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Dataforseo

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alinaqi
dataforseo

Keyword & SERP research via the DataForSEO API — search volume, competition, CPC, keyword ideas for naming/SEO decisions

Overview

Publisheralinaqi
Repositorymaggy
Skill namedataforseo
Stars
707
Forks
57
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by alinaqi on GitHub. Read the source before you install it.

Installation

Install the Dataforseo AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alinaqi/maggy/tree/main/skills/dataforseo
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/dataforseo .claude/skills/dataforseo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dataforseo in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dataforseo on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dataforseo is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DataForSEO Skill — keyword & SERP research

Purpose: Get real search demand (monthly volume, competition, CPC) and keyword ideas from Google Ads data via DataForSEO, so naming and content decisions are grounded in data rather than intuition.

Auth (never commit the key)

DataForSEO uses HTTP Basic auth. Provide credentials via environment only:

bash
export DATAFORSEO_LOGIN="you@example.com"
export DATAFORSEO_PASSWORD="…"
# or a single combined value:
export DATAFORSEO_API_KEY="login:password"   # or its base64

The helper reads these from the env — it never hardcodes or logs the secret, and only ever calls api.dataforseo.com. Keep the key in a local .env that is git-ignored; do not paste it into the repo, a URL, or chat.

Usage

The helper is dataforseo.py in this skill folder:

bash
DFS="$(cat ~/.claude/.bootstrap-dir)/skills/dataforseo/dataforseo.py"

python3 "$DFS" whoami                                  # auth check + balance
python3 "$DFS" volume "claude code" "coding agent"     # search volume table
python3 "$DFS" ideas  "coding agent"                   # related keyword ideas
# options: --location <code, default 2840=US>  --language <code, default en>

volume → each keyword's monthly search volume, competition (LOW/MEDIUM/HIGH), and CPC. ideas → the searched long-tail around a seed.

Reading the results (naming decisions)

  • Zero / n/a volume on every candidate is itself the answer: the names aren't search terms, so SEO can't rank them against each other — decide on clarity/brand and accept that discovery happens on GitHub/HN/X, not search.
  • Category term vs. brand term: a huge-volume category word (e.g. the tool's ecosystem name) is where demand lives; a brand name rides it only if it contains it. Weigh discoverability against being on-brand for what you built.
  • Wrong-intent volume is a trap: a candidate with existing volume may be drawing an audience searching for something else (a different industry meaning). That volume hurts, it doesn't help.
  • Competition + CPC proxy commercial intent, not just popularity — high CPC means advertisers pay for that term (buyers), low CPC often means informational or noise.

Cost & etiquette

live endpoints bill per call (search volume / ideas ≈ $0.05–0.10 each). Batch many keywords into ONE volume call (it accepts up to 1000) rather than looping. Check whoami for balance before large runs.

Common endpoints (beyond the helper)

NeedEndpoint
Search volumekeywords_data/google_ads/search_volume/live
Keyword ideaskeywords_data/google_ads/keywords_for_keywords/live
SERP (who ranks)serp/google/organic/live/advanced
Ranked keywords for a domaindataforseo_labs/google/ranked_keywords/live
Competitor domainsdataforseo_labs/google/competitors_domain/live

All take the same Basic-auth header; POST a JSON array of task objects.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dataforseo AI skill do?

Keyword & SERP research via the DataForSEO API — search volume, competition, CPC, keyword ideas for naming/SEO decisions

Why use Dataforseo on TypingMind?

Because you install it once and use it with any model. Dataforseo is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dataforseo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/dataforseo. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dataforseo?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dataforseo?

As many as you like. As long as a model supports skills, you can use Dataforseo with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dataforseo AI skill free?

Yes. It is published on GitHub by alinaqi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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