Lookalike Candidate Sourcing logo

Lookalike Candidate Sourcing

Organization
swan-gtm
lookalike-candidate-sourcing

Use this skill when a role worked and you want more of that person, or when you are backfilling someone strong and a job-board post is not going to find them. Takes one exemplar profile and returns a ranked, scored shortlist with the reasoning behind each score visible.

Overview

Publisherswan-gtm
Repositorygtm-skills
Skill namelookalike-candidate-sourcing
Stars
216
Forks
103
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 swan-gtm on GitHub. Read the source before you install it.

Installation

Install the Lookalike Candidate Sourcing 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/swan-gtm/gtm-skills/tree/main/skills/amir-baldiga/lookalike-candidate-sourcing
  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/swan-gtm/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/amir-baldiga/lookalike-candidate-sourcing .claude/skills/lookalike-candidate-sourcing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lookalike Candidate Sourcing 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 Lookalike Candidate Sourcing 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 Lookalike Candidate Sourcing 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.

Use this when one hire worked and you want more like them. It produces a ranked shortlist where every score decomposes into title, skills, seniority, and location, so a hiring manager can argue with it.

The play

  1. Take one exemplar, not a job description. A JD describes what someone wrote down. A person who is actually good in the role describes what worked. Start from the exemplar's real current title, skills, and location.

  2. Push filters into the search, not into post-processing. Role family, country, city, and current-role-only belong in the query. Filtering a large result set client-side wastes lookups and hides how narrow you actually were.

  3. Start broad, then tighten once. Role family plus two skills plus a country is a good opening net. Stacking an exact title, an all-skills-match, and a city returns zero and looks like "nobody exists" when it is really a bad query.

  4. Decide whether to enrich. Without enrichment you get a cheap keyword-ordered list. With it you get skills, education, and geography per candidate, at one extra lookup each. Enrich 20, not 200, until the shortlist direction is confirmed.

  5. Score transparently out of 100:

    • Title match, 40 — exact title 40, same role family at any seniority 25, different family 0.
    • Skills overlap, 30 — shared skills over the exemplar's skill count, times 30, compared on normalized names.
    • Seniority, 15 — same level 15, one level off 7, further 0. Infer from title prefix: Senior, Staff, Principal, Head of, VP.
    • Location, 15 — same city 15, same metro 10, same country 5.
  6. Drop the exemplar before ranking. Search returns them, they score 100, and it makes the list look broken.

  7. Return a table, not JSON. Name, current title, company, location, score, and the one line explaining the score.

What good looks like

  • The strongest sourcers treat a zero-result search as information about the query, not the market, and can say which filter they loosened and why.
  • The mediocre version returns fifty unranked names and calls it a pipeline, leaving the hiring manager to do the actual sourcing work.
  • A score is only useful if it decomposes. "77" means nothing; "77: exact title, 6 of 9 skills, one level junior, same metro" is a conversation.
  • You know it is good when the hiring manager disagrees with a specific score for a specific reason. That means they can see the reasoning.

Rules

  • MUST remove the exemplar from their own results.
  • MUST show the score breakdown, never a bare number.
  • MUST start broad and tighten deliberately, one filter at a time.
  • NEVER infer salary, notice period, or willingness to move. That data is not in a public profile.
  • NEVER present a keyword-ordered list as a ranked one when enrichment was skipped. Say the scores were not computed.

Frequently asked questions

What does the Lookalike Candidate Sourcing AI skill do?

Use this skill when a role worked and you want more of that person, or when you are backfilling someone strong and a job-board post is not going to find them. Takes one exemplar profile and returns a ranked, scored shortlist with the reasoning behind each score visible.

Why use Lookalike Candidate Sourcing on TypingMind?

Because you install it once and use it with any model. Lookalike Candidate Sourcing 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 Lookalike Candidate Sourcing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/swan-gtm/gtm-skills/tree/main/skills/amir-baldiga/lookalike-candidate-sourcing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lookalike Candidate Sourcing?

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 Lookalike Candidate Sourcing?

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

Is the Lookalike Candidate Sourcing AI skill free?

Yes. It is published on GitHub by swan-gtm 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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