Skills, workflows, frameworks.

Pulls 18 podcast RSS feeds plus a Vimeo library, transcribes new media via AssemblyAI, extracts topic ideas via Claude, writes to Notion. Twice-weekly cron.

One webhook → publishes the same post to X, Bluesky, Threads, and LinkedIn with per-channel formatting. The publish half of my content pipeline.

A practical map of where AI regulation actually binds marketers as of June 2026: a volatile US federal posture, the binding state patchwork, the EU AI Act timeline and its high-risk delay, copyright after the Bartz settlement, and the open gap on agentic AI. With 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, an implementation guide, and a decision tree. Built for CMOs and teams putting GenAI into production with consumer protection and compliance built in.

A 13-area checklist from MMA's Responsible AI Innovation Lab for adopting AI in marketing, from leadership alignment to vendor evaluation. Use it to audit where your organization stands 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.

A fill-in-the-blanks CLAUDE.md for Claude Code: communication rules, a verification section that stops the model grading its own homework, subagent routing by judgment required, adversarial review framed so it terminates, session handoff, safety, and context budget. Includes the maintenance discipline most configs skip.

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.