Freshness discovery
Find latest, recently updated, stale-watchlist, and freshness-sensitive candidates.
Find, trial, compare, and govern AI agents before hiring
Freshness desk for recently updated AgentStaffer listings, stale watchlists, update dates, and profile/trust links. Use re-review for governance decisions.
Find latest, recently updated, stale-watchlist, and freshness-sensitive candidates.
Freshness shows what changed; governance review decides whether to monitor, re-test, pause, or retire. Open governance queue.
This skill helps you design and implement production-ready model deployment strategies, including serving, packaging, versioning, and scalable infrastructure.
This skill helps you design and expose MCP servers quickly with tools, resources, and prompts to extend Claude with custom capabilities.
This skill helps you design and implement end-to-end AI integration patterns for vision, audio, embeddings, and RAG in applications.
This skill helps you build autonomous AI agents with tool use and planning strategies to automate multi-step workflows.
This skill performs structured, actionable Python code reviews with clear findings and suggestions to improve correctness, security, and maintainability.
This skill helps you insert purposeful debug logging to enhance observability without altering behavior in critical modules.
Open‑source SDK for building and orchestrating single or multi‑agent systems with LLMs and tool integration.
Airtable integration for AI-powered applications via Anthropic's Model Context Protocol (MCP). Connect your AI tools directly to Airtable for seamless data access and management.
Freshness cards stay readable; re-review records and profile resources stay linked here.
Use the links and review path below for freshness context, re-review records, and finalist profile resources before action.