Linkedin Employee Advocacy logo

Linkedin Employee Advocacy

CommunityPopular
sergebulaev
linkedin-employee-advocacy

Stand up and run a LinkedIn employee advocacy program for a marketing or sales team. Covers 14-day launch playbook, brand-guideline governance, per-post time budget, cadence benchmarks, and team ROI (reach, engagement, pipeline). Triggers on "employee advocacy", "get the team posting", "scale LinkedIn across team", "advocacy ROI". Not for planning one person's own calendar (use linkedin-content-planner).

Overview

Publishersergebulaev
Repositorylinkedin-skills
Skill namelinkedin-employee-advocacy
Stars
2.6K
Forks
456
Bundled files
3
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 3 bundled files

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

  • Open source

    Published by sergebulaev on GitHub. Read the source before you install it.

Installation

Install the Linkedin Employee Advocacy 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.
  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/sergebulaev/linkedin-skills.git /tmp/linkedin-skills
mkdir -p .claude/skills
cp -r /tmp/linkedin-skills/skills/linkedin-employee-advocacy .claude/skills/linkedin-employee-advocacy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin Employee Advocacy 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 Linkedin Employee Advocacy 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 Linkedin Employee Advocacy 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.

LinkedIn Employee Advocacy

Stand up a marketing-team LinkedIn advocacy program that scales without killing authenticity. Employee posts get 8x more engagement than brand-page posts — this skill operationalizes that advantage.

When to use

  • Marketing leader wants to get their team posting on LinkedIn
  • User is planning an advocacy program launch
  • Team is posting but output is inconsistent / off-brand / low-engagement
  • Need ROI measurement framework for an existing program
  • Requests: "how do I get the team posting", "launch advocacy", "scale LinkedIn across 10 people"

Input

  • Team size (5-50 typical)
  • Marketing goal (reach / pipeline / recruiting / thought leadership)
  • Current state (everyone silent / some active / inconsistent)
  • Brand guideline constraints

Output

  • 14-day launch plan (if cold-starting)
  • Operating model (voice capture, ideation, approval, posting, measurement)
  • Cadence targets per team member (realistic, not punishing)
  • KPI dashboard spec (team reach, engagement, pipeline attribution)
  • Governance playbook (brand safety without blocking velocity)

Four operating principles

  1. Scale authentically. Individuals compose in their own voice, not corporate language. Corporate-tone team posts underperform authentic voice 3x.
  2. Maintain control. Brand guidelines integrated into the workflow. Review step is optional, not blocking — high-trust roles bypass review entirely.
  3. Remove friction. Per-post time budget: 5 minutes. Anything more and the program dies in week 3.
  4. Prove ROI. Track team reach, engagement, pipeline impact. Without attribution, the program gets cut at the first budget review.

Benchmarks

  • Launch target: team posting within 14 days
  • Active team size benchmark: 8-11 members
  • Output benchmark: 70+ posts/week (at 8 members) or 3-5 posts/member/week
  • Per-post time budget: 5 minutes
  • Team touchpoint math: 11 people × 3 posts/week × 300 min impressions = 40,000 monthly touchpoints baseline
  • Employee vs. brand page: 8x more engagement, 6-8x more reach on personal posts

14-day launch playbook

Days 1-3: Voice capture

  • Short interview with each team member (5-10 min) to extract their actual voice
  • Identify their domain expertise and 2-3 content pillars
  • Set realistic individual cadence (some commit to 1/week, some 3/week — don't force uniformity)

Days 4-7: First posts

  • Everyone ships their first post, drafted in their voice
  • Marketing reviews only for brand safety (never for style)
  • Celebrate every first post internally — social proof unlocks the next team member

Days 8-10: Ideation pipeline

  • Set up a shared ideation source (newsletter digest, trending-topics feed, internal wins)
  • Each team member gets 5-10 topic suggestions per week
  • They pick, not assigned

Days 11-14: Rhythm lock

  • Establish cadence: each team member publishes on fixed days/times
  • Set up KPI dashboard (see below)
  • Run first weekly review

Governance: brand-safe without being blocked

What marketing reviews:

  • Factual claims about the company / products / customers
  • Confidential info
  • Legal/compliance issues (finance, health, regulated industries)

What marketing does NOT review:

  • Personal voice, tone, style
  • Opinions the team member has about their own work
  • Formatting, hashtags, emoji choices
  • Topic selection (within pillars)

The review SLA: <4 business hours. Anything longer and the post is dead (posts go stale in the news cycle).

ROI measurement

Per-person metrics (content quality)

  • Impressions per post
  • Engagement rate (reactions + comments + shares / impressions)
  • Comments (depth signal)
  • Profile views attributed to post

Team-level metrics (program health)

  • Total team reach
  • Total team engagement
  • Individual contribution rank (leaderboard)
  • Active members / total members (participation rate)

Business metrics (pipeline impact)

  • Inbound DMs sourced from LinkedIn content
  • Meetings booked from LinkedIn
  • Closed-won deals with LinkedIn as first-touch channel
  • Employee referrals sourced from LinkedIn (if recruiting is a goal)

Anti-patterns

  • Copy-paste corporate posts across team accounts — LinkedIn detects this, suppresses all of them
  • Ghostwriting that erases the writer's voice — reads as fake
  • Mandatory posting cadence without individual calibration — program dies in 6 weeks
  • Approval loops >24h — makes the program feel like work
  • Measuring only vanity metrics — program gets cut without pipeline attribution
  • All-same pillars across team — redundancy kills team reach (360Brew penalizes clustering)

Resources

  • references/advocacy-principles.md — the 4 operating principles with examples
  • references/team-cadence-matrix.md — realistic cadence by role + seniority
  • references/governance-playbook.md — what to review, what not to, SLA

Related skills

  • linkedin-post-writer — each team member uses this for individual drafts
  • linkedin-profile-optimizer — team profiles should match before the program launches (otherwise profile clicks convert poorly)
  • linkedin-content-planner — each team member gets their own pillar mix
  • linkedin-thread-monitor — track which team members' comments drive author replies
  • linkedin-engager-analytics — see who's engaging with each team member's posts
  • linkedin-comment-drafter — its reshare mode is how team members amplify a brand or colleague post to their own feed with a short take (lib.repost(post_url, commentary) on approval); the cleanest advocacy action after an original post

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 Linkedin Employee Advocacy AI skill do?

Stand up and run a LinkedIn employee advocacy program for a marketing or sales team. Covers 14-day launch playbook, brand-guideline governance, per-post time budget, cadence benchmarks, and team ROI (reach, engagement, pipeline). Triggers on "employee advocacy", "get the team posting", "scale LinkedIn across team", "advocacy ROI". Not for planning one person's own calendar (use linkedin-content-planner).

Why use Linkedin Employee Advocacy on TypingMind?

Because you install it once and use it with any model. Linkedin Employee Advocacy 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 Linkedin Employee Advocacy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-employee-advocacy. 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 Linkedin Employee Advocacy?

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 Linkedin Employee Advocacy?

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

Is the Linkedin Employee Advocacy AI skill free?

Yes. It is published on GitHub by sergebulaev 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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