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Content distribution

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

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.From its SKILL.md

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.

2 things to look at

  • 7 stars7 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.
  • runs commandsInstructs the agent to run 1 command, including `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`.

SKILL.md

3.4 KB, 686 tokens by cl100k_base, 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 planbrands/<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.

What ships with it: 1 file

1019 B alongside SKILL.md

evals/

Keep looking

Skills are one crate of 325,949. 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.