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Content distribution

Skill siddiqss/semantic-seo-suite/skills/content-distribution

Grounded semantic SEO, GEO & off-page as Claude Code skills — with a fabrication guard that refuses to invent numbers. Free & MIT.

Install
npx -y skills add siddiqss/semantic-seo-suite --skill content-distribution

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Plan how a brand's published content gets seen — per-piece channel fit, repurposing atoms (X thread, LinkedIn post, Reddit answer, short-form video, newsletter), and a promotion cadence anchored to the publishing calendar. Use whenever the user asks how to promote or distribute content, repurpose a post, where to share an article, build a launch or content-promotion plan, drive traffic before SEO kicks in, or "we published it, now what". Matches channels to the brand's real personas; invents no reach numbers. Triggers on distribution / promotion / repurposing intent broadly.

SKILL.md

3.4 KB, as published. Nobody here has run it

content-distribution

Publishing isn't distribution. The map decides what to write; this makes sure each piece is seen while organic traffic is still compounding. It reads the same workspace, respects the tier, and points its plays at the brand's actual personas — not a generic channel list.

Read first: ../../framework/content-distribution.md (atoms, channel fit, cadence, the honesty rule).

Preconditions

  • entity-profile.json (audience + personas drive channel fit) + topical-map.json (statuses tell published from planned).
  • Naming the specific communities/newsletters needs web_search: true (T1). At T0 the plan proposes channel types + atoms + cadence but not named venues — say so; don't invent subreddits or metrics.

Workflow

  1. Build the plan (T0, offline).

    python ../../scripts/distribution_plan.py --map brands/<slug>/topical-map.json \
  --entity-profile brands/<slug>/entity-profile.json --brand "<Brand>" \
  --out brands/<slug>/outreach/distribution-plan.md

    Per node (published first, then core→outer): fitting channels, the repurposing atoms, and a cadence. Priorities/channel fit are derived; the atoms are asserted formats.

  2. Find the real venues (T1, web_search). For the top personas, discover the specific subreddits, Slack/Discord communities, newsletters, and creators the ICP actually uses. Record each measured + dated with why-relevant. Never assert a community exists without checking; never attach a reach/engagement estimate.

  3. Write the distribution plan → brands/<slug>/outreach/distribution-plan.md:

    • Priority-ordered pieces with channels, atoms, cadence.
    • The named venues per persona (measured), or an explicit note they weren't looked up (T0).
    • For a product that can demo itself (e.g. an AI video tool), flag the dogfood atom — generate the short-form asset with the product; the promo and the demo are one.

  4. Feed the loop.

    • Community questions worth answering → query-network additions via topical-map-builder.
    • A link or citation earned while promoting → hand to link-opportunities / answer-engine-optimizer.
    • Once GSC has data, seo-performance-tracker shows which distributed pieces actually converted attention to rankings.

Definition of done

  • distribution-plan.md written: priority-ordered, channels + atoms + cadence per piece.
  • Channels tied to the brand's real personas; venues measured (T1) or explicitly not looked up (T0). No invented reach numbers, no invented communities.

Grounding ladder

  • T0: offline channel types, repurposing atoms, cadence. Useful alone.
  • T1 (web_search): + named communities/newsletters/creators for the ICP, measured.
  • T2: no paid dependency.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.