AgentWorkforce
26 verified skills42 total stars
choosing-swarm-patterns
Use when coordinating multiple AI agents and need to pick the right orchestration pattern - covers 10 patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) with decision framework and reflection protocol
development3
creating-skills
Use when creating new Claude Code skills or improving existing ones - ensures skills are discoverable, scannable, and effective through proper structure, CSO optimization, and real examples
development3
optimizing-smithery-score
Use when publishing an MCP server to Smithery and need to maximize the quality score - covers scoring categories, tool metadata requirements, deploy reliability, and known external deployment limitations
tools3
prpm-json-best-practices
Best practices for structuring prpm.json package manifests with required fields, tags, organization, multi-package management, enhanced file format, eager/lazy activation, and conversion hints
development3
relay-80-100-workflow
Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to commit.
development3
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops3
packages/openclaw/skill
--- summary: Structured messaging for multi-claw communication — channels, threads, DMs, reactions, search, and persistent history. --- # Relaycast Structured messaging for multi-claw communication. Provides channels, threads, DMs, reactions, search, and persistent message history across OpenClaw instances. ## Environment - `RELAY_API_KEY` — Your Relaycast workspace key (required) - `RELAY_CLAW_NAME` — This claw's agent name in Relaycast (required) - `RELAY_BASE_URL` — API endpoint (default:
development3
writing-agent-relay-workflows
Use when building multi-agent workflows with the relay broker-sdk - covers the WorkflowBuilder API, DAG step dependencies, agent definitions, step output chaining via {{steps.X.output}}, verification gates, evidence-based completion, owner decisions, dedicated channels, dynamic channel management (subscribe/unsubscribe/mute/unmute), swarm patterns, error handling, event listeners, step sizing rules, authoring best practices, and the lead+workers team pattern for complex steps
development3
writing-agent-relay-workflows
Use when building multi-agent workflows with the relay broker-sdk - covers the WorkflowBuilder API, DAG step dependencies, agent definitions, step output chaining via {{steps.X.output}}, verification gates, evidence-based completion, owner decisions, dedicated channels, dynamic channel management (subscribe/unsubscribe/mute/unmute), swarm patterns, error handling, event listeners, step sizing rules, authoring best practices, and the lead+workers team pattern for complex steps
development3
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops3
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops3
relay-80-100-workflow
Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to commit.
development3
using-agent-relay
Use when coordinating multiple AI agents in real-time - provides inter-agent messaging via MCP tools
tools3
choosing-swarm-patterns
Use when coordinating multiple AI agents and need to pick the right orchestration pattern - covers 10 patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) with decision framework and reflection protocol
development3
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops
writing-agent-relay-workflows
Use when building multi-agent workflows with the relay broker-sdk - covers the WorkflowBuilder API, DAG step dependencies, agent definitions, step output chaining via {{steps.X.output}}, verification gates, evidence-based completion, owner decisions, dedicated channels, dynamic channel management (subscribe/unsubscribe/mute/unmute), swarm patterns, error handling, event listeners, step sizing rules, authoring best practices, and the lead+workers team pattern for complex steps
development
relayfile-workspace
Use when working in a shared relayfile virtual filesystem with other agents - covers reading/writing files with metadata, discovering other agents' work, conflict handling, ACL permissions, and real-time collaboration patterns
data-ai
choosing-swarm-patterns
Use when coordinating multiple AI agents and need to pick the right orchestration pattern - covers 10 patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) with decision framework and reflection protocol
development
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops
choosing-swarm-patterns
Use when coordinating multiple AI agents and need to pick the right orchestration pattern - covers 10 patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) with decision framework and reflection protocol
development
creating-claude-rules
Use when creating or fixing .claude/rules/ files - provides correct paths frontmatter (not globs), glob patterns, and avoids Cursor-specific fields like alwaysApply
testing
creating-claude-rules
Use when creating or fixing .claude/rules/ files - provides correct paths frontmatter (not globs), glob patterns, and avoids Cursor-specific fields like alwaysApply
testing
relay-80-100-workflow
Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to commit.
development
running-headless-orchestrator
Use when an agent needs to self-bootstrap agent-relay and autonomously manage a team of workers - covers infrastructure startup, agent spawning, lifecycle monitoring, and team coordination without human intervention
devops
writing-agent-relay-workflows
Use when building multi-agent workflows with the relay broker-sdk - covers the WorkflowBuilder API, DAG step dependencies, agent definitions, step output chaining via {{steps.X.output}}, verification gates, evidence-based completion, owner decisions, dedicated channels, dynamic channel management (subscribe/unsubscribe/mute/unmute), swarm patterns, error handling, event listeners, step sizing rules, authoring best practices, and the lead+workers team pattern for complex steps
development
relay-80-100-workflow
Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to commit.
development