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semantic-core-architect

semantic-core-architect

github.com/sergekostenchukMarketing & growth3250 installsUpdated 18w ago
Repository

About semantic-core-architect

A marketing skill that turns site goals and audience information into reusable semantic-core artifacts before URL architecture, linking, or content planning. It maps intent-based query clusters, entities, topics, priorities, evidence gaps, and assumptions without making final URL decisions.

Key features

  1. 01Intent-based query clustering
  2. 02Audience and job mapping
  3. 03Entity and topic mapping
  4. 04Evidence and data-gap labeling
  5. 05Priority assignment and handoff

Use cases

  1. 01Building a semantic core for a site
  2. 02Mapping audiences to search intents
  3. 03Preparing inputs for URL architecture

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sergekostenchuk/ui-ux-agent-skill-system --skill semantic-core-architect

After installing, type/semantic-core-architectin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

evals.json
agents/
openai.yaml
assets/
entity-topic-map.template.yaml
semantic-core.template.yaml
references/
semantic-core-rubric.md

Details

From the project README

Semantic Core Architect

Use this skill before URL architecture, internal linking, schema, content planning, or LLM-friendly page design. It turns a site goal into reusable semantic artifacts.

Read references/semantic-core-rubric.md before producing a full semantic core.

Owns

  • query clusters;
  • user/search intents;
  • audience and job-to-be-done mapping;
  • entity and topic mapping;
  • language and locale priority;
  • evidence labels and data gaps;
  • handoff to information architecture.

Does Not Own

  • final URL/canonical policy;
  • internal link graph;
  • schema implementation;
  • page copywriting;
  • rank guarantees;
  • external link placement.

Workflow

  1. Capture the goal, audience, markets, languages, content model, constraints, and forbidden areas.
  2. Separate observed facts, user-provided facts, inferred assumptions, and unknowns.
  3. Build query clusters by intent, not by keyword volume alone.
  4. Map entities and topics to likely canonical page candidates without deciding final URLs.
  5. Assign priority from strategic value, page feasibility, audience fit, and evidence strength.
  6. Mark volume, difficulty, competitive strength, and rank opportunity as unknown unless verified from an approved source.
  7. Produce semantic-core.yaml and entity-topic-map.yaml using the templates in assets/.
  8. Hand off to information-architecture-seo with gaps and assumptions explicit.

Evidence Rules

  • Search volume, difficulty, traffic, ranking, and assistant citation claims require current evidence.
  • If keyword tools, Search Console, rank trackers, logs, or assistant-monitoring data are unavailable, use unknown.
  • Current facts about search engines, AI crawlers, rich results, or platforms must follow the cluster freshness policy.
  • Do not use competitor pages as proof of volume unless they come with a measured source.

Priority Model

Use P0 only when a cluster is both central to the site's identity and needed by downstream architecture. Use P1 for important supporting clusters. Use P2 for useful expansion. Use P3 for backlog or speculative ideas.

Priority is not ranking probability.

Required Outputs

Create or update:

Each cluster must include intent, audience, languages, entities, queries, evidence, assumptions, unknown metrics, and downstream notes.

Validation

Before marking work complete:

  • check every cluster has intent, audience, language, priority, and at least one query or topic seed;
  • check every metric field is either evidence-backed or unknown;
  • check entity names have stable ids;
  • check no URL/canonical decision is made as final;
  • run the cluster linter after skill edits.

Validate skill edits with:

python3 $CODEX_HOME/skills/senior-skill-architect/scripts/lint_production_skill.py $HOME/SKILL/skills/semantic-core-architect
python3 $HOME/SKILL/plans/seo-llm-skill-cluster/scripts/lint_skill_cluster.py $HOME/SKILL

Forward tests for this skill live in evals.json.

Output Shape

Return:

  1. Semantic core summary.
  2. Top clusters by priority.
  3. Entity/topic map summary.
  4. Unknown metrics and evidence gaps.
  5. Handoff notes for information architecture.

Comments

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