OpenSEO Keyword Research

SkillSearch

Discover keyword opportunities with MEASURED volume, keyword difficulty, CPC, and intent from OpenSEO, then write them into brand/keyword-plan.md. Use this skill whenever someone asks for keyword difficulty, KD, search volume, keyword ideas with metrics, striking-distance opportunities from Search Console, or SERP-validated keyword priorities. For qualitative research without an OpenSEO connection, use mktg's keyword-research instead (metrics will be unknown). Triggers: "keyword difficulty", "search volume", "keyword opportunities", "striking distance keywords", "measured keyword research".

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the OpenSEO Keyword Research skill

What this skill tells your AI

The instructions your AI receives, as published by moizibnyousaf/marketing-cli in skills/openseo-keyword-research/SKILL.md and read by ahel’s review.

Turn seed topics into a prioritized, MEASURED keyword opportunity set and land it in brand/keyword-plan.md. mktg's keyword-research is the playbook (methodology); this skill is the measured-data engine behind it when OpenSEO is configured.

On Activation

  1. Readiness check: mktg seo status --json --fields readiness,catalog.endpointError,project — if readiness is not_configured, stop spending: state the gap and hand off to Exa-backed keyword-research with metrics marked unknown. For a ready state, verify live access with the free whoami MCP tool before paid calls.
  2. Project binding: read .seo/openseo.json for projectId. Missing → run openseo-project-setup first (or ask the user for the project id).
  3. Brand grounding: read brand/positioning.md + existing brand/keyword-plan.md (tolerate templates). Business-fit beats volume-fit — the positioning file is the filter.

OpenSEO MCP Tools

  • get_search_console_performance: when GSC is connected, START here. High rowLimit, filter average position ~5–20 client-side (the API sorts by clicks, not position). These striking-distance terms are the fastest wins — and zero extra credit cost.
  • get_keyword_metrics: hydrate up to 700 keywords per call with volume, KD, intent, CPC, trends. Use on striking-distance terms and every candidate set.
  • research_keywords: discovery from 1–5 seeds per call; prefer ~150 results unless exhaustive research was requested.
  • get_ranked_keywords: when the brief includes a domain/page — exact ranking rows (near-misses, competitor-owned terms).
  • get_serp_results: inspect SERPs for top candidates when intent is ambiguous. Keep batches small (≤10 queries).
  • list_saved_keywords: avoid re-researching what's already saved.
  • save_keywords: a free shared-state mutation. Use only after confirmation, with concise tags (topic:<t>, intent:<i>, page:<slug>).

Workflow

  1. Normalize seeds into 2–5 distinct research angles filtered by positioning.
  2. GSC connected? Pull striking-distance terms first and hydrate with get_keyword_metrics. Work that list before broad discovery.
  3. research_keywords per angle; get_keyword_metrics to hydrate; get_ranked_keywords if a domain is in the brief.
  4. Remove irrelevant, duplicate, branded-only, and off-intent terms.
  5. Prioritize by practical opportunity: business fit → clear intent → reasonable KD → volume/CPC signal → winnable SERP.
  6. get_serp_results for high-potential or ambiguous terms when SERP intent would change the call.
  7. Write the shortlist into brand/keyword-plan.md (preserve its required sections per brand/SCHEMA.md; confirm before overwriting populated sections).
  8. Present: best opportunity theme, top keywords now, keywords to save, SERP caveats. Then next actions: openseo-keyword-clustering, seo-content, or save.

Cost Discipline

  • State estimated call counts before bulk pulls (>200 keywords) and get confirmation.
  • Small exploratory batches are fine without asking.
  • GSC-first ordering exists precisely to avoid spending credits on data the user already owns.

Anti-Patterns

  • Inventing metrics when OpenSEO returns nothing — because a hallucinated KD of "about 35" silently becomes the foundation of a content plan. If OpenSEO doesn't return a value, write unknown.
  • Volume-first prioritization — because a 10k-volume term that doesn't match the product converts nobody and burns months. Positioning filters the list before metrics rank it.
  • save_keywords without explicit confirmation — saves do not burn provider credit, but they mutate shared OpenSEO account state and broad tags can disrupt other workflows. Ask, state the count and tag change, then save.
  • Skipping the GSC-first pass when GSC is connected — because striking-distance terms (positions 5–20) are provably the cheapest wins in SEO and they're free to read. Discovery research before first-party data is wasted spend.
  • Overwriting a populated keyword-plan.md without confirmation — because that file is brand memory other skills build on (seo-content, seo-machine). Merge; confirm destructive rewrites.

Close the loop

After writing files, log completion so mktg plan / mktg status count the work (bare mktg run only logs loaded):

mktg run openseo-keyword-research --complete --writes <paths written> --result success --json

Progressive Enhancement

LevelBehavior
L0 (no OpenSEO)Hand off to Exa-backed keyword-research; metrics unknown
L1 (OPENSEO_API_KEY)Metrics via available calls; MCP steps deferred
L2 (MCP connected)Full workflow incl. SERP validation
L3 (GSC connected)Striking-distance-first ordering; highest-signal path

Adapted from every-app/open-seo .agents/skills/keyword-research (MIT). Workflow and tool guidance upstream; mktg brand-memory writes, positioning filter, and cost discipline added here.

Signals

GitHub stars
31
Forks
5
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
openseo-keyword-research
Source
github.com/moizibnyousaf/marketing-cli