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
npx -y skills add siddiqss/semantic-seo-suite --skill content-distributionAssembled 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
-
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.mdPer node (published first, then core→outer): fitting channels, the repurposing atoms, and a cadence. Priorities/channel fit are
derived; the atoms areassertedformats. -
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. -
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.
-
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.mdwritten: 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/
- evals.json1019 B