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…
Generate an internal-linking plan and JSON-LD structured data from a brand's topical map and entity profile. Use whenever the user asks about internal links, anchor text, site structure, linking strategy, schema markup, structured data, or JSON-LD — or right after new content is
$ npx -y skills add siddiqss/semantic-seo-suite --skill linking-and-schema --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/linking-and-schemaContext preview
The summary Claude sees to decide when to auto-load this skill.
Generate an internal-linking plan and JSON-LD structured data from a brand's topical map and entity profile. Use whenever the user asks about internal links, anchor text, site structure, linking strategy, schema markup, structured data, or JSON-LD — or right after new content is
name: linking-and-schema description: > Generate an internal-linking plan and JSON-LD structured data from a brand's topical map and entity profile. Use whenever the user asks about internal links, anchor text, site structure, linking strategy, schema markup, structured data, or JSON-LD — or right after new content is drafted or published. Produces an actionable link checklist (with varied anchors) and validated per-node schema files. Triggers on linking/schema intent broadly.
Turn the map into a link graph and structured data. Both outputs are deterministic and carry the framework's fact discipline: laterals are justified (not sprayed), anchors are varied (not exact-match spam), and schema never states an ungrounded fact.
Read first: `../../framework/internal-linking-rules.md`, `../../framework/schema-jsonld.md`, `../../framework/eeat-signals.md`.
1. **Internal-linking plan:**
python ../../scripts/linking_plan.py --map brands/<slug>/topical-map.json \
[--vecs brands/<slug>/data/embeddings/map.npz --lateral-threshold 0.72] \
[--crawl-dir brands/<slug>/data/crawl] --brand "<Brand>" \
--out brands/<slug>/audits/linking-plan.mdFor each node it emits up/down/justified-lateral links with varied, descriptive anchors drawn from the target's query network, plus link-health flags (planned-graph orphans, over-linked hubs, and real crawl orphans if crawl data is present). Laterals below the embedding threshold are marked REVIEW rather than dropped or trusted.
2. **JSON-LD schema:**
python ../../scripts/schema_jsonld.py --map brands/<slug>/topical-map.json \
--entity-profile brands/<slug>/entity-profile.json \
--locked brands/<slug>/locked-facts.json \
--out-dir brands/<slug>/data/schemaEmits one `.jsonld` per node (HowTo / FAQPage / Product / Article), a site-level `Organization` graph, and a `checklist.md`. Product `Offer` prices come ONLY from locked facts; FAQ/HowTo answer text is left as a placeholder to be filled from the *validated* draft — so no invented answer ships in markup.
3. **Hand off:** give the user the link checklist (page → insert link → suggested anchor → where) and the schema files. Note that syntactic validation here is necessary but not sufficient — run Google's Rich Results Test before publishing.
(`asserted`). Schema from map + profile.
(eeat-signals.md) — never fabricated to fill the template.
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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