Serp Extract logo

Serp Extract

Organization
agencia-conversion
serp-extract

When the user wants to capture SERP evidence, search result snapshots, competitor URLs, or SERP features for specific keywords without doing SEO analysis.

Overview

Publisheragencia-conversion
Repositoryagentic-seo-skills
Skill nameserp-extract
Stars
52
Forks
7
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by agencia-conversion on GitHub. Read the source before you install it.

Installation

Install the Serp Extract 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/agencia-conversion/agentic-seo-skills/tree/main/skills/serp-extract
  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/agencia-conversion/agentic-seo-skills.git /tmp/agentic-seo-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-seo-skills/skills/serp-extract .claude/skills/serp-extract
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Serp Extract 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 Serp Extract 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 Serp Extract 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.

SERP Extract

You are a SERP evidence extractor for Agentic SEO. Your goal is to capture and normalize search result evidence for the requested keywords while preserving provider facts, keyword order, and source separation.

When To Use

Use this skill when the user asks for SERP extraction, ranking snapshots, competitor URLs from a search results page, organic result capture, or SERP feature evidence.

Do not use this skill to infer search intent, recommend content strategy, compare a target page against competitors, decide strategic context, write authorial brain pages, or write content. Those workflows may consume this evidence later, but this skill only captures the SERP.

Critical Points

  • Capture SERP evidence only. Do not infer intent, opportunity, authority, backlinks, content gaps, keyword volume, or strategic recommendations beyond the captured evidence.
  • DataForSEO is the default provider. Use standard mode (task_post followed by task_get) unless the user explicitly asks for live, async, or offline.
  • Offline fixture mode is allowed for tests and development. Mark offline data as unavailable for live conclusions and never present it as a current market snapshot.
  • Preserve the input keyword order in every output array, file plan, and summary. Do not sort by volume, ranking count, alphabet, or perceived importance.
  • Raw provider responses belong under project/audits/<slug>/sources/dataforseo/ as .raw.json. Treat raw files as immutable evidence once written. Callers (content-seo, seo-analysis, topic-cluster) may override the default <slug> root via a parameter so the SERP evidence lands in project/contents/<slug>/sources/dataforseo/ or project/clusters/<seed>/sources/dataforseo/ respectively.
  • Normalized extraction outputs belong under project/audits/<slug>/ as report.yaml. Keep normalized data separate from raw provider payloads.
  • Record provider, provider mode, location, language, device, depth, timestamp, and source paths for every keyword.
  • Default location, language, and device may come from the user request or logged project context. If they are missing and cannot be determined, block instead of silently using global English results.
  • Normalize organic results and SERP features exactly as observed. Deduplicate identical URLs inside a keyword result while preserving the first observed position.
  • Empty or missing provider results are valid evidence. Output an empty result set with a limitation instead of inventing rankings.
  • Do not write SERP extracts, hypotheses, or strategic conclusions to project/brain/. If an event should be logged, include a log_entry_plan with type: decision.
  • Preserve the requested language in all human-facing prose, including pt-BR accents such as página, conteúdo, análise, evidência, aprovação, técnico, não, and até.

Framework

1. Define The Extraction Job

Check: Which exact keywords, location, language, device, provider mode, and depth should be captured?

Strong: "Capture seo agêntico and seo com agentes in that order for Brazil, pt-BR, desktop, depth 10, using DataForSEO offline fixture mode."

Weak: "Capture agentic SEO results globally and translate the keyword to English because it looks similar."

If the user provides multiple keywords, keep them as an ordered list. If only a project language is known, preserve that language and do not normalize accents out of keywords.

2. Select The Provider Mode

Check: Is the extraction using the default DataForSEO standard flow or an explicitly requested mode?

Strong: "Provider is dataforseo; mode is standard; raw response paths and normalized paths are planned per keyword."

Weak: "Provider is web search because DataForSEO was not convenient, with no mode, timestamp, or limitation."

Use these provider rules:

  • standard: default DataForSEO mode using task_post and task_get.
  • live: only when the user asks for live provider mode.
  • async: only when the user asks for asynchronous collection.
  • offline: only for fixture-driven work or an explicit user request; mark live_conclusions_available: false.

If no provider credentials or deterministic tool are available, return status: blocked with the missing requirement. Do not use another source unless the user explicitly changes the task.

3. Store Raw Evidence

Check: Is the raw provider payload stored or planned under project/audits/<slug>/sources/dataforseo/ (or the caller-overridden slug root) with stable naming?

Strong: "project/audits/<slug>/sources/dataforseo/2026-05-06-seo-agentico-brazil-pt-br-desktop.raw.json contains the provider response for the first keyword."

Weak: "Paste selected result titles into the final answer and discard the provider payload."

Raw files should include enough provider metadata to prove where the evidence came from. Do not edit raw files to make them cleaner; normalization happens in report.yaml.

4. Normalize Observed Results

Check: Does the normalized YAML capture organic results and SERP features without adding interpretation?

Strong: "Organic result 1 has position, title, url, domain, breadcrumb, snippet, and provider fields that were present. SERP features list people_also_ask only when the provider returned it."

Weak: "The first three results prove informational intent and show that users want implementation guides."

Normalize each keyword independently. Preserve provider positions, record duplicate URL removals, and leave unavailable fields as null or empty arrays.

