content-brief-generator

name

content-brief-generator

description

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".

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.