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linkedin-post-experimentation

linkedin-post-experimentation

github.com/lvtd-llcMarketing & growth143 installsUpdated 7w ago
Repository

About linkedin-post-experimentation

A skill for designing and evaluating LinkedIn post experiments. It helps marketers test one content variable at a time, define metrics in advance, and use audience fit and response quality to guide the next iteration.

Key features

  1. 01Form hypotheses about content and audience
  2. 02Test one variable at a time
  3. 03Set metrics before publishing
  4. 04Collect results after publishing
  5. 05Choose whether to repeat, revise, or stop

Use cases

  1. 01Compare formats for a content idea
  2. 02Test a topic with a target audience
  3. 03Assess post performance beyond impressions
  4. 04Decide how to revise a content angle

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lvtd-llc/skills --skill linkedin-post-experimentation

After installing, type/linkedin-post-experimentationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/core/
examples.md
knowledge.md
rules.md
workflows/
run-post-experiment.md

Details

From the project README

LinkedIn Post Experimentation

Source Traceability

Primary source: Growth Hacking LinkedIn by Bjorn Radde, especially sections 2.1 "Phases of growth hacking", 2.2 "Growth Hacking LinkedIn", 3.4.1 "Posts", 3.7 "Social Selling Index", and 4.1 "LinkedIn analysis tools". Guidance is transformed and paraphrased.

Reference Routing

NeedRead
Experiment model and source notesreferences/core/knowledge.md
Experiment design rulesreferences/core/rules.md
Test templates and analysis examplesreferences/core/examples.md
Run a content experimentworkflows/run-post-experiment.md

Workflow

  1. Turn a content idea into a hypothesis about audience, topic, format, or response.
  2. Choose one variable to test.
  3. Define metrics before publishing: impressions, engagements, comments, saves, sends, profile visits, followers, newsletter subscriptions, or qualified conversations.
  4. Publish, respond to comments, and collect results after a sensible window.
  5. Decide whether to repeat, revise, or stop the content angle.

Output Format

# LinkedIn Content Experiment

## Hypothesis
[Audience + content variable + expected signal.]

## Test Design
- Variable:
- Control or comparison:
- Format:
- Publishing window:
- Engagement plan:

## Metrics
| Metric | Why It Matters | Target |
|--------|----------------|--------|

## Decision Rules
- Repeat:
- Revise:
- Stop:

## Learning Log
- What happened:
- What to try next:

Quality Bar

  • Do not optimize five variables at once.
  • Prefer learning from qualified response over raw reach.
  • Treat analytics as directional, not perfect truth.
  • Include comment quality and audience fit, not just impressions.

Comments

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