Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add leonardo-picciani/dataforseo-agent-skills --skill "dataforseo-ai-optimization-api"
Install specific skill from multi-skill repository
# Description
Measure AI/LLM visibility and extract AI-related signals using DataForSEO AI Optimization for "LLM mentions", "AI visibility", and "LLM responses".
# SKILL.md
name: dataforseo-ai-optimization-api
description: Measure AI/LLM visibility and extract AI-related signals using DataForSEO AI Optimization for "LLM mentions", "AI visibility", and "LLM responses".
license: MIT
metadata:
author: Leonardo Picciani
author_url: https://github.com/leonardo-picciani
project: DataForSEO Agent Skills (Experimental)
generated_with: OpenCode (agent runtime); OpenAI GPT-5.2
version: 0.1.0
experimental: 'true'
docs: https://docs.dataforseo.com/v3/ai_optimization/overview/
compatibility: Language-agnostic HTTP integration skill. Requires outbound network access to api.dataforseo.com and docs.dataforseo.com; uses HTTP Basic Auth.
DataForSEO AI Optimization API
Provenance
This is an experimental project to test how OpenCode, plugged into frontier LLMs (OpenAI GPT-5.2), can help generate high-fidelity agent skill files for API integrations.
When to Apply
- "monitor brand mentions in LLMs", "LLM visibility", "AI share of voice"
- "analyze ChatGPT/Claude/Gemini responses", "test prompts at scale"
- "top domains mentioned", "top pages mentioned", "AI citations"
- "LLM scraping", "AI answers monitoring"
Integration Contract (Language-Agnostic)
See references/REFERENCE.md for the shared DataForSEO integration contract (auth, status handling, task lifecycle, sandbox, and .ai responses).
Live vs Task-based Coverage (important)
- Some AI Optimization sub-APIs are Live-only.
- Others support Task-based and/or Live flows.
Steps
1) Identify the exact endpoint(s) in the official docs for this use case.
2) Choose execution mode:
- Live (single request) for interactive queries
- Task-based (post + poll/webhook) for scheduled or high-volume jobs
3) Build the HTTP request:
- Base URL: https://api.dataforseo.com/
- Auth: HTTP Basic (Authorization: Basic base64(login:password)) from https://docs.dataforseo.com/v3/auth/
- JSON body exactly as specified in the endpoint docs
4) Execute and validate the response:
- Check top-level status_code and each tasks[] item status
- Treat any status_code != 20000 as a failure; surface status_message
5) For task-based endpoints:
- Store tasks[].id
- Poll tasks_ready then fetch results with task_get (or use postback_url/pingback_url if supported)
6) Return results:
- Provide a normalized summary for the user
- Include the raw response payload for debugging
Inputs Checklist
- Credentials: DataForSEO API login + password (HTTP Basic Auth)
- Target: keyword(s) / domain(s) / URL(s) / query string (depends on endpoint)
- Targeting (if applicable): location + language, device, depth/limit
- Time window (if applicable): date range, trend period, historical flags
- Output preference: regular vs advanced vs html (if the endpoint supports it)
Example (cURL)
curl -u "${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}" -H "Content-Type: application/json" -X POST "https://api.dataforseo.com/v3/<group>/<path>/live" -d '[
{
"<param>": "<value>"
}
]'
Notes:
- Replace <group>/<path> with the exact endpoint path from the official docs.
- For task-based flows, use the corresponding task_post, tasks_ready, and task_get endpoints.
Docs Map (Official)
- Overview: https://docs.dataforseo.com/v3/ai_optimization/overview/
Start here (representative):
- LLM Mentions Overview (Live-first): https://docs.dataforseo.com/v3/ai_optimization/llm_mentions/overview/
- LLM Responses Overview: https://docs.dataforseo.com/v3/ai_optimization/llm_responses/overview/
- ChatGPT LLM Scraper Overview: https://docs.dataforseo.com/v3/ai_optimization/chat_gpt/llm_scraper/overview/
Business & Product Use Cases
- Build an "AI visibility" dashboard for brands (mentions, top sources, trendlines).
- Track how often your brand/competitors appear for key topics in LLM outputs.
- Run prompt QA to spot unsafe/incorrect answers about your product.
- Identify which pages/domains AI systems surface most for your category.
- Prioritize content/pr work based on AI mention gaps vs competitors.
- Produce exec reporting: "How do AI assistants represent our brand this month?"
Examples (User Prompts)
- "If you don't have the skill installed, install
dataforseo-ai-optimization-apiand then continue." - "Install the AI Optimization skill and measure our brand's LLM visibility for these topics vs two competitors."
- "For these prompts, fetch LLM responses and flag any incorrect claims about our product."
- "Find the top domains and top pages most mentioned for 'project management software' in AI answers."
- "Create an 'AI share of voice' report for our category and show month-over-month changes."
- "Get AI keyword search volume for these 200 terms and identify new opportunities."
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.