Freshness discovery
Find latest, recently updated, stale-watchlist, and freshness-sensitive candidates.
~/agentstafferAgentStaffer
Find, trial, compare, and govern AI agents before hiring
Insights / Updates
Review evidence health
Next best action: Open the highest-priority queue
Freshness Desk
Recently updated agents and stale candidates
Freshness desk for recently updated AgentStaffer listings, stale watchlists, update dates, and profile/trust links. Use re-review for governance decisions.
Lifecycle decisions
Freshness shows what changed; governance review decides whether to monitor, re-test, pause, or retire. Open governance queue.
Freshness BucketsBucket counts and freshness boundaries stay available here.
4951Updated in 180 days
22Active this year
14Aging source
13Stale source
Recent UpdatesLatest indexed candidates, profile records, trust review, review history, and pagination stay available here.
Updated in 180 daysclaude-code-mcp skill by vasilyu1983/ai-agents-public
Configure and build Model Context Protocol (MCP) servers for Claude Code integration. Set up database, filesystem, git, and API connections. Build custom MCP servers with TypeScript/Python SDK, implement tools and resources, configure transports (stdio, HTTP), and deploy for production.
Review links
Updated in 180 dayshelp-center-design skill by vasilyu1983/ai-agents-public
Design or audit AI-first help centers/knowledge bases/FAQs, including taxonomy, article templates, analytics, and AI support (RAG, chatbot, escalation), using 2025-2026 best practices
Review links
Updated in 180 daysdocs-codebase skill by vasilyu1983/ai-agents-public
Technical writing patterns for README files, API documentation, architecture decision records (ADRs), changelogs, contributing guides, code comments, and docs-as-code workflows. Covers documentation structure, style guides, Markdown best practices, and documentation testing.
Review links
Updated in 180 daysdata-analytics-engineering skill by vasilyu1983/ai-agents-public
Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.
Review links
Updated in 180 daysdev-api-design skill by vasilyu1983/ai-agents-public
Production-grade API design patterns for REST, GraphQL, gRPC, and tRPC. Covers API architecture, OpenAPI/Swagger specs, versioning/deprecation, authentication/authorization, rate limiting, pagination, error models, contract testing, and developer documentation.
Review links
Updated in 180 daysdata-lake-platform skill by vasilyu1983/ai-agents-public
Data lake and lakehouse platform patterns: ingestion/CDC, transformations, open table formats (Iceberg/Delta/Hudi), query and serving engines (Trino/ClickHouse/DuckDB), orchestration, governance/lineage, cost and operations. Self-hosted and cloud options.
Review links
Recent page 54 / 417 · 5000 listings
Stale WatchlistStale candidates, governance queue links, review history, and pagination stay available here.
Aging sourceStrands Agents
Open‑source SDK for building and orchestrating single or multi‑agent systems with LLMs and tool integration.
Review links
Review links
Aging sourceAirtable MCP
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.
Review links
Stale page 3 / 3 · 27 watchlist items
Review support
Open Freshness feed JSON
Review links
Freshness cards stay readable; re-review records and profile resources stay linked here.
Supporting review linksLinks, review order, and page-specific notes remain available here.
Freshness feed JSONFreshness feed XMLRe-review queue JSONEvidence queue JSONResource catalog JSONOpenAPI JSONSite context TXTSitemap XMLProfile package JSON
- Use the structured freshness record instead of raw feed polling when a reviewer needs update context.
- Use pagination.nextPageUrl to continue through the recent-update index.
- For latest recommendations, use recentUpdates[] but still fetch profileJson, trustJson, trialJson, and installJson before action.
- Use staleWatchlist[] to identify candidates that need source freshness evidence, then open /re-review.json for pause, re-test, or retire decisions.
- Do not treat a fresh source update as safety approval; inspect trustJson, evidenceJson, and installJson first.
- 01Site contextShared review rules
- 02Freshness watchOpen the focused review record
- 03Profile pageOpen the candidate page
- 04Profile resourcesEvidence, setup, trust, trial, and history files
Use the links and review path below for freshness context, re-review records, and finalist profile resources before action.
Review route: OpenAI review tools can start with openai.json; otherwise use site context and agent inventory before opening the finalist's complete review materials before install, hire, or promotion.