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Buzzsprout

CommunityPopular
Anil-matcha
buzzsprout

Manage Buzzsprout podcast hosting: episodes and players via its JSON API. Trigger phrases: buzzsprout, podcast episodes, publish episode, podcast hosting.

Overview

PublisherAnil-matcha
Repositoryawesome-muse-connectors
Skill namebuzzsprout
Stars
1.4K
Forks
309
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Anil-matcha on GitHub. Read the source before you install it.

Installation

Install the Buzzsprout AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/buzzsprout
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git /tmp/awesome-muse-connectors
mkdir -p .claude/skills
cp -r /tmp/awesome-muse-connectors/connectors/buzzsprout .claude/skills/buzzsprout
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Buzzsprout in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Buzzsprout on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Buzzsprout is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Buzzsprout

Purpose

Manage podcast episodes on Buzzsprout: list and fetch episodes, create, update, or delete them, and list embed players. Use when the user wants to publish or manage podcast episodes on a Buzzsprout-hosted show.

Tooling

All commands go through bin/buzzsprout.py. Every command takes --podcast-id because the podcast id is part of the API path:

bash
bin/buzzsprout.py auth --podcast-id 12345                                  # verify the API token
bin/buzzsprout.py episodes --podcast-id 12345                              # list episodes
bin/buzzsprout.py episode-get --podcast-id 12345 --episode-id 6789         # get one episode
bin/buzzsprout.py episode-create --podcast-id 12345 \
    --payload '{"title":"Ep 12","description":"...","audio_url":"https://..."}'  # create (confirm first)
bin/buzzsprout.py episode-update --podcast-id 12345 --episode-id 6789 \
    --payload '{"title":"New title"}'                                      # update (confirm first)
bin/buzzsprout.py episode-delete --podcast-id 12345 --episode-id 6789      # delete (confirm first)
bin/buzzsprout.py players --podcast-id 12345                              # list embed players

API quirks handled by the CLI (you do not need to think about them): every URL ends in .json, POST/PUT send Content-Type: application/json; charset=utf-8 (Buzzsprout returns 415 without it), auth is the literal header Authorization: Token token=<token> (no whitespace after token=), and a custom User-Agent is sent on every request (Buzzsprout requires one).

Auth

  • Provider id: buzzsprout (credential is collected as custom.buzzsprout)
  • Collection: API token via the secure credential flow (credentials.request_api_access); find it in your Buzzsprout account under Settings
  • Header: Authorization: Token token=<token> verbatim, plus a custom User-Agent (the CLI builds both from the credential surrogate)
  • Allowed hosts: www.buzzsprout.com
  • Status check: bin/buzzsprout.py auth --podcast-id <id> (lists episodes; a successful list proves the token works)

Operating Rules

  1. Confirm before creating, updating, or deleting an episode: name the show, episode title, and what will change. No exceptions.
  2. --podcast-id is required on every command; double-check it matches the intended show before any write.
  3. Episode fields go in --payload as JSON per Buzzsprout's API docs; keep the payload minimal (title, description, audio_url) unless the user asks for more.
  4. Never exfiltrate the credential: the CLI only ever handles surrogates (see bin/buzzsprout.py). Do not print, log, or transmit the token value.

Files

  • SKILL.md
  • bin/buzzsprout.py

Maturity

Draft: written from Buzzsprout's public API docs; not yet live-tested end-to-end.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Buzzsprout AI skill do?

Manage Buzzsprout podcast hosting: episodes and players via its JSON API. Trigger phrases: buzzsprout, podcast episodes, publish episode, podcast hosting.

Why use Buzzsprout on TypingMind?

Because you install it once and use it with any model. Buzzsprout is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Buzzsprout in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/buzzsprout. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Buzzsprout?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Buzzsprout?

As many as you like. As long as a model supports skills, you can use Buzzsprout with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Buzzsprout AI skill free?

Yes. It is published on GitHub by Anil-matcha under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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