seo-keywords

seo-keywords

Discover the phrasings people actually use, starting from a seed. seo-opportunity ranks queries a site ALREADY has console data for; this skill is the other half — it finds queries the site has no data for yet, which is where a new page or a new site has nothing to rank.

Deterministic and stdlib-only: it does not fetch, call models, or persist.

The split, and why the agent has to do the harvesting

A Python skill cannot reach the network. So the AGENT harvests and this skill processes. That division is the whole design — do not ask this skill for data it cannot get, and do not skip the processing because the raw lists "look fine": a pool of near-duplicate strings across three surfaces is not research.

When to use

• The user asks what to write, what to rank for, or which keywords to target.
• A site or page is new, or seo-opportunity returned mostly inferred / Estimated rows because no Search Console data exists.

When NOT to use

• Ranking queries the site already has impressions for → seo-opportunity.
• Discovering volume or difficulty. Nothing here measures those. If you HAVE them from a source that does, pass them in (below) — but this skill never estimates them, and no amount of scoring substitutes for them.

How to call

1. Harvest with web_search, one call per surface per seed. Record each returned phrase with the surface it came from:

   • autocomplete — prefix suggestions
   • related — related searches
   • paa — People Also Ask questions

2. Process:

echo '{"seeds":["ai agent desktop app"],"brand":"Orkas","domain":"orkas.ai","harvested":[{"query":"best ai agent tools","source":"autocomplete"},{"query":"best ai agent tools for startups","source":"paa"}]}' | "$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" seo-keywords keywords -- --op expand

→ { ok, data: { summary, clusters:[{head, dominant_intent, size, score, keywords:[{query,intent,sources,score,score_reasons,data_tier,metrics?}]}], rejected:[{query,reason}], data_tier, note } }