技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
asc-metrics【数据与研究】When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App St…
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
You analyze the user's official App Store Connect data synced into Appeeky — exact downloads, revenue, IAP, subscriptions, and trials. This is first-party data, not estimates.
If ASC is not connected, prompt the user to connect it at appeeky.com/settings and return.
app-marketing-context.md — read it for app contextGET /v1/connect/metrics/apps
Match the user's app to an app_apple_id if not already known.
GET /v1/connect/metrics?from=YYYY-MM-DD&to=YYYY-MM-DD
GET /v1/connect/metrics/apps/:appId?from=YYYY-MM-DD&to=YYYY-MM-DD
Response includes: daily[], countries[], totals.
See full API reference: appeeky-connect.md
Fetch two equal-length windows and compare:
| Metric | Prior Period | Current Period | Change |
|---|---|---|---|
| Downloads | [N] | [N] | [+/-X%] |
| Revenue | $[N] | $[N] | [+/-X%] |
| Subscriptions | [N] | [N] | [+/-X%] |
| Trials | [N] | [N] | [+/-X%] |
| Trial → Sub Rate | [X]% | [X]% | [+/-X pp] |
What to look for:
From daily[], identify:
Sort countries[] by downloads and revenue:
Compute from the data:
| Metric | Formula | Benchmark |
|---|---|---|
| ARPD | Revenue / Downloads | > $0.05 good; > $0.20 excellent |
| Trial rate | Trials / Downloads | > 20% means strong paywall reach |
| Sub conversion | Subscriptions / Trials | > 25% is strong |
| Revenue per sub | Revenue / Subscriptions | Depends on pricing |
📊 [App Name] — [Period]
Downloads: [N] ([+/-X%] vs prior period)
Revenue: $[N] ([+/-X%])
Subscriptions: [N] ([+/-X%])
Trials: [N] ([+/-X%])
IAP Count: [N] ([+/-X%])
Trial→Sub: [X]%
Top Markets (downloads):
1. [Country] — [N] downloads, $[N]
2. [Country] — [N] downloads, $[N]
3. [Country] — [N] downloads, $[N]
Key Observations:
- [What the trend means]
- [Any anomaly and likely cause]
- [Opportunity identified]
Recommended Actions:
1. [Specific action based on data]
2. [Specific action based on data]
When a significant change (>20%) is detected, flag it:
⚠️ Downloads dropped [X]% this week
Possible causes: [list 2-3 hypotheses]
Next steps: [specific diagnostic actions]
"Why did my downloads drop?"
keyword-research skill)competitor-analysis skill)"Which countries should I localize for?"
Pull country breakdown → sort by downloads → flag high-download, non-English markets → use localization skill
"Is my monetization improving?"
Compare trial rate and trial→sub rate period over period → use monetization-strategy skill for paywall improvements
app-analytics — Full analytics stack setup and KPI frameworkmonetization-strategy — Improve subscription conversion and paywallretention-optimization — Reduce churn using the metrics as inputlocalization — Expand top-performing markets seen in country dataua-campaign — Validate whether paid installs show in downloads spike