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conversion-optimization

Diagnose and improve landing, signup, onboarding, activation, paywall, and upgrade flows using observed behavior and bounded experiments.

Skill by luxiaolei

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/luxiaolei-conversion-optimization

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Diagnose and improve landing, signup, onboarding, activation, paywall, and upgrade flows using observed behavior and bounded experiments.

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/conversion-optimization

What's inside

Skill name
conversion-optimization
Description
Diagnose and improve landing, signup, onboarding, activation, paywall, and upgrade flows using observed behavior and bounded experiments.
License
MIT
Instructions
18 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • SKILL.zh-CN.md

SKILL.md (first lines)

# Conversion Optimization

## Diagnose before changing

1. Define the exact funnel step, denominator, target segment, and business outcome.
2. Reproduce the flow on the real product with the appropriate device/account state; capture errors, ambiguity, latency, trust gaps, unnecessary work, and mismatched expectations.
3. Combine product behavior, funnel data, replay/session evidence, support/customer evidence, and technical failures. Missing denominators or broken instrumentation are first-class issues.
4. Check upstream traffic quality and downstream activation/revenue so a local conversion gain does not hide worse users.

## Experiment

Use `growth-experiment`: state the friction hypothesis, one primary change, primary metric, diagnostics, guardrails, observation logic, and rollback. For low traffic, use qualitative or sequential evidence without claiming randomized certainty.

## Implement and verify

Prefer preview/staging first, test happy and failure paths, preserve analytics events, then follow the autonomy policy for production rollout. Verify the live state after deploy and start the observation window only when instrumentation and treatment are confirmed.

From luxiaolei/AutoSpread, MIT.

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Last commit (this folder)
Sep 27, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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conversion-optimization skill. Agent Mart. https://agentmart.dev/skills/luxiaolei-conversion-optimization (last updated 2026-10-24)