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Content brief generator

Skill siddiqss/semantic-seo-suite/skills/content-brief-generator

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-brief-generator

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Generate entity-aware, Koray-style content briefs from a topical-map node or any target query — a semantically ordered heading skeleton with entity/attribute tags, internal-link targets, snippet target, word budgets, locked-facts references, and a do-not-fabricate list. Use whenever the user asks for a content brief, an article outline, writer instructions, "brief for X", or wants to start writing an article that exists in the topical map. Runs SERP recon first. Triggers on outline/brief intent broadly, not just the word "brief".

SKILL.md

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content-brief-generator

Produce a brief a stranger writer could execute without further explanation, built on the page's contextual vector (heading order = meaning) and locked to one macro context.

Read first: ../../framework/contextual-vectors.md, ../../framework/macro-micro-semantics.md, ../../framework/query-semantics.md, ../../framework/internal-linking-rules.md.

Preconditions

  • brands/<slug>/config.yaml (tier).
  • A node in brands/<slug>/topical-map.json (or create an ad-hoc node from a query).

Workflow

  1. Load the node (target query, intent, entities, query network, internal links). If ad-hoc, first decompose the query's entity via ../../framework/eav-modeling.md.

  2. SERP recon for the target query:

    • T0: web_search + fetch the top 2–3 results; extract their heading structures and which entities/attributes they cover. Note gaps you can beat.
    • T2: ../../scripts/dataforseo_client.py live SERP + People-Also-Ask for cleaner data. Record provenance.

  3. Build the contextual vector (the outline). Order per contextual-vectors.md: definition/snippet lead → defining attributes → values/how-to → comparisons/related → question network → edge cases (macro-micro border with a grouper question). Tag each heading entity: / attr: / rel: / q:, state must_cover, set a word_budget guideline. Keep ONE macro context and one intent.

  4. Snippet target. Write the ~40-word extractive answer the lead should win.

  5. Internal links. Pull up/down/lateral from the node; add descriptive, varied anchor suggestions from the target nodes' query networks. Justify laterals (named shared attribute at T0; embedding distance at T1).

  6. Lock the facts. List locked_facts_refs (keys the article may state) and an explicit do_not_fabricate list (specs/stats/prices lacking provenance — pull the brand's _pending_owner_confirmation items into here).

  7. Intent-conflict check vs sibling nodes (query-semantics.md): flag any node with overlapping query network + same intent. T0 by judgement; T1 via ../../scripts/semantic_distance.py.

  8. Emit brands/<slug>/briefs/<node-slug>.md (a readable brief) and, if the pipeline wants structured data, a JSON alongside it validating against ../../templates/brief.schema.json. Set node status: briefed.

Definition of done

  • One macro context, one intent; outline follows the contextual vector order.
  • Every heading tagged + has must_cover; snippet target written.
  • Internal links present with varied anchors; laterals justified.
  • do_not_fabricate names the unverified brand facts explicitly.
  • Intent-conflict check run; conflicts flagged or "none".

Grounding ladder

  • T0: LLM reasoning + web_search SERP glimpse; intent/queries asserted.
  • T1: autocomplete-enriched query coverage, embedding-based conflict check + lateral justification, SERP-verified intent.
  • T2: DataForSEO live SERP + PAA questions folded into the question network.

Keep looking

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