answer-engine-optimize…
Optimize a brand's mapped content to get cited by AI answer engines — Google AI Overviews, ChatGPT, Perplexity, Gemini. Scores drafts for citation-readiness…
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
$ npx -y skills add siddiqss/semantic-seo-suite --skill content-distribution --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/content-distributionContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: content-distribution description: > 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.
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).
(statuses tell published from planned).
plan proposes channel types + atoms + cadence but not named venues — say so; don't invent subreddits or metrics.
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.mdPer 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`:
(T0).
the short-form asset with the product; the promo and the demo are one.
4. **Feed the loop.**
**topical-map-builder**.
**answer-engine-optimizer**.
actually converted attention to rankings.
looked up (T0). No invented reach numbers, no invented communities.
A free, open-source alternative to Surfer, MarketMuse, Clearscope & Frase — built as Claude Code skills. Topical authority, content briefs, AI writing, schema, GEO, and backlinks. With a fabrication guard that refuses to invent numbers.
Optimize a brand's mapped content to get cited by AI answer engines — Google AI Overviews, ChatGPT, Perplexity, Gemini. Scores drafts for citation-readiness…
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