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 set up Langfuse observability, monitor LLM costs, and manage prompts for production with tracing and evaluation.
This skill performs deep PR review using parallel specialized agents to quickly identify security, quality, and architecture issues.
This skill provides proven MCP server patterns for building, securing, and debugging deployments with advanced authentication, UI, and tool integrations.
This skill provides production-ready LLM integration patterns for function calling, streaming, local inference, and fine-tuning to streamline tool use.
This skill enables rapid feature implementation with parallel subagents and scope-driven reflection to accelerate development from conception to tests.
This skill conducts multi-angle codebase exploration with parallel agents to map structure, data flow, and architecture for faster understanding.
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.