Marketing Seo Research

SEO Research

Keyword research + search metrics to enrich content ideas and drafts with a target_keyword and an SEO context block.

When to Use

• User wants keyword research or SEO data for a topic
• Picking a target_keyword for a content idea (content/ideas/{slug}.md)
• Generating an SEO context block to feed into a blog draft or service page

Read First

workspace/firm/profile.md — industry and geography/location (DataForSEO location name format, e.g. "Poland", "United States"). Default location: Poland.

Workflow

1. Keyword research (with fallback)

researchKeywords(topic, industry, location):

1. DataForSEO (preferred) — getKeywordData(keyword, location) →

{ keyword, search_volume, cpc, competition, competition_level } (default location Poland). Stored as the primary_keyword, source: dataforseo.

1. Fallback: AI research (Exa/Perplexity) when DataForSEO is unset/fails —

ask for 5 high-value B2B keywords for the topic (one per line). source: ai.

AI keyword query (verbatim shape):

Suggest 5 high-value SEO keywords for B2B content about "{topic}"
[in the {industry} industry]. Format: one keyword per line, no numbering,
just the keyword phrases.

Dry-run (no keys): propose keywords from topic + industry knowledge, mark source: dry-run.

2. Pick a target keyword

Choose the most relevant, realistic keyword (intent + achievable competition). Prefer specific long-tail over generic head terms for PSF/B2B.

3. Build SEO context block

generateSeoContext(topic, targetKeyword) → a short block for content prompts. It starts with a SEO Context: header, the target keyword, and (only when DataForSEO is available) one metrics line with monthly search volume and competition level — CPC is not included here:

SEO Context:
Target keyword: {target_keyword}
Keyword metrics: {search_volume} monthly searches, competition: {competition_level}

When the keyword research feeds idea generation, the prompt also nudges the model to "include target keywords naturally in content titles where appropriate" — it does not prescribe specific placements (title / first paragraph / H2).

4. Write outputs

• Set target_keyword: in the relevant content/ideas/{slug}.md frontmatter.
• Save full research to

workspace/marketing/seo/{topic-slug}.md:

---
topic:
location:
source: dataforseo | ai | dry-run
primary_keyword:
search_volume:
competition:
suggestions: []
date: 2026-06-01
---

Integration with content pipeline

• marketing-content-ideas can call this to attach target_keyword per idea.
• marketing-content-blog-post should weave the SEO context block into blog

drafts.

• marketing-service-page should use SEO context for standalone service pages.

LinkedIn/X do not use SEO research.

Rules

1. Always degrade gracefully: DataForSEO → AI → dry-run; never hard-fail.
2. One primary target_keyword per content piece; keep secondary as suggestions.
3. Write for humans — flag and avoid keyword stuffing.
4. Location/industry come from firm-context, not guessed per call.

Environment Variables

DATAFORSEO_LOGIN=
DATAFORSEO_PASSWORD=
EXA_API_KEY=        # or Perplexity — AI keyword fallback

Related Skills

| Skill | When | |-------|------| | marketing-content-ideas | Attach target keywords to ideas | | marketing-content-blog-post | Consume SEO context in blog drafts | | marketing-service-page | Consume SEO context in standalone service pages | | firm-context | Industry + target location |

Source & license

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

• Author: b2bforce
• Source: b2bforce/b2bforce
• License: MIT
• Homepage: https://www.b2bforce.ai/

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