# Marketing SEO Research

> Research SEO keywords and search metrics for a topic (DataForSEO with AI research fallback), then produce an SEO context block and a target keyword for content. Use when the user wants keyword research, SEO data, or a target keyword for a content idea or draft. Read firm profile for industry/location.

- Skill: `b2bforce/marketing-seo-research` (Agent Skill)
- Install (CLI): `npx skillmds@latest add b2bforce/marketing-seo-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/b2bforce/marketing-seo-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- License: MIT
- Author: b2bforce (https://skillmd.com/u/b2bforce)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/b2bforce/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`.
2. **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):

```text
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:

```text
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`:

```yaml
---
topic:
location:
source: dataforseo | ai | dry-run
primary_keyword:
search_volume:
competition:
suggestions: []
answer_engine_prompts: []   # prompt slugs from ai-visibility/!_prompts.md
date: 2026-06-01
---
```

`answer_engine_prompts` links this research to the panel in
`workspace/intelligence/ai-visibility/!_prompts.md`. Buyers increasingly ask the
question rather than searching the keyword, and only about a tenth of what answer
engines cite sits in the top 10 organic results — so a keyword can look healthy while
the firm is absent from the answer built on the same intent.

Fill it by matching this topic's buyer intent to existing prompt slugs. Do not create
prompts here; that is `intel-ai-visibility`, and a panel edited from two places stops
being comparable across batches.

## 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

```bash
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 |
| `intel-ai-visibility` | The same buyer intent measured in answer engines instead of SERPs |
| `firm-context` | Industry + target location |

## Scope

This skill covers classic search. It is still worth running — but it measures one
channel, and `answer_engine_prompts` exists so the file says which. Do not stretch
keyword volume into a claim about AI visibility; they are different measurements with
little overlap.