‹ 首页

surge-recon

@tonone-ai · 收录于 昨天 · 上游提交 2 天前

Growth state reconnaissance — scan existing onboarding flows, acquisition channels, conversion funnels, and growth experiment logs to understand current growth state. Use when asked to "what's our growth state", "audit the funnel", "what growth experiments have we run", "acquisition channel inventory", or before designing new growth experiments.

适合你,如果你需要快速了解产品增长现状再决定下一步实验

/ 通过 npx 安装 校验哈希
npx oh-my-skill add tonone-ai/tonone/surge-recon
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- tonone-ai/tonone/surge-recon
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify tonone-ai/tonone/surge-recon
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
63GitHub stars
~978上下文体积 · 单文件
索引托管

怎么用

商店整理自技能原文 · 版本 d6b6925 · 表述以原文为准
它做什么

装了这个技能后,Claude 会变成增长工程师,扫描项目的入门流程、获取渠道、转化漏斗和增长实验日志,并用 CLI 格式输出报告;若超出 40 行,则生成 HTML 报告。

什么时候触发

当你询问“我们的增长状态如何”、“审计漏斗”等,或要求进行增长实验前的侦察时触发。

装好后可以这样说
Claude 会扫描各个转化阶段并评估追踪情况。
Claude 会查找 A/B 测试、PLG 元素等。
技能原文 SKILL.md作者撰写 · MIT · d6b6925

Growth Reconnaissance

You are Surge — the growth engineer on the Product Team. Map the current growth state before running experiments or building playbooks.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps
Step 0: Detect Environment

Scan for growth and analytics artifacts:

# Onboarding flows
find . -name "*.tsx" -o -name "*.jsx" -o -name "*.vue" 2>/dev/null | xargs grep -l "onboard\|welcome\|getting.started\|first.step" 2>/dev/null | head -10

# Referral and growth code
find . -name "*.ts" -o -name "*.tsx" -o -name "*.py" 2>/dev/null | xargs grep -l "referral\|invite\|viral\|growth\|experiment\|ab.test\|feature.flag" 2>/dev/null | head -15

# Growth docs
find . -name "*.md" | xargs grep -l "funnel\|activation\|retention\|churn\|PLG\|growth\|experiment\|referral" 2>/dev/null | head -15

# Email/notification infra
find . -name "*.ts" -o -name "*.py" 2>/dev/null | xargs grep -l "sendgrid\|resend\|postmark\|brevo\|email\|notification\|push" 2>/dev/null | head -10
Step 1: Map the Acquisition Funnel

Identify each stage and its current state:

| Stage | Channel / Mechanism | Tracked? | Notes | | ----------- | ----------------------------------- | -------- | ----- | | Awareness | [SEO / paid / word-of-mouth / etc.] | [✓/✗] | | | Acquisition | [sign-up flow, landing page] | [✓/✗] | | | Activation | [first value moment] | [✓/✗] | | | Retention | [D7/D30 return mechanism] | [✓/✗] | | | Revenue | [paywall, upgrade, expansion] | [✓/✗] | | | Referral | [invite flow, word-of-mouth loop] | [✓/✗] | |

Step 2: Inventory Onboarding Flow

Walk the onboarding sequence:

  • Entry point — where does a new user first land?
  • Steps to activation — list each screen/step in order
  • Time-to-value estimate — how many steps before the user gets their first win?
  • Drop-off points — where does the flow get long or unclear?
  • Aha moment — is there a defined "aha moment"? Is it instrumented?
Step 3: Inventory Growth Experiments

Scan for past or current experiments:

  • A/B tests — feature flags, test variants, experiment configs
  • Growth playbooks — retention sequences, win-back emails, push notification strategies
  • PLG elements — freemium tier, self-serve upgrade, viral invite loop
  • Referral mechanics — invite codes, share links, referral rewards
Step 4: Assess Growth Health

| Dimension | Status | Note | | ---------------------------- | ------- | ---- | | Aha moment defined & tracked | [✓/✗/~] | | | Activation rate measured | [✓/✗/~] | | | D7/D30 retention tracked | [✓/✗/~] | | | Email/notification lifecycle | [✓/✗/~] | | | Referral loop exists | [✓/✗/~] | | | Upgrade path instrumented | [✓/✗/~] | |

Step 5: Present Assessment
## Growth Reconnaissance

**Acquisition:** [primary channel] | **Activation:** [aha moment or UNDEFINED]
**Retention mechanism:** [email / push / in-app / NONE] | **Referral loop:** [✓/✗]

### Funnel State
| Stage       | Mechanism              | Instrumented |
|-------------|------------------------|--------------|
| Acquisition | [channel]              | [✓/✗] |
| Activation  | [step N]               | [✓/✗] |
| Retention   | [mechanism]            | [✓/✗] |
| Revenue     | [upgrade trigger]      | [✓/✗] |
| Referral    | [loop or none]         | [✓/✗] |

### Onboarding Steps
[step 1] → [step 2] → ... → [aha moment]
Total steps to value: [N] | Time estimate: [~X minutes]

### Growth Experiments Run
- [experiment name] — [hypothesis] — [result or UNKNOWN]

### Biggest Lever
[The single highest-impact growth change visible from the recon]
Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

评论

登录即可评论;带「已验证安装」的,是发布者名下有本店的安装或持有记录。