Skills, workflows, frameworks.

Pulls 18 podcast RSS feeds and a Vimeo library twice a week, transcribes new media with AssemblyAI, extracts ideas with Claude, and files them to Notion.

One webhook call publishes a post to X, Bluesky, Threads, and LinkedIn, formatted per channel. The publish half of my content pipeline.

A map of where AI regulation binds marketers as of June 2026: a volatile US federal posture, the state patchwork, the EU AI Act timeline and its high-risk delay, copyright after the Bartz settlement, the open gap on agentic AI, and five moves to make now.

A framework for measuring AI's impact on marketing across five KPI categories: operational efficiency, customer engagement, revenue growth, responsible-AI governance, and strategic value. Includes baseline-setting, monitoring, audits, and a sample AI program scorecard.

MMA's framework for governing generative AI in marketing: governance dimensions, a five-step implementation guide, and a decision tree for approving use cases. For CMOs and teams taking GenAI to production.

A 13-area checklist from MMA's Responsible AI Innovation Lab for adopting AI in marketing, covering leadership, governance, data, vendors, risk, and monitoring. Use it to audit where you stand and what to fix first.

A repeatable way to score the risk of an AI marketing initiative. Applies a three-factor LIME method (Likelihood, Impact, Mitigation effort) across privacy, accuracy, ethics, and compliance, so teams can evaluate before and during deployment.

Fill-in-the-blanks operating rules for a coding agent: a CLAUDE.md for Claude Code and an AGENTS.md that also works in the Codex CLI.

A structured template for evaluating AI vendors before you buy: technical capability, data practices, responsible-AI alignment, a 1–5 automation-level scale, demo-and-trial observations, a procurement roadmap, and a full question bank. Updated with an agentic-capability assessment.

A fill-in-the-blanks generative AI usage policy for your organization: approved tools, data confidentiality, IP, bias and hallucination controls, and an AI Council approval process. Includes a 2026 appendix on governing agentic AI and MCP integrations.

An organizational responsible-AI charter you can adopt and customize: 11 principles spanning human oversight, fairness, transparency, privacy, IP, accountability, vendor accountability, safety, and societal benefit, plus explicit authorized and unauthorized use cases. Grounded in the NIST AI RMF, OECD, and the EU AI Act.