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growth-engineering生长工程

Agent Skill

growth-engineering 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

1,309

周安装

54

GitHub Stars

48

下载量

428
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:growth-engineering(生长工程)
来源仓库:https://github.com/travisjneuman/.claude
仓库路径:skills/growth-engineering
安装命令:
npx skills add https://github.com/travisjneuman/.claude --skill growth-engineering
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/travisjneuman/.claude --skill growth-engineering

简介

growth-engineering 用于处理 GitHub 仓库、Issue 和 Pull Request 信息,适合协作事项整理。

  • 适用于围绕代码变更、仓库状态和团队任务进行信息梳理。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态及是否涉及命令执行或文件读写。
  • 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

name
growth-engineering
description
>-

Growth Engineering Skill

Infrastructure and patterns for product-led growth, experimentation, and conversion optimization.


Feature Flag Systems

Implementation Pattern

// lib/feature-flags.ts
import { PostHog } from 'posthog-node';

const posthog = new PostHog(process.env.POSTHOG_API_KEY!);

interface FeatureFlags {
  'new-onboarding-flow': boolean;
  'pricing-experiment': 'control' | 'variant-a' | 'variant-b';
  'ai-suggestions': boolean;
}

export async function getFlag<K extends keyof FeatureFlags>(
  key: K,
  userId: string,
): Promise<FeatureFlags[K]> {
  const value = await posthog.getFeatureFlag(key, userId);
  return value as FeatureFlags[K];
}

// Usage in component
const showNewOnboarding = await getFlag('new-onboarding-flow', user.id);

Feature Flag Best Practices

  • Short-lived flags: Remove after experiment concludes (< 2 weeks)
  • Long-lived flags: Ops toggles for gradual rollouts, kill switches
  • Never nest feature flags (creates exponential complexity)
  • Clean up stale flags monthly
  • Log flag evaluations for debugging

A/B Testing Infrastructure

Experiment Design

// lib/experiments.ts
interface Experiment {
  id: string;
  name: string;
  variants: {
    id: string;
    weight: number; // 0-100, must sum to 100
  }[];
  targetAudience: {
    percentage: number; // % of users included
    filters?: Record<string, unknown>;
  };
  primaryMetric: string;
  secondaryMetrics: string[];
  minimumSampleSize: number;
  startDate: Date;
  endDate?: Date;
}

// Track experiment exposure
function trackExposure(experimentId: string, variantId: string, userId: string) {
  analytics.capture({
    event: '$experiment_started',
    distinctId: userId,
    properties: {
      $experiment_id: experimentId,
      $variant_id: variantId,
    },
  });
}

Statistical Significance

  • Minimum sample size: Calculate before starting (use Evan Miller calculator)
  • Don't peek: Set duration upfront, don't stop early on promising results
  • Sequential testing: Use if you must check early (adjusts p-values)
  • Minimum detectable effect: Define what improvement matters (e.g., 5% lift)

Product-Led Growth Patterns

Activation Metrics

StageMetricExample
Sign upRegistration completeUser creates account
SetupProfile completeFills required fields
Aha momentCore value experiencedCreates first project
HabitRepeated engagement3 sessions in first week
RevenueConversion to paidSubscribes to plan

Viral Loops

// Referral system pattern
interface Referral {
  referrerId: string;
  referredEmail: string;
  status: 'pending' | 'signed_up' | 'activated' | 'converted';
  rewardGranted: boolean;
}

// Track referral funnel
function trackReferralStep(referralId: string, step: Referral['status']) {
  analytics.capture({
    event: 'referral_step',
    properties: { referralId, step },
  });
}

Conversion Optimization

  • Reduce friction: Minimize form fields, enable social login
  • Social proof: Show user counts, testimonials, logos
  • Urgency: Trial countdown, limited-time offers (use sparingly)
  • Value demonstration: Interactive demos, free tier with clear upgrade path
  • Personalization: Onboarding flow based on use case selection

Growth Metrics

MetricFormulaTarget
Activation rateActivated / Signed up> 40%
Trial-to-paidPaid / Trial started> 15%
Net revenue retention(Start MRR + Expansion - Contraction - Churn) / Start MRR> 110%
Viral coefficientInvites sent * Conversion rate> 0.5
Time to valueMedian time from signup to aha moment< 5 min
DAU/MAU ratioDaily active / Monthly active> 20%

Experimentation Platforms

PlatformTypeBest For
PostHogSelf-hosted/cloudFull-stack, open source
LaunchDarklyCloudFeature flags at scale
StatsigCloudAuto-stats, warehouse-native
GrowthbookSelf-hosted/cloudOpen source, Bayesian stats
OptimizelyCloudEnterprise, multi-channel

Related Resources

  • ~/.claude/skills/product-analytics/SKILL.md - Analytics and tracking
  • ~/.claude/agents/product-analytics-specialist.md - Analytics agent
  • ~/.claude/skills/authentication-patterns/SKILL.md - Auth for PLG

_Measure everything. Experiment constantly. Remove what doesn't work._

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能力概览

能力 1

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能力 2

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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

37.33%
按下载量换算160

Claude

29.5%
按下载量换算126

Cursor

19.32%
按下载量换算83

Gemini CLI

8.66%
按下载量换算37

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

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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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