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Cloud Claw deployments require correct agent choices, names, tokens, and quota checks. This skill guides new launches through the local AltLLM CLI.

Delegated agents need review rules before they can safely keep authority. This skill helps define GenLayer evidence, rubrics, decisions, and revocation handoffs.

MCP server work often fails when tools, resources, prompts, widgets, and authentication are mixed without clear patterns. This skill gives Claude, Codex, and Claude Code focused mcp-use guidance for implementation, review, and deployment.

Email teams need prompts and drafts that stay accurate, reviewable, and safe. This skill guides Claude, Codex, and Claude Code through briefs, critique loops, variants, and approval gates.

Email implementation often spans templates, content, tests, and rollout approvals. This skill helps AI agents make scoped repository changes with QA checks and approval boundaries.

Email work often spans agents, tools, approvals, and live systems, which raises execution risk. This skill structures handoffs, QA checks, and approval gates before action.

Responsive email work often fails across clients, devices, and dark mode. This skill guides structured audits, module planning, and handoff checks for reliable email production.

Newsletter teams need plans that balance editorial quality, sponsor obligations, and reader trust. This skill helps Claude, Codex, and Claude Code produce structured plans, audits, and review gates for email work.

Email campaign work can hide risky provider actions and unclear assumptions. This skill turns audits, migrations, and execution plans into inspectable playbooks with approval gates.

Kubernetes delivery is hard to keep consistent across teams and clusters. This skill helps Claude, Codex, and Claude Code design GitOps workflows with ArgoCD, Flux, sync policies, and safe deployment patterns.

Creating Claude Code extensions from scratch can lead to missing metadata and unclear structure. This skill provides starter templates for agents, skills, commands, and hooks.

AI agents often repeat mistakes without structured reflection. This skill provides daily drills, scoring rubrics, journals, and testable improvement hypotheses.