openseo-keyword-research

openseo-keyword-research is a skill for Claude Code from MoizIbnYousaf/marketing-cli. It costs 124 tokens per session (1,379 once invoked), scanned A, original, MIT.

A measured keyword-research method that turns topic ideas into a prioritized plan using search volume, ranking difficulty, advertising cost, intent, trends, and Search Console data. Search Console is Google’s service for seeing how a site appears in search.

In plain words
What is it for?
Finding low-hanging search opportunities, expanding seed topics, checking existing rankings, adding keyword metrics, and writing the results to a keyword plan.
Why use it?
It separates plausible keyword ideas from opportunities supported by search and ranking data, while keeping them aligned with the business.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the marketing-cli plugin — 88 skills, 9 commands, 1 hook, 2 MCP servers shipped together

Good fit Finding low-hanging search opportunities, expanding seed topics, checking existing rankings, adding keyword metrics, and writing the results to a keyword plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/moizibnyousaf/marketing-cli/openseo-keyword-research
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add MoizIbnYousaf/marketing-cli --skill openseo-keyword-research
Clone the repo
git clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cli

Made for: Claude Code.

Or install marketing-cli, the plugin that ships this one along with the rest of its 88 skills, 9 commands, 1 hook, 2 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for openseo-keyword-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/openseo-keyword-research/github.svg)](https://agentmods.dev/skills/moizibnyousaf/marketing-cli/openseo-keyword-research)
Your own site
<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/openseo-keyword-research"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/openseo-keyword-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for openseo-keyword-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/openseo-keyword-research"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/openseo-keyword-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,379 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 69
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
  • medium Excessive Agency · line 78
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.01379
Opus 5 $0.00062 $0.00690
Sonnet 5 $0.00025 $0.00276
Haiku 4.5 $0.00012 $0.00138

Measured 11d ago against content hash 959f9c176075, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

openseo-keyword-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/openseo-keyword-research/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OpenSEO Keyword Research

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. Catalog check: mktg catalog info openseo --json --fields configured — if not configured, stop spending: state the gap and hand off to Exa-backed keyword-research with metrics marked unknown.
  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: ONLY after explicit user 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.

Read the full file on GitHub · 100 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 100 lines · 124 tokens per session scan A 959f9c176075

Subscribe to this mod's changes

openseo-keyword-research is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 25d ago), licensed MIT. It adds 124 tokens to every session and 1,379 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

citedy-content-writer

From topic to published blog post in one conversation — generate SEO- and GEO-optimized articles with AI illustrations and voice-over in 55 languages, create social media adaptations for 9 platforms, set up automated content sessions, and manage product knowledge base. End-to-end blog autopilot. Powered by Citedy.

citedy/adclaw · 70 tokens

citedy-trend-scout

Find what your audience is searching for right now — scout X/Twitter and Reddit for trending topics, discover and deep-analyze competitors, and find content gaps. Combine social signals with SEO intelligence. Powered by Citedy.

citedy/adclaw · 53 tokens

launch-tier-planner

Use when the user asks to "plan my launch tier", "how big should this launch be", or "build a launch risk register with kill criteria"; produces a tier decision (Tier 1 flagship all-channel / Tier 2 targeted / Tier 3 changelog-level), a launch-type declaration (new-product / feature / relaunch / partnership with…

aaron-he-zhu/aaron-marketing-skills · 181 tokens

narrative-drift-monitor

Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates)…

aaron-he-zhu/aaron-marketing-skills · 216 tokens

advocacy-program-designer

Use when the user asks to "design an employee advocacy program", "set up founder-led sharing", or "build a share kit for the team"; produces an advocacy program blueprint in two modes — participation-driven opt-in (default) or top-down assigned with its coercion and authenticity risks flagged — with a voluntary opt-in…

aaron-he-zhu/aaron-marketing-skills · 167 tokens

engagement-inbox-manager

Use when the user asks to "triage our comments, DMs, and mentions", "draft replies to this thread", "can we repost this fan post", or "set up inbox SLAs and an escalation path"; produces a ranked triage queue with register detection (sincere / ironic / performative / parasocial, sentiment-inversion table included …

aaron-he-zhu/aaron-marketing-skills · 216 tokens