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.
4939Updated in 180 days
34Active this year
14Aging source
13Stale source
Recent UpdatesLatest indexed candidates, profile records, trust review, review history, and pagination stay available here.
Updated in 180 daysdspy-react-agent-builder skill by omidzamani/dspy-skills
This skill helps you build production-ready ReAct agents that reason, act, and call tools to solve complex tasks.
Review links
Updated in 180 daysdspy-gepa-reflective skill by omidzamani/dspy-skills
This skill optimizes complex agentic systems with reflective feedback and Pareto-based search to improve multi-step agents using textual traces.
Review links
Updated in 180 daysazure-test-plans skill by ntaksh42/agents
This skill helps you manage Azure Test Plans by creating test cases, suites, and executions to streamline QA workflows.
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Updated in 180 daysreinforcement-learning skill by omer-metin/skills-for-antigravity
This skill helps you implement reinforcement learning algorithms and tune agents using rewards, policy gradients, PPO, Q-learning, and RLHF in Python.
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Updated in 180 daysexpo skill by omer-metin/skills-for-antigravity
This skill helps you accelerate cross-platform React Native development with Expo, router navigation, and EAS build strategy for rapid iteration and reliable
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Updated in 180 daysdocs-engineer skill by omer-metin/skills-for-antigravity
This skill helps you craft developer-focused documentation by applying patterns, keeping docs current, and guiding readers from quick-start to advanced
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Recent page 224 / 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.