~/agentstafferAgentStaffer
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Roles / Technical Category
Review matching candidates
Next best action: Open a profile, compare evidence, then run a sandbox trial
Multi-agent Orchestration
Frameworks and tools for coordinating multiple agents, teams, roles, swarms, or AI workforces.
4955total
4955filtered
306page
413pages
Category fit
Which technical function should the agent perform?
Filter candidates by capability, protocol, trust, install readiness, and trial status.
- Coordinate several agents around one outcome.
- Assign specialist roles and handoffs.
- Inspect launch packets before running a team.
Recommended trials and bundles
playbooks / skill
letta-expert skill by willsigmon/sigstack
This skill helps you build AI agents with true long-term memory using Letta to remember, learn, and improve across sessions.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
explainx / agent
LevelFields
AI scans for events proven to impact stock prices, so you don't have to.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
lhon-research skill by openclaw/skills
This skill coordinates AI agents to fetch open LHON research tasks, synthesize findings, and submit structured results to GitHub for faster progress.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
lightning-agent-commerce skill by openclaw/skills
This skill enables end-to-end lightning network commerce for agents by setting up a complete payment stack and enabling paid data exchange.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
mcpservers / tool
Lightrun MCP Server
AI-powered live runtime debugging with Lightrun production context. Browse Lightrun MCP Server for Claude, Cursor, VS Code, and other AI agents.
MCPCLISDK
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
lily-memory skill by openclaw/skills
This skill enhances OpenClaw agents with persistent memory, auto-recall, auto-capture, and hybrid search for long-term context and loop prevention.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
lily-memory-5-0-0 skill by openclaw/skills
This skill provides persistent memory for OpenClaw agents, auto recalling and capturing facts with hybrid search and error detection to improve continuity.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
linear skill by letta-ai/skills
This skill helps manage Linear issues via GraphQL, enabling listing, updating, prioritizing, commenting, and searching to streamline backlogs.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
playbooks / skill
linearis skill by openclaw/skills
This skill provides a Linear.app CLI interface with JSON output to list, create, update, and search Linear issues for LLM agents.
MCPHTTPCLI
- Fit
- 61 / 100
- Trust
- Needs review
- Install
- MCP ready
Review links
Review support
Open Category guide
Review links
Technical category pages stay focused on job browsing; complete review records and finalist profile resources stay linked here.
Supporting review linksLinks, review order, and page-specific notes remain available here.
Category guideTechnical role guideAgent inventoryResource mapReview guideAPI referenceSite contextSitemapProfile resources
4955 matching candidates for Multi-agent Orchestration. Open candidate profiles for fit, setup guidance, trust evidence, and sandbox trial planning.
- 01Site contextShared review rules
- 02Role categoryOpen the focused review record
- 03Agent inventoryBrowse the matching inventory
- 04Profile resourcesEvidence, setup, trust, trial, and history files
Use the links and review path below for role-category candidates, related playbooks, 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.