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cro-optimizer局部优化器

Agent Skill

cro-optimizer 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

210

周安装

9

GitHub Stars

19

下载量

73
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:cro-optimizer(局部优化器)
来源仓库:https://github.com/humblytics/ai-marketing-skills
仓库路径:skills/cro-optimizer
安装命令:
npx skills add https://github.com/humblytics/ai-marketing-skills --skill cro-optimizer
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/humblytics/ai-marketing-skills --skill cro-optimizer

简介

该技能结合 Humblytics 实时数据识别漏斗断点,生成高影响力 A/B 测试假设。

  • 适用于增长团队制定季度优化路线图,聚焦 ROI 最高的实验方向。
  • 输出包含预期收益与实施成本的优先级排序清单,支持资源分配决策。
  • 依赖第三方分析平台接入,需确认数据权限与采样频率满足测试要求。
  • cro-optimizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

CRO Optimizer

Purpose

Analyze conversion funnels using live Humblytics analytics data, identify the highest-impact drop-off points, and generate prioritized A/B test hypotheses with expected revenue impact. This skill turns raw analytics into a ranked optimization roadmap.

When to Use

  • Diagnosing why a funnel is underperforming
  • Identifying the biggest conversion bottleneck across a user journey
  • Generating a prioritized list of A/B test ideas
  • Preparing a CRO sprint plan or quarterly optimization roadmap
  • Analyzing page-level or step-level drop-off rates
  • Comparing conversion performance across segments (device, source, geography)

Credentials

This skill reads a Humblytics API key from the environment. Never paste API keys directly into chat — they persist in transcripts and logs.

Setup (one time):

  1. cp.env.example.env at the repo root and fill in HUMBLYTICS_API_KEY
  2. source.env in your shell before running the agent (or use direnv, or add the exports to your shell profile)
  3. Get the key from Humblytics Dashboard > Settings > API
  4. The skill will ask for your Property ID (also in Dashboard > Settings > API)

If HUMBLYTICS_API_KEY is not in the environment, stop and point the user at .env.example — do not accept the key in chat.

Before You Start

  1. Confirm the property ID — Ask the user which Humblytics property to analyze
  2. Identify the funnel — Clarify which conversion flow to examine (e.g., homepage > pricing > signup > onboarding)
  3. Check for context — Look for existing project docs, AGENTS.md, or product briefs that describe the business model, target audience, and current conversion goals
  4. Establish the time range — Default to last 30 days; ask if the user wants a different window
  5. Confirm API access — Verify HUMBLYTICS_API_KEY is available as an environment variable

Core Workflow

Step 1: Pull Funnel Data

Retrieve analytics data from the Humblytics API:

  • Page views and sessions for each step in the funnel
  • Event data for key conversion actions (signups, clicks, form submissions)
  • Device and source breakdowns to identify segment-specific issues
  • Heatmap summaries if available for high-traffic pages

Use the Humblytics API endpoints:

  • GET /properties/{propertyId}/analytics/pages — Page-level traffic
  • GET /properties/{propertyId}/analytics/funnel — Funnel step data
  • GET /properties/{propertyId}/analytics/events — Custom event tracking
  • GET /properties/{propertyId}/heatmaps — Click and scroll heatmap data

Step 2: Map the Funnel

Build a complete picture of the user journey:

Traffic Source → Landing Page → Key Action → Conversion → Retention

For each step, calculate:

  • Volume: How many users reach this step
  • Conversion rate: Percentage who proceed to the next step
  • Drop-off rate: Percentage who abandon at this step
  • Absolute drop-off: Raw number of users lost

Step 3: Identify the Biggest Leak

Apply the Largest Leak First principle:

  1. Calculate the absolute number of users lost at each step
  2. Rank steps by absolute drop-off (not percentage)
  3. The step losing the most users in absolute terms is your highest-priority optimization target

Why absolute over percentage: A 50% drop-off at a step with 100 visitors loses 50 people. A 10% drop-off at a step with 10,000 visitors loses 1,000 people. Fix the 1,000-person leak first.

Step 4: Diagnose Root Causes

For each high-drop-off step, investigate:

  • Page load time — Slow pages kill conversions. Check if the step has performance issues.
  • Mobile vs desktop — Is the drop-off concentrated on mobile? Layout/UX issue.
  • Traffic source — Do certain acquisition channels show higher drop-off? Expectation mismatch.
  • Scroll depth — Are users seeing the CTA? Check heatmap scroll data.
  • Click patterns — Are users clicking non-interactive elements? Confusing UI.
  • Form fields — For forms, which field has the highest abandonment rate?

Step 5: Generate Test Hypotheses

For each identified issue, create a hypothesis using the ICE framework:

Format:

IF we [change], THEN [metric] will [improve/increase/decrease]
BECAUSE [evidence from data]

Impact: [1-10] — How much will this move the needle?
Confidence: [1-10] — How sure are we this will work?
Ease: [1-10] — How quickly can we implement and test this?
ICE Score: [average of three]

Step 6: Prioritize and Recommend

Rank all hypotheses by ICE score and present:

  1. Top 3 Quick Wins — High ease, decent impact (ship this week)
  2. Top 3 High-Impact Tests — High impact, may require more effort (sprint backlog)
  3. Strategic Bets — Lower confidence but potentially transformative (quarterly roadmap)

For each recommendation, include:

  • The specific page or funnel step
  • What to change and why
  • Expected impact on conversion rate
  • Suggested test duration based on traffic volume

Analysis Frameworks

The RICE Prioritization (for larger teams)

  • Reach: How many users per month does this affect?
  • Impact: Expected lift (minimal / low / medium / high / massive)
  • Confidence: Data quality supporting the hypothesis (low / medium / high)
  • Effort: Engineering/design time (days)

Score = (Reach x Impact x Confidence) / Effort

Segment Analysis Checklist

Always break down conversion data by:

  • Device type (mobile / desktop / tablet)
  • Traffic source (organic / paid / direct / referral / social)
  • Geography (if international)
  • New vs returning visitors
  • Entry page

Common Funnel Archetypes

Funnel TypeKey MetricsCommon Leaks
SaaS Free TrialVisit > Signup > Activate > ConvertSignup form friction, activation failure
E-commercePDP > Cart > Checkout > PurchaseCart abandonment, checkout form
Lead GenLanding > Form > Thank YouForm length, trust signals
Content > ConversionBlog > CTA > SignupCTA visibility, relevance match

Output Format

Present findings as:

  1. Funnel Overview — Visual step-by-step with volumes and rates
  2. Key Finding — The single biggest insight in one sentence
  3. Drop-off Analysis — Ranked list of leaks with absolute numbers
  4. Root Cause Diagnosis — What is causing each major leak
  5. Prioritized Test Roadmap — ICE-scored hypotheses ready for execution
  6. Expected Impact — If top 3 tests succeed, projected conversion lift

Related Skills

  • ab-test-generator — Take the hypotheses from this skill and generate actual test configurations
  • funnel-reporter — Pull comprehensive funnel reports with revenue data
  • page-cro — Deep-dive into a specific page's conversion issues
  • copywriting — Generate optimized copy for test variants

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

33.13%
按下载量换算24

Claude

30.95%
按下载量换算23

Cursor

16.34%
按下载量换算12

Gemini CLI

9.76%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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

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