Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计通过

mkt-lp-optimizationmkt LP 优化

Agent Skill

mkt-lp-optimization 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

321

周安装

13

GitHub Stars

2

下载量

101
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mkt-lp-optimization(mkt LP 优化)
来源仓库:https://github.com/hungv47/agent-skills
仓库路径:skills/mkt-lp-optimization
安装命令:
npx skills add https://github.com/hungv47/agent-skills --skill mkt-lp-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/hungv47/agent-skills --skill mkt-lp-optimization

简介

mkt-lp-optimization 针对落地页进行转化路径优化。

  • 适合通过关键词分析用户行为与流失节点。
  • 输出建议需结合 A/B 测试结果,避免主观臆断。
  • 涉及表单提交或数据整理时应遵守隐私规范。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Landing Page Conversion Optimization

*Horizontal skill — optimizes the conversion layer where communication meets action.*

Inputs Required

  • Landing page URL or description of the page
  • ICP research from .agents/mkt/icp-research.md (recommended — VoC language strengthens copy)
  • Traffic source context (where visitors come from)

Output

  • Optimization recommendations with specific copy/structure changes
  • For new pages: complete page structure with copy

Quality Gate

Before delivering, verify:

  • Headline scores ≥3 out of 4 on the U's (Useful, Unique, Urgent, Ultra-specific)
  • Message matches the traffic source (headline echoes the ad/link that brought them)
  • One primary CTA per page (secondary CTAs don't compete)
  • Trust signals appear within scroll-distance of every CTA
  • Form has ≤5 fields (or justified why more are needed)

Chain Position

Horizontal — works with mkt-icp-research (audience data), mkt-copywriting (copy principles), mkt-experiment (test design)


Before Starting

Step 0: Product Context

Check for .agents/mkt/product-context.md. If available, read for product details and accuracy.

Required Artifacts

None — can audit any page standalone.

Optional Artifacts

ArtifactSourceBenefit
icp-research.mdmkt-icp-researchVoC data for persuasion
product-context.mdmkt-copywritingProduct details for accuracy
experiment-*.mdmkt-experimentTest design context

Core Frameworks

4-U Headline Formula

UQuestionScoring
UsefulDoes it communicate clear value?Y/N
UniqueCould a competitor use this headline?Y/N (N = good)
UrgentIs there a reason to act now?Y/N
Ultra-specificDoes it include a number or concrete outcome?Y/N

80% of visitors won't read past the headline. Generate 10+ variations, score each.

PAS Copy Framework

  1. Problem: Lead with pain in the audience's own language. If .agents/mkt/icp-research.md exists, use VoC quotes directly.
  2. Agitate: What happens if they don't solve this? Make consequences vivid.
  3. Solve: Your product as the relief. Benefits, not features.

Message Match

Check: does the landing page headline echo the exact promise from the ad/email/link? Broken promise = instant bounce.

WebSearch directive: If auditing a live page, search site:[domain] "[headline text]" to find the ads/links driving traffic. Verify message match.

First-Person CTA

"Get MY guide" > "Get YOUR guide" (90% more clicks). Formula: [Action Verb] + [What They Get]


Quick Reference

Social Proof Hierarchy (most → least powerful)

  1. Testimonials with specific results ("Increased revenue 40% in 3 months")
  2. Case studies with before/after numbers
  3. Customer count ("Join 10,000+ teams")
  4. Media mentions / press logos
  5. Expert endorsements
  6. Customer logos

Cognitive Bias Stack

BiasHow to Apply
Loss aversion"Don't miss..." / limited genuine availability
Social proofTestimonials near CTAs, user counts
AnchoringShow higher price first, then actual price
ReciprocityGive free value before asking (lead magnet, calculator)
AuthorityExpert quotes, certification badges, press logos

Form Rules

  • ≤5 fields. Every additional field costs ~10% conversions
  • Start with just email. Use progressive profiling for the rest
  • Each field must justify its existence — if you can ask later, do

Trust Signals

Cluster near CTAs and forms: security badges, money-back guarantee, privacy link, contact info.


Testing Priority

Test in this order (highest impact first):

  1. Headlines (biggest swing)
  2. Offers (what you're promising)
  3. CTAs (text, color, placement)
  4. Page layout
  5. Form fields

Use mkt-experiment for proper test design with success/kill thresholds and sample size validation.


Workflows

New Landing Page

  1. Read ICP research (if available) for VoC language and pain points
  2. Define primary conversion goal (one per page)
  3. Generate 10+ headline variations using 4-U formula
  4. Structure body with PAS framework
  5. Add social proof (strongest above fold)
  6. Design form (minimal fields)
  7. Place trust signals near every CTA
  8. Verify message match with traffic source
  9. Run through quality gate checklist

Optimization Audit

  1. Check message match between traffic sources and headline
  2. Score headline against 4-U formula
  3. Audit social proof: placement, specificity, relevance
  4. Count form fields — can any be removed?
  5. Check mobile experience (thumb zone CTAs, load time)
  6. Identify highest-impact fix
  7. Design A/B test via mkt-experiment

Artifact Frontmatter

When saving optimization artifacts, use this frontmatter:

---
skill: mkt-lp-optimization
version: 1
date: [today's date]
status: draft
---
On re-run: rename existing artifact to [name].v[N].md and create new with incremented version.

References

ReferenceUse For
core-principles.mdHeadlines, value props, CTAs, forms, message match, PAS
social-proof-trust.mdSocial proof hierarchy, biases, trust signals
ux-design.mdVisual hierarchy, mobile optimization
advanced-psychology.mdHeadline formulas, close sequences, pricing, urgency
testing-optimization.mdA/B testing, tracking
implementation-checklist.mdPre-launch checklists

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.07%
按下载量换算34

Claude

30.83%
按下载量换算31

Cursor

20.75%
按下载量换算21

Gemini CLI

9.51%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills