Skills seo-dataforseo

SEO keyword research using the DataForSEO API. Perform keyword analysis, YouTube keyword research, competitor analysis, SERP analysis, and trend tracking. Use when the user asks to: research keywords, analyze search volume/CPC/competition, find keyword suggestions, check keyword difficulty, analyze competitors, get trending topics, do YouTube SEO research, or optimize landing page keywords. Requires a DataForSEO API account and credentials in .env file.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/adamkristopher/seo-dataforseo" ~/.claude/skills/clawdbot-skills-seo-dataforseo && rm -rf "$T"

manifest: skills/adamkristopher/seo-dataforseo/SKILL.md

source content

SEO Keyword Research (DataForSEO)

Setup

Install dependencies:

pip install -r scripts/requirements.txt

Configure credentials by creating a

.env

file in the project root:

DATAFORSEO_LOGIN=your_email@example.com
DATAFORSEO_PASSWORD=your_api_password

Get credentials from: https://app.dataforseo.com/api-access

Quick Start

User says	Function to call
"Research keywords for [topic]"	`keyword_research("topic")`
"YouTube keyword data for [idea]"	`youtube_keyword_research("idea")`
"Analyze competitor [domain.com]"	`competitor_analysis("domain.com")`
"What's trending?"	`trending_topics()`
"Keyword analysis for [list]"	`full_keyword_analysis(["kw1", "kw2"])`
"Landing page keywords for [topic]"	`landing_page_keyword_research(["kw1"], "competitor.com")`

Execute functions by importing from

scripts/main.py

import sys
from pathlib import Path
sys.path.insert(0, str(Path("scripts")))
from main import *

result = keyword_research("AI website builders")

Workflow Pattern

Every research task follows three phases:

1. Research

Run API functions. Each function call hits the DataForSEO API and returns structured data.

2. Auto-Save

All results automatically save as timestamped JSON files to

results/{category}/

. File naming pattern:

YYYYMMDD_HHMMSS__operation__keyword__extra_info.json

3. Summarize

After research, read the saved JSON files and create a markdown summary in

results/summary/

with data tables, ranked opportunities, and strategic recommendations.

High-Level Functions

These are the primary functions in

scripts/main.py

. Each orchestrates multiple API calls for a complete research workflow.

Function	Purpose	What it gathers
`keyword_research(keyword)`	Single keyword deep-dive	Overview, suggestions, related keywords, difficulty
`youtube_keyword_research(keyword)`	YouTube content research	Overview, suggestions, YouTube SERP rankings, YouTube trends
`landing_page_keyword_research(keywords, competitor_domain)`	Landing page SEO	Overview, intent, difficulty, SERP analysis, competitor keywords
`full_keyword_analysis(keywords)`	Strategic content planning	Overview, difficulty, intent, keyword ideas, historical volume, Google Trends
`competitor_analysis(domain, keywords)`	Competitor intelligence	Domain keywords, Google Ads keywords, competitor domains
`trending_topics(location_name)`	Current trends	Currently trending searches

Parameters

All functions accept an optional

location_name

parameter (default: "United States"). Most functions also have boolean flags to skip specific sub-analyses (e.g.,

include_suggestions=False

Individual API Functions

For granular control, import specific functions from the API modules. See references/api-reference.md for the complete list of 25 API functions with parameters, limits, and examples.

Results Storage

Results auto-save to

results/

with this structure:

results/
├── keywords_data/    # Search volume, CPC, competition
├── labs/             # Suggestions, difficulty, intent
├── serp/             # Google/YouTube rankings
├── trends/           # Google Trends data
└── summary/          # Human-readable markdown summaries

Managing Results

from core.storage import list_results, load_result, get_latest_result

# List recent results
files = list_results(category="labs", limit=10)

# Load a specific result
data = load_result(files[0])

# Get most recent result for an operation
latest = get_latest_result(category="labs", operation="keyword_suggestions")

Utility Functions

from main import get_recent_results, load_latest

# List recent files across all categories
files = get_recent_results(limit=10)

# Load latest result for a category
data = load_latest("labs", "keyword_suggestions")

Creating Summaries

After running research, create a markdown summary document in

results/summary/

. Include:

Data tables with volumes, CPC, competition, difficulty
Ranked lists of opportunities (sorted by volume or opportunity score)
SERP analysis showing what currently ranks
Recommendations for content strategy, titles, tags

Name the summary file descriptively (e.g.,

results/summary/ai-tools-keyword-research.md

Tips

Be specific — "Get keyword suggestions for 'AI website builders'" works better than "research AI stuff"
Request summaries — Always create a summary document after research, named specifically
Batch related keywords — Pass multiple related keywords at once for comparison
Specify the goal — "for a YouTube video" vs "for a landing page" changes which data matters most
Ask for competition analysis — "Show me what videos are ranking" helps identify content gaps

Defaults

Location: United States (code 2840)
Language: English
API Limits: 700 keywords for volume/overview, 1000 for difficulty/intent, 5 for trends, 200 for keyword ideas