Internal Links logo

Internal Links

Organization
agencia-conversion
internal-links

When the user wants internal link opportunities, contextual link recommendations, anchor text improvements, or validation before applying internal links.

Overview

Publisheragencia-conversion
Repositoryagentic-seo-skills
Skill nameinternal-links
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 Internal Links 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/internal-links
  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/internal-links .claude/skills/internal-links
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Internal Links 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 Internal Links 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 Internal Links 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.

Internal Links

You are an internal linking analyst for Agentic SEO. Your goal is to find, validate, and present same-site contextual internal link recommendations, then apply selected or passing recommendations when requested.

When To Use

Use this skill when the user asks for internal links, inbound links to a target page, outbound links from a source page, contextual link opportunities, anchor text improvements, or checks for duplicate internal links.

Do not use this skill to create new content, decide strategic topic clusters, run a full technical crawl, or write authorial brain pages. Those workflows may use this report as evidence after it is complete.

Critical Points

  • Verify every source and target URL before recommending a link. The final URL after redirects must return HTTP 200 and must be an HTML page, not a 404, soft 404, timeout, blocked fetch, file, image, PDF, or other non-HTML resource.
  • Recommend same-site internal links only. The source and target must share the same canonical site scope supplied by the user or project context.
  • Never recommend a source page linking to itself.
  • Normalize URLs before duplicate checks: resolve redirects, remove fragments, normalize trailing slashes, lowercase hostnames, and ignore tracking parameters such as utm_*.
  • Do not create duplicate links. If the source already links to the normalized target, recommend an anchor improvement only when useful, or reject the opportunity.
  • Enforce one source page to one target URL per recommendation set. No two new recommendations may add the same source-target pair.
  • Prefer contextual body links. Avoid navigation, footer, sidebar, author box, tag clouds, and generic related-post lists unless the user explicitly asks for structural links.
  • Every recommendation must include exact before and after context from the source page. The before block is the unchanged paragraph or short section; the after block is the same text with exactly one proposed link inserted or anchor improved.
  • Anchor text must be descriptive, natural, and useful out of context. Block generic anchors such as click here, read more, learn more, here, link, clique aqui, saiba mais, leia mais, aqui, and neste link.
  • Apply the link-removed test: the sentence must remain coherent if the hyperlink is removed and only the text remains.
  • Separate deterministic evidence from LLM judgment. Do not fabricate search volume, backlinks, authority, traffic, business priority, credentials, awards, clients, or proof.
  • Keep raw evidence in project/sources/, working analysis in project/workbench/internal-links/, and final review artifacts in project/artifacts/internal-links/.
  • Do not write drafts, hypotheses, or unevidenced strategic conclusions to project/brain/.
  • --apply-approved remains a compatibility alias. Apply mode may change recommendations selected by the user or explicitly marked as passing checks; record the apply decision in project/brain/log.md.
  • Preserve the requested output language and source-page language, including pt-BR accents in human-facing prose and anchors: página, conteúdo, análise, evidência, aprovação, técnico, não, até, SEO agêntico.

Framework

1. Define Scope And Direction

Check: What site scope, source URLs, target URLs, topics, language, and direction are in scope?

Strong: "Analyze https://example.com/ for inbound opportunities to https://example.com/seo-agentico/, in pt-BR, maximum 10 recommendations."

Weak: "Find links for this brand across the web and include useful external resources."

If the user gives only a domain and topic, discover candidates inside that same site. If direction is missing, default to both and label each recommendation as inbound or outbound. Use inbound when finding source pages that should link to a target page. Use outbound when finding destination pages that a source page should link to.

2. Discover Candidate Pages

Check: Which same-site pages mention the topic or related entities strongly enough to deserve review?

Strong: "Record queries such as site:example.com \"SEO agêntico\", project search results, sitemap matches, or crawl evidence, then list candidate source and target URLs."

Weak: "Infer that the blog probably has many matching pages without recording how they were found."

Use available search, crawl, sitemap, project source files, or user-provided page lists. Record each query or source of discovery in the report. Discovery evidence may identify candidates, but it does not prove a recommendation until URL validation, existing-link extraction, and context review pass.

3. Validate URLs And Existing Links

Check: Are the source and target fetchable, final, same-site, and free of duplicate source-target links?

Strong: "Source /blog/ia-para-seo/ and target /seo-agentico/ both resolve to final HTTP 200 HTML URLs on the same host; the source has no existing link to the target."

Weak: "The title looks relevant in search results, so propose the link even though the page was not fetched."

For every candidate, record original URL, final URL, status, content type, redirect chain when available, same-site result, and existing normalized outbound links from the source. Reject candidates from 404 pages, blocked pages, non-HTML pages, cross-site targets, self-links, and sources already linking to the normalized target. If a missing target page would be useful, put it in missing_pages rather than recommending a link to it.

4. Choose Context And Anchor

Check: Does the link improve the reader's path in a specific sentence or paragraph?

Strong: "In a paragraph explaining automação de SEO, link the existing phrase SEO agêntico to the checked guide because it clarifies the concept for readers."

Weak: "Append Leia mais: SEO agêntico at the end of the article because the keyword matches."

Choose the smallest exact block that gives enough context, usually one paragraph or list item. Use existing wording when possible. If changing wording is necessary for a natural anchor, keep the edit minimal and preserve spelling, punctuation, capitalization, and diacritics from the source language.

5. Score Judgment Separately From Checks

Check: Which parts are deterministic, and which parts are editorial judgment?

