Marketing distribution

Once the writing track publishes a post, that post becomes the marketing track’s input. The entry point is marketing-director. To run only the distribution of an already-published post without writing it yourself, you call this track directly. Utterances like “how should I spread this post,” “what do I do after publishing,” and “start marketing” are triggers.

Foundation first

marketing-director checks .claude/product-marketing-context.md before starting any work. If that document is missing or stale, it updates it first. The ICP, keyword areas, and primary channels are planted there, and every marketing skill references this document. Right after a fork it’s a placeholder, so you have to fill in your blog’s identity first through /start or /product-marketing-context for the marketing track to run meaningfully.

Routing and priority

marketing-director doesn’t write copy itself. It decides which channel to prioritize a published post on and which work to delegate to which agent.

• Holds directly: product-marketing-context (the Foundation doc), community-marketing (Reddit and Hacker News posting strategy)
• Delegates to content-lead: copywriting, copy-editing, social-content (X threads)
• Delegates to seo-discovery-lead: ai-seo (SEO aimed at LLM citations)

Priority is sorted by ICE (Impact × Confidence × Ease) or RICE. This is a guard against growing the channel count without limit. Adding a channel beyond the primary channels planted in product-marketing-context needs user approval. Ad channels and email sequences are out of current scope.

A distribution action states its hypothesis first

The marketing track runs on five principles (MKTS). The three that come up most often in distribution are these.

• Hypothesis Before Execution — state a hypothesis before a distribution action. Something like “this tone should fit r/ClaudeAI, because a first-person trial-and-error tone matches the top-post pattern of that subreddit.”
• Customer Verbatim — copy and persona decisions prioritize actual customer quotes. If it’s a guess, mark it [hypothesis].
• Peeking Problem — don’t conclude on A/B results or distribution metrics before statistical significance. A blog has a small sample, so this is a spot to be especially careful.

AdSense safety gets looked at in distribution copy too. Language that directly urges a purchase clashes with the ad context, so it gets dropped from distribution copy.

AI-SEO’s three axes

This is what seo-discovery-lead handles. Where traditional SEO aims for a search-results rank, AI-SEO aims to get Claude, ChatGPT, or Perplexity to cite your post when they build an answer. It diagnoses a post across three axes.

• Entity authority — whether you’re recognized as an authoritative source on the topic the post covers. Whether entities appear consistently and connect to other trusted sources.
• Citation worthiness — whether the form is good to cite. Whether the structure — clear definitions, step-by-step procedures, comparison tables — is easy to excerpt and cite.
• Freshness — whether it’s a topic where recency affects answer selection. Whether dates and update history act as signals.

On top of that come Article, FAQPage, and HowTo schema, internal linking between a series and related posts, and query-intent classification (informational, navigational, transactional). Citation data is read over a window of 90 days or more, not concluded on a small sample.

Running a series

When a series is in progress, the distribution of each episode is coordinated so it doesn’t clash with the payoff of the next episode’s setup. For a three-parter, episode one’s distribution lays the setup that episode two will pay off. Because the AI-smell gate flags a teaser like “continued in the next part” with points on even-numbered episodes, a teaser goes only where the series structure makes it natural.

Next

The next section is updates. It covers where /update-from-base brings the base’s new commits into your blog’s repository, and the policy that splits base-owned from user-owned.