By curiositech
Authoritative port management and multi-agent coordination for local development. Atomic port assignment, pub/sub messaging, distributed locks, and agent registry via CLI, SDK, or HTTP API.
Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.
Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).
Requires secrets
Needs API keys or credentials to function
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Stop your agents from fighting each other.
Atomic port assignment, session coordination, pub/sub messaging, and agent resurrection — one daemon, zero config.
Port Daddy is a daemon that gives every AI agent its own port, coordinates file access, and recovers work when they crash. One install, zero config.
Examples in this README assume the default local daemon URL http://localhost:9876. If your daemon is running on a different port, use pd status or set PORT_DADDY_URL before copying the HTTP examples.
While individual agents are brilliant, coordination is the bottleneck. Port Daddy provides the missing primitives: atomic port assignment, pub/sub messaging, distributed locks, session trails, and agent resurrection.
# Start working (registers agent + claims port + starts session)
pd begin "Building the auth layer" --identity myapp:api --lifecycle durable
# Log progress, coordinate with other agents
pd note "JWT validation passing all tests"
pd pub api:ready '{"endpoints": ["/login", "/register"]}'
# Done (ends session + releases everything)
pd done "Auth complete"
myapp:api) map to stable, deterministic ports.*.pd.local) to visualize active agents, service health, and message traffic.http://api.pd.local instead of magic port numbers.dist/daemon/port-daddy-daemon executable when that artifact is present.# Via Homebrew (macOS)
brew install curiositech/tap/port-daddy
# Via npm
npm install -g port-daddy
# Optional signed Mac menu-bar app from the public site
curl -LO https://portdaddy.dev/downloads/PortDaddy-FleetBar-macOS-arm64.zip
curl -LO https://portdaddy.dev/downloads/PortDaddy-FleetBar-macOS-arm64.zip.sha256
shasum -a 256 -c PortDaddy-FleetBar-macOS-arm64.zip.sha256
unzip PortDaddy-FleetBar-macOS-arm64.zip
pd doctor # Verify environment
pd start # Start the daemon
pd bench 50 # Run performance benchmarks (Target: <1ms latency)
pd start and pd install are binary-first. They refuse to start a source-backed tsx server.ts daemon unless PORT_DADDY_ALLOW_SOURCE_DAEMON=1 is set for a local development session. Release promotion builds the daemon executable, builds the public sample bundle, installs the binary service, and checks the macOS LaunchAgent did not regress to tsx server.ts.
npx claudepluginhub curiositech/port-daddy --plugin port-daddy463+ skills for engineering, design, research, and productivity. Includes /next-move — a 5-agent meta-DAG that predicts your highest-impact next action.
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Repowire mesh usage skills for AI coding agents: cross-agent review and planning, delegate, usage patterns, and install/update. Backend-agnostic and parameterised on the agent you choose.
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