How do I install the Linkedin Post Experimentation skill?

Run npx skillmds add thedixitjain/linkedin-post-experimentation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Linkedin Post Experimentation safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Linkedin Post Experimentation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Linkedin Post Experimentation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Linkedin Post Experimentation?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Linkedin Post Experimentation by thedixitjain

Name: Linkedin Post Experimentation
Rating: 4.2 (5 reviews)
Author: thedixitjain

Linkedin Post Experimentation

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.

thedixitjain f4ae3f4 5 files · 9.6 KB Updated Sep 9, 2026 2 repo stars

File contents

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

Turn a content idea into a hypothesis about audience, topic, format, or response.
Choose one variable to test.
Define metrics before publishing: impressions, engagements, comments, saves, sends, profile visits, followers, newsletter subscriptions, or qualified conversations.
Publish, respond to comments, and collect results after a sensible window.
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.

Source: hashgraph-online/awesome-codex-plugins → plugins/LVTD-LLC/skills/skills/linkedin-post-experimentation/SKILL.md

---name: linkedin-post-experimentationdescription: 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.--- # 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 ```markdown # 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. --- **Source:** [`hashgraph-online/awesome-codex-plugins`](https://github.com/hashgraph-online/awesome-codex-plugins) → `plugins/LVTD-LLC/skills/skills/linkedin-post-experimentation/SKILL.md`

Frequently asked questions