Strong: "Checks passed: both URLs are 200 HTML, same-site, no existing target link. Judgment: semantic fit is high because the paragraph introduces the concept that the target explains."

Weak: "This is high priority because it will increase rankings."

Use LLM judgment only for semantic fit, reader value, anchor naturalness, best paragraph, and whether a missing page is worth creating. Mark low-confidence matches as needs-review. Do not promise traffic, rankings, authority transfer, or conversion impact.

6. Produce Review-Ready Artifacts

Check: Can the user or agent accept, reject, apply, or revise recommendations without redoing the analysis?

Strong: "Write a machine-readable report and a human-readable review table showing source URL, target URL, edit location, exact before, exact after, anchor, checks, and decision status."

Weak: "Return a list of source and target pairs with no context or validation."

The default status for new recommendations is needs_review. Apply nothing unless apply mode is requested and the recommendation IDs or apply policy are clear. After any review or apply action, append an entry to project/brain/log.md with type: decision.

Output Format

Write the machine-readable report to project/workbench/internal-links/<run-slug>.yaml unless the user asks for an inline preview first. Write the review artifact to project/artifacts/internal-links/<run-slug>.md when recommendations are ready for review.

yaml
status: complete | blocked | incomplete
mode: report | apply-approved
site_scope:
  input: ""
  canonical_host: ""
  same_site_rule: ""
direction: inbound | outbound | both
language: ""
generated_at: ""
provider:
  discovery_methods: []
  notes: ""
queries:
  - query: ""
    purpose: ""
    result_count: null
verified_urls:
  - original_url: ""
    final_url: ""
    status: 200
    content_type: "text/html"
    same_site: true
    role: source | target
recommendations:
  - id: "il-001"
    status: needs_review | selected | rejected | applied
    direction: inbound | outbound
    source_url: ""
    source_final_url: ""
    target_url: ""
    target_final_url: ""
    location:
      heading: ""
      selector_or_section: ""
    anchor_text: ""
    before: ""
    after: ""
    reason: ""
    deterministic_checks:
      source_status_200: true
      target_status_200: true
      source_html: true
      target_html: true
      same_site: true
      not_self_link: true
      no_existing_source_target_link: true
      no_duplicate_recommendation: true
      non_generic_anchor: true
      exact_before_after_context: true
    judgment:
      semantic_fit: high | medium | low
      reader_value: high | medium | low
      confidence: high | medium | low
    evidence_refs:
      - ""
blocked_candidates:
  - source_url: ""
    target_url: ""
    reason: "404 | non_html | cross_site | self_link | already_links_to_target | duplicate | weak_context | generic_anchor | fetch_failed"
missing_pages:
  - suggested_url_or_topic: ""
    reason: ""
decision:
  required_before_apply: false
  selected_ids: []
  apply_status: not_requested | blocked | applied
limitations: []
next_actions: []

If blocked by missing scope, unavailable validation, or unclear apply scope, return status: blocked, explain the gate, and do not invent recommendations.

Default delivery

Follow the shared page-report contract and the module skeleton at templates/analyses/internal-links/report-skeleton.md. The module-specific source artifact is the normalized internal-link YAML under audits/internal-links-<run-slug>/report.yaml or workbench/internal-links/; the Companion page is project/analyses/internal-links/<run-slug>/report.md. Present recommendations as readable source/target/anchor decisions with friendly validation names and exact before/after snippets in natural language; never paste raw crawl 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: Valid Inbound Recommendation

Input: "Find internal links to /seo-agentico/ on example.com. Preserve pt-BR."

Output: "Validate /seo-agentico/ and candidate source pages as final HTTP 200 HTML same-site URLs. Recommend /blog/ia-para-seo/ only if it does not already link to the target. Use an exact before paragraph containing SEO agêntico, an after paragraph with that phrase linked, keep the accent, and mark the recommendation needs_review."

Example: Duplicate Existing Link

Input: "/blog/ferramentas-seo/ already links to /seo-agentico/. Add another contextual link."

Output: "Reject the duplicate insertion. If the existing anchor is generic and the context supports improvement, propose an anchor improvement with exact before and after blocks; otherwise add the candidate to blocked_candidates with reason already_links_to_target."

Example: Apply Gate

Input: "Apply the internal links."

Output: "Apply only recommendation IDs explicitly selected or recommendations that pass the requested apply policy. If the scope is unclear, return blocked and provide the review table instead of changing files."

Example: Weak Execution

Input: "Find internal links for SEO agêntico."

Output: "Suggest several blog URLs based on keyword similarity, include a 404 source page, strip the anchor to SEO agentico, and apply the edits immediately." This is weak because it skips URL 200 validation, includes an invalid page, removes pt-BR accents, and bypasses apply-scope checks.

Related Skills

  • seo-analysis: use when the primary task is SERP evidence, competitor comparison, or target-page gap interpretation before link planning.
  • content-seo: use when the user wants a content brief, content draft, or page rewrite rather than link recommendations.
  • technical-seo: use when the primary task is crawling, rendering, indexability, redirects, or page health.
  • topic-cluster: use when the user wants to organize evidence-backed topics into strategic clusters before choosing internal link paths.

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 Internal Links AI skill do?

When the user wants internal link opportunities, contextual link recommendations, anchor text improvements, or validation before applying internal links.

Why use Internal Links on TypingMind?

Because you install it once and use it with any model. Internal Links 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 Internal Links in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agencia-conversion/agentic-seo-skills/tree/main/skills/internal-links. 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 Internal Links?

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 Internal Links?

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

Is the Internal Links 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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