All Skills

Render A2UI (Agent-to-UI declarative surfaces) in CopilotKit v2. Enable the runtime via CopilotRuntime({ a2ui: {...} }), then enable the provider via <CopilotKit a2ui={{ theme }}>. Auto-activates via /info — do NOT manually pass renderActivityMessages. createA2UIMessageRenderer ships from @copilotkit/react-core/v2; low-level primitives (A2UIProvider, A2UIRenderer, createCatalog) ship from @copilotkit/a2ui-renderer. Covers theme customization, createSurface dedup, action-bridge try/finally cleanup. Load when an agent emits A2UI operations (createSurface / updateComponents / updateDataModel), when wiring a2ui on CopilotRuntime, or when styling A2UI surfaces.

Use for the CODE half of a managed Intelligence Channel with Slack or Microsoft Teams: customising the Channel a CLI-scaffolded project already ships, or — for a project the CLI did not generate — writing the Channel declaration, the long-running host, and the awaited activation call. Teams provider setup is in scope, because the CLI or dashboard wizard performs it. Creating a Slack app for the first time is not: if no Slack app exists yet, use setup-slack-channel for the provider half and return here for the code.

Use when migrating a CopilotKit v1 application to v2 -- updating package imports, replacing deprecated hooks and components, switching from GraphQL runtime to AG-UI protocol runtime, and resolving breaking API changes.

@copilotkit/react-core — mount the CopilotKit provider (from @copilotkit/react-core/v2) in a Next.js App Router / React Router v7 / TanStack Start / SPA app, drop in CopilotChat/CopilotPopup/CopilotSidebar (v2 chat components ship from react-core/v2 — NOT react-ui, which is CSS-only in v2), access and subscribe to agents with useAgent / useAgentContext / useCapabilities, switch between multiple agents, manage durable Intelligence threads with useThreads, register browser-side tools via useFrontendTool, render tool calls with useRenderTool / useComponent / useDefaultRenderTool, gate execution with useHumanInTheLoop, wire file attachments with useAttachments, configure suggestion pills, and register activity- and custom-message renderers. publicLicenseKey is canonical (publicApiKey is deprecated alias). Load the reference under references/ that matches your task.

Use when contributing to the CopilotKit open-source project — forking, cloning, setting up the monorepo, creating branches, running tests, and submitting pull requests against CopilotKit/CopilotKit.

UX Writing

wait X seconds/minutes (user will provide input). use `sleep` command. DONT do it in background

polish

Resolve all active PR comments (conversation + code review).

Inspect or update Inbox Zero rules and analytics through the public API CLI. Use when tasks involve rules, stats, or API-driven automation.

roast-my-code

Continue to the next step.

Step-by-Step Execution

Generate a conventional commit message by analyzing staged git changes. Use when the user wants to create, write, or generate a git commit message from their current staged diff.

Migrates external consumer code from the mongodb-mcp-server v1/v2 single-package API to the v3 scoped-package structure. In v3, mongodb-mcp-server is a binary-only package (npx / MCPB); library embedding uses @mongodb-js/mcp-cli, @mongodb-js/mcp-core, @mongodb-js/mcp-http-runners, @mongodb-js/mcp-tools-*, and the other @mongodb-js/mcp-* packages. Use when asked to migrate a project embedding or extending mongodb-mcp-server ("update my code to v3", "fix imports after the v3 release", "migrate my custom server/tools to the new packages"), or by external users following the v3 migration guide.

Review a GitHub pull request for the OpenOps codebase. Checks out the PR, analyzes changes through OpenOps-specific lenses, validates findings with confidence scoring, previews before posting, and supports inline comments.

This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.

This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.

This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.

This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.