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Found 1,462 Skills
Orchestrate Zoen product engineering from discovery to production using the curated Matt Pocock skill set, Extreme Programming, ontology-first architecture, and risk-controlled delivery. Use for any Zoen product, architecture, feature, bug, refactor, AI-agent, integration, release, or engineering-workflow decision, especially nouns-links-verbs-evidence, AgentRun/ToolCall, Action Gateway, approvals, audit and provenance, tenancy, client-owned export, or production readiness.
Plan and run AI-agent dynamic workflows for complex tasks that benefit from explicit orchestration, goal mode, subagents or simulated work packets, approval gates, integration, verification, and reusable workflow artifacts. Use when the user invokes this skill, asks for a swarm, subagents, parallel agents, a dynamic workflow, a large migration or audit, multi-track research plus implementation, or Claude Code-style workflow orchestration.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when "build agent, AI agent, autonomous agent, tool use, function calling, multi-agent, agent memory, agent planning, langchain agent, crewai, autogen, claude agent sdk, ai-agents, langchain, autogen, crewai, tool-use, function-calling, autonomous, llm, orchestration" mentioned.
Use when installing skills from a shared ai-agent-skills library repo. Inspect with `--list` first, prefer `--collection`, and preview with `--dry-run` before installing.
Find, compare, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks for a loop, recurring agent workflow, automation cadence, iterative improvement process, an existing Loop Library recommendation, or help turning an outcome into a bounded copy-ready loop through a short question-led design session.
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
Discover, find, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks to analyze a codebase for potential loops, mine coding-thread history for work done more than once, turn repeated engineering work into a loop, find or recommend a published loop, create a recurring agent workflow or automation cadence, turn an outcome into a bounded copy-ready loop, or review an existing loop for weak checks, unsafe authority, unbounded repetition, stale state, or unclear stopping behavior.
Manage an AI-agent project from request through decomposition, preparation, routing, execution, monitoring, review, and closure using durable task records. Use when asked to manage, coordinate, run, or track a multi-task project.
Use to populate the Omni-Channel Supervisor action + tab surface on an existing OmniSupervisorConfig by inserting the standard OmniSupervisorConfigAction and OmniSupervisorConfigTab companion rows via the Data API, idempotent (inserts only the missing types). Requires an OmniSupervisorConfig from service-omni-supervisor-config-deploy. Triggers: give the supervisor the standard action buttons and tabs, add Wallboard/Agents/Queues tabs to Omni Supervisor, configure supervisor actions like Change Queues/Skills. Do not use to create the supervisor configuration itself, create supervisor users, assign the supervisor permission set, or add custom-action / FlexiPage / AWS-dashboard / AI-agent surfaces (those need an external reference and must be set up in Setup).
Build or rebuild a private, local-first macOS system that measures daily Git commits, active AI-agent sessions, and instruction prompts, then sends aggregate charts to Discord. Use when someone asks to set up an agentic productivity tracker, install daily agent reports, recreate this architecture on a Mac, or understand how to build it safely. Differentiator: creates a fresh system from generic contracts and never copies another person's data, paths, credentials, repositories, prompts, or private configuration.
DeepWorkPlan — turn any repo AI-first and run Deep Work Plans. Routes to create, execute, refine, resume, status, verify, and repo-onboarding sub-skills based on intent. Use when the developer wants to plan, execute, manage, or verify structured multi-task work, or make a repository AI-agent-ready.
Frame a messy supply-chain pain point as a specific, owned decision; determine whether AI is appropriate; and select a bounded delegation model and use case. Use when leaders, planners, consultants, or transformation teams need to scope an AI-agent opportunity, compare candidate use cases, define human approval boundaries, or decide that process, data, or governance must be fixed before AI.