podcast-content-analyzer

podcast-content-analyzer is an agent for Claude Code from davepoon/buildwithclaude. It costs 35 tokens per session (208 once invoked), scanned A, original, MIT.

A podcast transcript analyzer that finds memorable, informative, or highly shareable parts of a long recording. It can score segments and attach timestamps, keywords, chapters, and clip suggestions.

In plain words
What is it for?
Use it to identify strong moments, create chapter breakdowns, extract quotable lines and SEO keywords, and suggest social media clips.
Why use it?
It helps podcast teams decide which parts of an episode deserve chapters, short clips, quotes, or search optimization.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agents-media-content plugin — 11 agents shipped together

Good fit Use it to identify strong moments, create chapter breakdowns, extract quotable lines and SEO keywords, and suggest social media clips.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/davepoon/buildwithclaude/podcast-content-analyzer
About the project

davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.

davepoon/buildwithclaude · 3,421 stars · on GitHub · buildwithclaude.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/davepoon/buildwithclaude

Made for: Claude Code.

Or install agents-media-content, the plugin that ships this one along with the rest of its 11 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for podcast-content-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/agents/davepoon/buildwithclaude/podcast-content-analyzer.svg)](https://agentmods.dev/agents/davepoon/buildwithclaude/podcast-content-analyzer)
Your own site
<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/podcast-content-analyzer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/podcast-content-analyzer.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 208 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.00208
Opus 5 $0.00017 $0.00104
Sonnet 5 $0.00007 $0.00042
Haiku 4.5 $0.00003 $0.00021

Measured 4d ago against content hash 1901bba21934, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

podcast-content-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/agents-media-content/agents/podcast-content-analyzer.md · 34 lines

What it actually says

You are a content analysis expert specializing in podcast and long-form content production.

When invoked:

  1. Analyze transcript for engagement potential
  2. Identify viral moments and quotable segments
  3. Score content based on shareability
  4. Create chapter markers with timestamps
  5. Extract keywords for SEO optimization
  6. Suggest social media clips

Process:

  • Evaluate emotional impact and story arcs
  • Identify educational or informational value
  • Find unique perspectives and insights
  • Assess platform-specific requirements
  • Score segments for engagement potential
  • Consider audience demographics

Provide:

  • Viral moment timestamps with scores
  • Chapter breakdown with descriptions
  • Top quotable segments
  • SEO keyword recommendations
  • Social media clip suggestions
  • Content improvement insights
  • Engagement optimization tips

Focus on identifying high-impact content for maximum audience engagement.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 34 lines · 35 tokens per session scan A 1901bba21934

Subscribe to this mod's changes

podcast-content-analyzer is an agent published in the GitHub repository davepoon/buildwithclaude (3,421 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 208 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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