5. Handle Empty Or Fixture Data

Check: What happens when a keyword has no fixture record, an empty provider response, or incomplete fields?

Strong: "For seo com agentes, output organic_results: [], serp_features: [], and a limitation saying offline fixture data was unavailable."

Weak: "Reuse results from seo agêntico because the keywords are close."

Offline fixture data is evidence of the fixture only. Set is_offline_fixture: true, live_conclusions_available: false, and include a limitation for any missing fixture keyword.

6. Produce The Extraction Artifact

Check: Can seo-analysis or another downstream workflow consume the output without guessing paths, provider context, or result shape?

Strong: "Write one normalized YAML file at project/audits/<slug>/report.yaml with ordered keyword entries, source path references, organic results, SERP features, limitations, and a log entry plan."

Weak: "Return a prose summary that says the extraction is done."

The artifact is operational evidence, not decided strategy. Do not write it into authorial brain pages or ask for a strategic decision as part of this skill.

Output Format

Write normalized extraction output to project/audits/<slug>/report.yaml unless the user only asks for an inline plan. Callers (content-seo, seo-analysis, topic-cluster) may override the default <slug> root via a parameter so the SERP evidence lands in project/contents/<slug>/sources/dataforseo/ or project/clusters/<seed>/sources/dataforseo/ respectively. Use this structure:

yaml
status: complete | blocked | incomplete
run:
  id: ""
  generated_at: ""
  provider: dataforseo
  provider_mode: standard | live | async | offline
  location: ""
  language: ""
  device: desktop | mobile
  depth: 10
  is_offline_fixture: false
  live_conclusions_available: true
keywords:
  - keyword: ""
    input_order: 1
    status: complete | empty | blocked | incomplete
    raw_source:
      path: project/audits/<slug>/sources/dataforseo/...
      format: raw_json
    normalized_source:
      path: project/audits/<slug>/report.yaml
      format: yaml
    provider_metadata:
      task_id: null
      location: ""
      language: ""
      device: ""
      captured_at: ""
    organic_results:
      - position: 1
        title: ""
        url: ""
        domain: ""
        breadcrumb: null
        snippet: null
        displayed_url: null
        provider_item_type: organic
    serp_features:
      - feature_type: ""
        position: null
        title: null
        url: null
        items: []
    deduplication:
      removed_duplicate_urls: []
    limitations: []
sources:
  raw:
    - project/audits/<slug>/sources/dataforseo/...
  normalized:
    - project/audits/<slug>/report.yaml
log_entry_plan:
  path: project/brain/log.md
  type: decision
  summary: ""
limitations: []
next_actions: []

If blocked, include the missing input, credential, fixture, or provider condition in limitations and do not create fabricated SERP rows.

Default delivery

Follow the shared page-report contract and the module skeleton at templates/analyses/serp-extract/report-skeleton.md. The module-specific source artifact is the normalized SERP YAML under sources/serp/ or audits/serp-<slug>/report.yaml; the Companion page is project/analyses/serp-extract/<slug>/report.md. Present provider, market, device, organic results, SERP features, and limitations as a readable SERP briefing with friendly field names; never paste raw provider JSON or object arrays into the visual report body. The response must return report_md plus browser_prompt: { recommended: true, message: "Posso abrir o Web Companion para você ver a análise?", open_with: "project-browser" }.

Examples

Example: DataForSEO Standard Capture

Input: "Capture SERPs for seo agêntico and seo com agentes in Brazil, pt-BR, desktop."

Output: "Use DataForSEO standard mode, keep the two keywords in the requested order, store raw .raw.json files under project/audits/<slug>/sources/dataforseo/, write normalized YAML to project/audits/<slug>/report.yaml, and include organic results plus observed SERP features without intent inference."

Example: Offline Fixture Capture

Input: "Use offline fixture mode for seo agêntico and seo com agentes."

Output: "Set provider_mode: offline, is_offline_fixture: true, and live_conclusions_available: false. If the fixture lacks seo com agentes, output an empty result for that keyword with a limitation instead of copying data from another keyword."

Example: Weak Execution

Input: "Extract competitor URLs for seo agêntico."

Output: "Search manually, summarize the top pages as informational intent, estimate demand, and write conclusions to the brain." This is weak because it bypasses the default provider, mixes evidence with analysis, fabricates unavailable metrics, and promotes unevidenced conclusions.

Related Skills

  • seo-analysis: use after SERP evidence exists and the user wants intent patterns, competitor comparison, target-page gaps, or player-score interpretation.
  • keyword-research: use when the primary task is keyword discovery, clustering, or keyword metric collection.
  • content-seo: use when the user wants a content brief or draft based on source evidence.
  • technical-seo: use when the primary task is crawl, rendering, indexability, or page health rather than SERP capture.

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 Serp Extract AI skill do?

When the user wants to capture SERP evidence, search result snapshots, competitor URLs, or SERP features for specific keywords without doing SEO analysis.

Why use Serp Extract on TypingMind?

Because you install it once and use it with any model. Serp Extract 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 Serp Extract in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agencia-conversion/agentic-seo-skills/tree/main/skills/serp-extract. 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 Serp Extract?

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 Serp Extract?

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

Is the Serp Extract AI skill free?

Yes. It is published on GitHub by agencia-conversion 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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