Link Prospecting logo

Link Prospecting

OrganizationPopular
every-app
link-prospecting

Find link prospects, discover contact paths, and draft outreach from SERPs and backlink signals.

Overview

Publisherevery-app
Repositoryopen-seo
Skill namelink-prospecting
Stars
22K
Forks
2.8K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by every-app on GitHub. Read the source before you install it.

Installation

Install the Link Prospecting 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/every-app/open-seo/tree/main/.agents/skills/link-prospecting
  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/every-app/open-seo.git /tmp/open-seo
mkdir -p .claude/skills
cp -r /tmp/open-seo/.agents/skills/link-prospecting .claude/skills/link-prospecting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Link Prospecting 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 Link Prospecting 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 Link Prospecting 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.

OpenSEO Link Prospecting

Goal

Find realistic pages, sites, and authors that might reference the user's page, product, study, guide, or tool. Use OpenSEO for prospect discovery, then use available web/search/browser tools for contact discovery.

Required inputs

  • projectId
  • User domain or target URL
  • Linkable asset, page, product, study, tool, or topic
  • Optional competitors
  • Optional market/location/language

Project context

The project-context tools are free and shared with the app and other agents.

  1. Call get_project_context first and ground the outreach in it — positioning supplies the claim that makes a link worth giving, and the saved competitors are the backlink profiles to mine.
  2. This skill needs positioning and competitors. If either is empty, run a minimal inline setup: ask the user why someone would cite them and who they compete with, or infer from the site and find_serp_competitors and confirm, write it back with update_project_context, then continue the prospecting. Never front-load the full interview; suggest seo-project-setup at the end for the rest.
  3. Before spending credits, check the research log. If the same research ran within the last 30 days, reuse that result and say so instead of re-buying it.
  4. On finish, write back what is durable — the linkable asset via addKeyPages, any competitor whose backlink profile proved useful via addCompetitors — and append a research log entry: { appendResearchLog: { summary: "Link prospecting: <asset/target page>. Verdict: <conclusion>" } }.

Deliver as a report

Deliver through the seo-report skill, saving with skill: "link-prospecting". If that skill is not available, say so and stop before writing HTML.

OpenSEO MCP tools

  • get_serp_results: find ranking articles, listicles, resource pages, comparisons, and topical publishers.
  • get_backlinks_overview: inspect competitor domain or page backlink/referring-domain patterns.
  • get_domain_overview: qualify important prospect domains.
  • get_ranked_keywords: understand what a prospect or competitor ranks for when topical fit matters.
  • search_local_businesses and get_local_serp_results: use for local SEO link prospecting when nearby businesses, local competitors, or Maps/category signals can reveal partnership targets.
  • research_keywords: expand prospecting queries.

Contact discovery tools

After OpenSEO identifies good prospects, use available non-OpenSEO browsing or search tools for public contact discovery. Depending on the client, this may be web search, page fetches, browser automation, or a search API.

Look for:

  • Author byline pages
  • Contact pages
  • Editorial guidelines
  • About/team pages
  • LinkedIn, X, Bluesky, or other professional profiles
  • Newsletter or publication masthead pages
  • Public email addresses in page HTML or visible page text
  • Structured data such as Person, Organization, sameAs, or email

Only record contact details that were actually found. Include the source URL for any email, profile, or contact form.

Prospecting query patterns

Build queries from the asset/topic:

  • <topic> resources
  • best <category> tools
  • <competitor> alternatives
  • <topic> statistics
  • <topic> guide
  • <topic> examples
  • <topic> templates
  • <topic> software
  • <topic> for <audience>

Use get_serp_results in batches for the most relevant patterns. Send at most 10 queries per call.

Workflow

  1. Clarify the linkable asset and the reason someone would reference it.
  2. Build 5-10 prospecting queries by default.
  3. Call get_serp_results for those queries.
  4. If competitors are provided, call get_backlinks_overview for the strongest competitor domains or pages first. Continue without backlink evidence if it is unavailable.
  5. For local SEO, use search_local_businesses and get_local_serp_results around priority locations to identify nearby competitors, categories, and local SERP evidence before searching for local chambers, associations, campus resources, community pages, and directories.
  6. Filter prospects:
    • Keep topical relevance and editorial pages.
    • Prioritize articles, directories, resource pages, comparisons, statistics pages, templates, and curated lists.
    • Deprioritize homepages, login pages, thin affiliate pages, spam, unrelated forums, and direct competitors unless a comparison angle is valid.
  7. For each good prospect, define the outreach angle:
    • Broken/missing resource
    • Better current data
    • Useful tool/template
    • Alternative or comparison inclusion
    • Expert quote or supporting reference
  8. For the strongest prospects, visit or search the prospect site to find the best contact path.
  9. Draft outreach messages. If contact details were found, include the source. If not, list the next best contact-discovery path.

Output format

h1: the linkable asset.

If a report template applies (see seo-report), its sections and tone replace this list.

Sections in this order:

  1. The angle — one or two opening sentences naming the best outreach angle and the prospect type to work first.
  2. Prospects — a table of prospect URL, site, source, suggested angle, contact path, and priority. Keep it under about eight columns; drop the ones that add nothing for this run.
  3. Why these — one finding per prospect worth explaining: the evidence that they link to things like this, then the exact ask.
  4. Outreach drafts — the message text for each of two or three reusable angles: resource or list inclusion, an article update, and a comparison mention.
  5. Limitations — notes: contact paths not found, prospects that are direct competitors or likely paid placements, and which source found each contact detail.
  6. What to do next — an ordered list: who to send to first, and in what order.
  7. How this report was made — opens with the skill link line from seo-report, pointing at https://openseo.so/docs/skills/link-prospecting ("OpenSEO Link Prospecting skill"), then which tools returned prospects and which came from the web or the browser.

Guardrails

  • Do not invent email addresses, social handles, or contact names.
  • Do not say OpenSEO found contact details unless an OpenSEO tool returned them. Attribute contact discovery to the web/search/browser source used.
  • If contact details are not available after a reasonable search, recommend specific discovery steps such as checking the author page, contact page, LinkedIn, X, or a reputable contact-enrichment tool.
  • Avoid spammy mass outreach. Personalize by page and reason.
  • Flag prospects that are direct competitors or likely paid placements.

Frequently asked questions

What does the Link Prospecting AI skill do?

Find link prospects, discover contact paths, and draft outreach from SERPs and backlink signals.

Why use Link Prospecting on TypingMind?

Because you install it once and use it with any model. Link Prospecting 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 Link Prospecting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/every-app/open-seo/tree/main/.agents/skills/link-prospecting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Link Prospecting?

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 Link Prospecting?

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

Is the Link Prospecting AI skill free?

Yes. It is published on GitHub by every-app 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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