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podcast-analytics-expert播客分析专家

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

10,108

周安装

442

GitHub Stars

10

下载量

4,099
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:podcast-analytics-expert(播客分析专家)
来源仓库:https://github.com/willsigmon/sigstack
仓库路径:skills/podcast-analytics-expert
安装命令:
npx skills add https://github.com/willsigmon/sigstack --skill 'Podcast Analytics Expert'
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/willsigmon/sigstack --skill 'Podcast Analytics Expert'

简介

用于辅助数据整理、CSV/Excel 分析和指标计算。

  • 适合清洗字段、汇总数据、发现异常或生成统计口径。
  • 使用时需确认数据来源和时间范围,避免将样本当全量事实。
  • 涉及敏感数据时应先确认脱敏边界和导出权限。
  • 可用于图表准备和分析结果的可读化转述。podcast-analytics-expert 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Podcast Analytics Expert

Understand your audience and grow your podcast.

Analytics Sources

SourceDataAccess
Hosting PlatformDownloads, geo, devicesAPI
Spotify for PodcastersStreams, retention, demographicsDashboard
Apple Podcasts ConnectFollowers, plays, devicesDashboard/API
YouTubeViews, watch time, subsAPI
Op3Open analytics, prefixFree

Key Metrics

Downloads

  • Downloads: Total file requests
  • Unique listeners: Deduplicated by IP/device
  • Per episode: Performance comparison
  • Trend: Week-over-week growth

Engagement

  • Completion rate: % who finish episodes
  • Drop-off points: Where listeners stop
  • Average consumption: Minutes per listener
  • Retention curve: Listener loyalty over time

Audience

  • Geographic: Countries, cities
  • Devices: iOS vs Android vs Desktop
  • Apps: Which podcast apps
  • Demographics: Age, gender (Spotify only)

Transistor Analytics API

# Get show analytics
curl https://api.transistor.fm/v1/analytics/shows/123 \
  -H "x-api-key: YOUR_API_KEY"

# Episode analytics
curl "https://api.transistor.fm/v1/analytics/episodes/456?start_date=2026-01-01" \
  -H "x-api-key: YOUR_API_KEY"

Response

{
  "data": {
    "downloads": 1234,
    "unique_listeners": 890,
    "countries": {
      "US": 450,
      "UK": 120,
      "CA": 80
    }
  }
}

OP3 (Open Podcast Prefix Project)

FREE, open-source, privacy-focused analytics. 17M+ downloads/month tracked.

Setup

Prepend prefix to your RSS enclosure URLs:

<enclosure url="https://op3.dev/e/https://your-host.com/episode.mp3"/>

Query Downloads

curl "https://op3.dev/api/1/shows/YOUR_SHOW_ID/downloads?start=2026-01-01"

Why OP3?

  • Completely free forever
  • Open data (public API)
  • Privacy-focused (no user tracking)
  • Works with any podcast host
  • Great for indie podcasters

Spotify for Podcasters API

Get Episode Performance

# Spotify doesn't have public API for podcasters
# Use dashboard exports or third-party tools

# Chartable, Podtrac for cross-platform analytics

Apple Podcasts Connect

Analytics Export

import requests

# Apple Podcasts Connect API (requires JWT auth)
# See: https://developer.apple.com/documentation/appstoreconnectapi

headers = {"Authorization": f"Bearer {jwt_token}"}
response = requests.get(
    "https://api.appstoreconnect.apple.com/v1/analyticsReportRequests",
    headers=headers
)

Building a Dashboard

Python Analytics Aggregator

from dataclasses import dataclass
from datetime import date

@dataclass
class PodcastMetrics:
    date: date
    downloads: int
    unique_listeners: int
    avg_completion: float
    top_countries: dict

async def aggregate_metrics(show_id: str, start: date, end: date):
    # Fetch from multiple sources
    transistor = await fetch_transistor(show_id, start, end)
    op3 = await fetch_op3(show_id, start, end)

    return PodcastMetrics(
        date=end,
        downloads=transistor.downloads + op3.downloads,
        unique_listeners=transistor.unique_listeners,
        avg_completion=transistor.avg_completion,
        top_countries=transistor.countries
    )

Growth Benchmarks

Indie Podcast (Monthly Downloads)

  • Starting out: 50-100
  • Growing: 500-1,000
  • Established: 5,000-10,000
  • Popular: 50,000+

Retention Benchmarks

  • Excellent: 80%+ completion
  • Good: 60-80%
  • Average: 40-60%
  • Needs work: <40%

Actionable Insights

Episode Length

Analyze completion rates by episode length.
If 60min episodes have 40% completion but 30min have 70%,
consider shorter episodes.

Release Timing

Track downloads by day of week and time.
Optimize release schedule for your audience's habits.

Content Analysis

Compare episode metrics to topics.
What topics drive the highest engagement?

Tools for Vibe Coders

Chartable

  • Cross-platform analytics
  • Attribution tracking
  • SmartLinks

Podtrac

  • Industry-standard measurement
  • IAB certified
  • Free tier available

Spotify for Podcasters

  • Deep Spotify insights
  • Free
  • Spotify-only data

Use when: Podcast growth, audience analysis, content optimization, multi-platform tracking

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

34.65%
按下载量换算1,420

Claude

31.63%
按下载量换算1,297

Cursor

18.27%
按下载量换算749

Gemini CLI

8.3%
按下载量换算340

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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