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Playbook Linkedin Engagement

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growthenginenowoslawski
playbook-linkedin-engagement

Turn the people who like and comment on a source account's LinkedIn posts (a competitor, or one of the client's own customers) into ICP-filtered prospect rows, with an optional copy-ready personalization line when the campaign asks for one. Triggers on "who engages with our competitors", "likers and commenters", "LinkedIn engagement list", "people commenting on competitor posts", "who engages with our customers' posts", "warm LinkedIn audience", "competitor engagers". Outputs one clean prospect row per engager; engagement_line only on request.

Overview

Publishergrowthenginenowoslawski
Repositorycoldoutboundskills
Skill nameplaybook-linkedin-engagement
Stars
757
Forks
267
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 growthenginenowoslawski on GitHub. Read the source before you install it.

Installation

Install the Playbook Linkedin Engagement 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/growthenginenowoslawski/coldoutboundskills/tree/main/skills/playbooks/playbook-linkedin-engagement
  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/growthenginenowoslawski/coldoutboundskills.git /tmp/coldoutboundskills
mkdir -p .claude/skills
cp -r /tmp/coldoutboundskills/skills/playbooks/playbook-linkedin-engagement .claude/skills/playbook-linkedin-engagement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Playbook Linkedin Engagement 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 Playbook Linkedin Engagement 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 Playbook Linkedin Engagement 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.

Playbook: LinkedIn Engagement

All rules here are best practice, not law. Override any of them when the campaign calls for it; note the best practice once and move on.

Use when an account's audience is the client's audience — a competitor or the client's own customer.

Do not use for more companies in a market, similar companies (playbook-lookalikes), or job changers (playbook-new-in-role). The chain is the same for both lanes; only the target URL changes.

Output: one row per engager — who, employer, post, evidence URL. engagement_line is opt-in.

The number that governs planning

⚠️ Roughly 1 in 10 raw engagers survives. Harvest 10 to 15x what you need.

Cost follows from that: about $4.45 per 1,000 raw engagers, which is about $45 per 1,000 usable prospects at the measured survival rate. That is squarely in expensive territory, so shortlist posts by engagement count before you scrape.

The source-company rule (both halves, always automatic)

This is the rule that separates a usable engagement list from an embarrassing one, and it has two halves that people implement only one of.

(a) Drop every engager employed by ANY source company — not just the author of the post they engaged with. Match each current employer on resolved domain first, then profile URL, then squashed name, and also on resolved email domain.

(b) Push every source company onto that client's do-not-contact list. Row drops fix only this run; the block list runs at send time, which is what stops the same people arriving through a different lane next month.

It bites hardest on the customer lane, where the source companies are people the client already works with.

⛔ Never block the client's own domain. If it appears in the source set, the list is wrong — stop.

Before any paid call

  1. The operator confirms the source list. Say it out loud: "these companies and everyone who works at them go on this client's DNC list."
  2. Resolve every source account to a bare domain first. An unresolved source silently disables both halves of the rule above. Unresolved means stop, not continue. (playbook-social-link-finding does this conversion in both directions.)
  3. If the client's ICP is unknown, ask. Never infer a headcount band, a country list, or a title set.

Run it

0   Confirm source URLs, client ICP, and whether the brief wants a line (default: no).
0b  Resolve every source account to a bare domain -> source-companies.json (EXACT match only).
    Unresolved => STOP.
1   Company posts, windowed to the last month, shortlisted by engagement count.
2   Post reactions + post comments for the shortlisted posts.
3   ICP enrichment (headcount, company) on the engagers.
4   Deterministic pre-gate: source-company drop, ICP filter, dedupe.
5   Suppression pass: client DNC, existing TAM, live campaigns.
6   DNC push (once per harvest).
7   Email finding, valid only.

Rows are the deliverable; lines are opt-in. Never run a line writer unless the brief asks. When it does, an empty-line row leaves that campaign for the normal one rather than shipping a gap.

Verification

PASS 17/18 correct outcomes on the source-and-gate test — and note what that counts: a correct exclusion is scored as a usable outcome. Raw signal yield was 1 usable prospect in 17 unique people.

Both numbers are true and they measure different things. The gate works; the yield is thin. Plan from the yield.

⚠️ The end-to-end harvest has never been run in one pass. Do a one-post run and read the output before scaling.

Clay implementation

  • clay-table.md — where the gate lives in a table.
  • clay-workflow.md — the CLI-buildable version.

Edge cases and failure modes

SymptomCauseFix
The list contains the client's own employeesThe client's domain got into the source setStop. The list is wrong
The list contains the competitor's own staffOnly half (a) was implemented, matched on name aloneMatch on resolved domain first, then profile URL, then squashed name, then email domain
The same people reappear next month through another laneHalf (b) was skippedThe DNC push is what persists. Row drops do not
The source-company rule appears to do nothingAn unresolved source URL produced an empty source setAn empty set silently disables both halves. Unresolved means stop
Cost per usable prospect is 10x the estimateYou costed raw engagers, not survivors~1 in 10 survives. Harvest 10-15x
A person's employer is wrongTaking the first entry of an experience array rather than the current oneRead the current experience explicitly
Rows silently vanish on a joinPost and engager records key differentlyConfirm the join key on one post before scaling
The run stalls waiting on a scraperThere is no fallback source by designThe run waits. A different scraper is not a drop-in substitute here

Hard rules

  • Both halves of the source-company rule, every run, automatically.
  • The operator confirms the source list before any paid call.
  • Rows are the deliverable. Lines are opt-in.
  • If the ICP is unknown, ask.

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 Playbook Linkedin Engagement AI skill do?

Turn the people who like and comment on a source account's LinkedIn posts (a competitor, or one of the client's own customers) into ICP-filtered prospect rows, with an optional copy-ready personalization line when the campaign asks for one. Triggers on "who engages with our competitors", "likers and commenters", "LinkedIn engagement list", "people commenting on competitor posts", "who engages with our customers' posts", "warm LinkedIn audience", "competitor engagers". Outputs one clean prospect row per engager; engagement_line only on request.

Why use Playbook Linkedin Engagement on TypingMind?

Because you install it once and use it with any model. Playbook Linkedin Engagement 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 Playbook Linkedin Engagement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/growthenginenowoslawski/coldoutboundskills/tree/main/skills/playbooks/playbook-linkedin-engagement. 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 Playbook Linkedin Engagement?

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 Playbook Linkedin Engagement?

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

Is the Playbook Linkedin Engagement AI skill free?

Yes. It is published on GitHub by growthenginenowoslawski 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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