Video & Animation

Creates Buildkite pipelines dynamically using the Buildkite REST API and pipeline upload commands. Manages agent targeting with queue tags and implements automatic retry strategies for flaky tests.

Monitors Buildkite agent pools via the Buildkite REST API v3 and GraphQL API. Tracks agent connectivity, queue depth, job wait times, and dispatches Slack alerts for stalled or disconnected agents.

Auto-scales Buildkite agent fleets based on queue depth and job wait times using the Buildkite GraphQL API. Manages AWS EC2 spot instances and Kubernetes HPA configurations for elastic CI capacity.

Use Pipecat to define realtime voice and multimodal agent pipelines with transports, model providers, tools, and turn-taking tests.

Use Material for MkDocs to turn Markdown documentation into a searchable, navigable static site with project-ready configuration and previews.

Use pgrx when an agent needs the Rust-native extension lifecycle for PostgreSQL, including local builds, test runs, and packaging across PostgreSQL versions.

Connect coding agents to Xcode build, test, simulator, and project-inspection tools through XcodeBuildMCP

Use Graphiti when an agent needs long-term memory that tracks what changed, when it changed, and which source episode produced each fact, instead of storing flat chunks or chat history alone.

Ingest disk, log, and system artifacts into a sortable forensic timeline before analysis, scoping, or case review.

Use Letta to create agents whose identity, tools, memory blocks, and conversation state persist across sessions for long-running assistant or application workflows.

Use TiDB when an agent needs one transactional SQL store that can also hold embeddings and serve vector retrieval for RAG, memory, or app-context workflows.

Use assistant-ui to build production React chat interfaces with reusable threads, runtimes, message components, and model/provider adapters.

Give Claude Code a routed skill pack for designing, validating, debugging, and deploying n8n workflows through n8n-mcp.

Model Go-native agent systems with graph workflows, tool calls, memory, MCP, A2A, evaluation, and OpenTelemetry observability.

Use Strands Agents to assemble model-agnostic Python or TypeScript agent harnesses with tools, MCP, guardrails, tracing, streaming, and provider swaps.

Use Memvid when an agent needs local, portable long-term memory and retrieval without running a vector database or full RAG service.

Compose prompts, plugins, memory, planners, and connectors into repeatable Python, .NET, or Java agent workflows with Microsoft Semantic Kernel.

Build and maintain a repository-local context graph so coding agents can orient inside large codebases without rediscovering the same structure every session.

Use deepdoctection when an agent workflow needs a local Python pipeline for document layout analysis, OCR, table recognition, and page-level extraction before LLM ingestion.

Turn a repository or wiki into an interactive knowledge graph that agents and humans can search, explore, and query before implementation work.

Compose, orchestrate, and trace production multi-agent workflows with Microsoft Agent Framework.

Inspect n8n nodes and templates, generate typed workflow code, and sync automations through Git-friendly files instead of hand-editing workflow JSON.

Author, validate, test, and troubleshoot Copilot Studio agents through YAML-driven commands instead of clicking through the product UI.

Use Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production code.