---

name: linkedin-post-experimentation description: Plan, measure, and iterate LinkedIn content experiments using post hypotheses, format tests, analytics, comments, and learning loops. Use when comparing LinkedIn hooks, formats, topics, hashtags, posting cadence, newsletters, documents, videos, or content performance data. license: MIT compatibility: Codex, Claude Code, and other Agent Skills-compatible clients. metadata: version: "0.1.0" displayName: LinkedIn Post Experimentation category: Marketing tags: linkedin-writing,linkedin,analytics,content-experiments,growth,social-media

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

Need	Read
Experiment model and source notes	references/core/knowledge.md
Experiment design rules	references/core/rules.md
Test templates and analysis examples	references/core/examples.md
Run a content experiment	workflows/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.