Semantic Core Architect

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](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/](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:

• semantic-core.yaml, based on [assets/semantic-core.template.yaml](assets/semantic-core.template.yaml);
• entity-topic-map.yaml, based on [assets/entity-topic-map.template.yaml](assets/entity-topic-map.template.yaml);
• a gap list for unverified data and needed research.

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](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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sergekostenchuk
• Source: sergekostenchuk/ui-ux-agent-skill-system
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.