Keyword researcher
Free, working Claude skills I use to run an AI automation studio. Drop-in SKILL.md files. By Robin Laires / Laires Labs.
npx -y skills add robdasi/skills --skill keyword-researcherAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Turn a topic into a keyword decision, title-first. Picks the main keyword that already sits in the title so the title never gets rewritten, ranks candidates by a strict priority (title match over volume, always), and builds a deliberate spread of secondary keywords by intent, returning none rather than forcing irrelevant ones. Run after brief-builder, before competitor-analyzer. Produces a keyword brief and stops.
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SKILL.md
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Keyword Researcher
A keyword on its own tells you nothing, and chasing volume quietly wrecks good titles. This skill makes the keyword decision the way it should be made: protect the title, match real intent, and refuse to bolt on keywords that don't fit the business just because they have traffic.
Run after brief-builder (load the site brief), before competitor-analyzer.
Inputs (ask for whatever is missing)
- The title or topic (required).
- The site brief from
brief-builder(so relevance is judged against the actual business). - Optional: a keyword tool (volume, difficulty). Work from live search if there isn't one.
The method
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Protect the title. The job is to find a main keyword that already appears in the title, so the title doesn't have to be rewritten to fit SEO. Start from the noun phrases in the title, not from a blank keyword tool.
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Rank candidates by a strict priority, in this order:
- Exact title match (the phrase is in the title), highest, always.
- Semantic match (all the words appear in the title).
- Volume, a sweet spot of roughly 500 to 10,000, but never sacrifice a title match for more volume.
- Competition, only as a tiebreaker between otherwise-equal options. Volume and competition are tiebreakers, never gates. Treat spelling variants (ecommerce / e-commerce) as the same.
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Build secondary keywords as an intent spread. Target around four, and choose them deliberately across intents: one informational, one commercial, one long-tail question, one use-case ("for agencies", "for beginners"). Variety is the point, not more of the same.
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Refuse irrelevant keywords. Judge every secondary against the business from the brief. If the suggestions are off-topic, return an empty set rather than force them in. A keyword that contains your term but belongs to a film or an event is still wrong.
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Cascade when the tool is dry. Keyword suggestions first, then broader keyword ideas, then generate from the title with the model. Never end with nothing because one source came back empty.
Output
Produce a keyword-brief.md: the main keyword and why it won (which priority rule), its intent, volume and competition if known (as context, not justification), the secondary keywords tagged by intent, and the target / skip call.
Then stop. The next skill reads the live SERP against this.