菜单
Manage revenue pipelines, track Stripe/analytics metrics, and automate financial reporting for SaaS businesses. Use when building revenue infrastructure.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/oyi77/1ai-skills --skill revenue-engine命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | revenue-engine |
| description | Manage revenue pipelines, track Stripe/analytics metrics, and automate financial reporting for SaaS businesses. Use when building revenue infrastructure. |
| domain | core |
| license | Apache-2.0 |
| tags | ["engine","infrastructure","memory","pipeline","revenue","self-improvement","money","analytics"] |
| version | 2.0.0 |
| author | oyi77 |
| subdomain | |
| type | core |
This engine is the financial nervous system of your business. Without it, you leak 5-15% of revenue through forgotten invoices, missed renewals, and untracked metrics. With it, you capture every dollar and know exactly where to focus to grow.
# Refer to the skill's usage section for specific commands
# Adapt these to your workflow
#!/usr/bin/env bash
# Revenue engine health check
mkdir -p ~/revenue-engine/{metrics,reports,alerts}
echo "=== Revenue Health Check ==="
echo "MRR: $(curl -s https://api.stripe.com/v1/subscriptions --header "Authorization: Bearer $STRIPE_KEY" 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(sum(int(i['plan']['amount']) for i in d['data'])/100)" 2>/dev/null || echo 'Set \$STRIPE_KEY')"
echo ""
echo "Metrics to track this week:"
echo " - MRR growth rate"
echo " - Churn rate (exits / total)"
echo " - LTV (avg revenue × avg months retained)"
echo " - CAC (total sales cost / new customers)"
echo " - Quick Ratio (new MRR + expansion MRR) / churned MRR"
Revenue Engine is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.
Trigger phrases:
Set up required environment variables and paths, configure logging level and output format, define resource limits (memory, time, API calls), enable/disable features via configuration flags.
Different revenue tasks need different models. Route intelligently:
# Example: Model routing
ROUTES = {
"code": ["claude-sonnet-4-20250514", "gpt-4o"],
"vision": ["gemini-2.5-pro", "gpt-4o"],
"fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}
def route_request(task: str, prompt: str):
models = ROUTES.get(task, ROUTES["fast"])
for model in models:
try:
return call_model(model, prompt)
except Exception:
continue
raise RuntimeError("All models failed")
| Excuse | Truth |
|---|---|
| "I'll track revenue manually" | Manual tracking misses 10%+ of revenue |
| "I don't have enough revenue yet" | Start tracking at $0; patterns emerge early |
| "Stripe dashboard is enough" | Dashboards don't alert you to problems |
On completion: "MRR: $[N], Churn: [N]%, LTV: $[N], CAC: $[N], Quick Ratio: [N], Revenue Health: [GREEN/YELLOW/RED]"
Use this skill when working with revenue engine.
See the parent skill for authoritative workflow documentation.