutation, prefer deterministic primitives, and read back every expected channel, connection, and knowledge assignment regardless of creation order.
Use when working in a repository to find non-obvious simplification candidates — dead, duplicated, speculative, over-built, added-then-removed, or hand-rolled-where-a-dependency-exists surface across code, docs, and skills — and turn the worthwhile ones into proposed Agent Notes or inline TODO/FIXME/XXX markers; also when asked to audit or coalesce superseded Agent Notes, or fold worthwhile simplification ideas from another PR or branch.
Helps an agent inspect a repository and produce C4 architecture documentation. Use this when reverse engineering a codebase, documenting system context, containers, components, or clarifying architecture from code and infrastructure.
Generate targeted test scenarios for a LiveKit voice or chat agent and run them as simulations — locally, from the agent's own code plus what the user wants stress-tested. Use whenever the user wants to "test my agent", "what should I test", "create/generate simulation scenarios", "make a sim test suite", "use lk agent simulate", "stress-test the X flow", "set up scenarios for my agent", or wants to probe edge cases / refusals / regressions before shipping. Generates scenarios on the user's machine (their code is never uploaded) and lets the user deeply steer what gets tested. Trigger even without the word "simulation" when the user clearly wants to decide what to test and verify how their agent behaves across realistic conversations. Not for building a new agent from scratch (use the livekit-agents skill), load-testing, or ordinary unit tests.