Compare Shows logo

Compare Shows

Organization
arcmira
compare-shows

Compares two podcasts or YouTube shows side by side: size, latest episode, what each talks about, what both cover, and shared sponsors. Uses arcmira.

Overview

Publisherarcmira
Repositorymcp
Skill namecompare-shows
Stars
78
Forks
0
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by arcmira on GitHub. Read the source before you install it.

Installation

Install the Compare Shows 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/arcmira/mcp/tree/master/plugins/arcmira/skills/compare-shows
  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/arcmira/mcp.git /tmp/mcp
mkdir -p .claude/skills
cp -r /tmp/mcp/plugins/arcmira/skills/compare-shows .claude/skills/compare-shows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compare Shows 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 Compare Shows 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 Compare Shows 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.

Compare two shows

status sizes each show, episodes gives the latest, occurrences with both channel ids ranks what each covers and returns shared for what both mention, and sponsors of each, joined on entity id, gives shared sponsors.

Run each program with arcmira_execute_read; arcmira_describe has the method reference.

When to use

  • compare two shows, or a show against a competitor
  • what two podcasts both talk about, or who they both advertise
  • a digest of what one or two shows covered this week or month

Pick the entity the user meant

Users give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws id_required.

  1. Resolve the exact name the user said, with their own words about it as context (a bare "Theo" has none).
  2. best and suggested give the id. A suggested pick is an assumption: the answer names it, with suggested.evidence as the reason.
  3. ask: several entities fit, and ask.options goes back to the user. No answer at all: the name is not in the index.

For this task:

  • Resolve each show with { type: "channel" } and use youtube_channel_id. Users shorten show names ("All In", "MTS"); resolve suggests a show by its initials or closest spelling, so say when a show was assumed. When nothing comes back, retry the full or hyphenated name before saying the show is not in the index.
  • Pass both UC ids in one occurrences call and read shared.

Worked program

Pass each block to arcmira_execute_read as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set CONTEXT to the user's own words about the name. When several entities fit it returns ask and runs nothing else. Show those options to the user, then run it again with ID set to the pick. Build date windows from arcmira.daysAgo(n) and arcmira.today().

Two shows, last 90 days

javascript
const SHOWS = ["TBPN", "All-In Podcast"], IDS = [null, null];   // after an ask, put the picked option's channel_id in IDS and run again
const ids = [], names = [], assumed = [];
for (const [i, n] of SHOWS.entries()) {
  if (IDS[i]) { ids.push(IDS[i]); names.push(n); assumed.push(null); continue; }
  const r = await arcmira.resolve(n, { type: "channel" });
  const e = r.best ?? r.suggested;
  if (!e) return { unresolved: n, ask: r.ask && { question: r.ask.question, options: r.ask.options.map(o => ({ ...o, channel_id: r.candidates.find(c => c.id === o.id)?.youtube_channel_id ?? null })) } };
  ids.push(e.youtube_channel_id);
  names.push(e.name);
  assumed.push(r.suggested ? r.suggested.evidence : null);
}
const after = arcmira.daysAgo(90);
const [s0, s1, e0, e1, occ, sp0, sp1] = await Promise.all([
  arcmira.status({ channelId: ids[0] }), arcmira.status({ channelId: ids[1] }),
  arcmira.episodes(ids[0], { limit: 1 }), arcmira.episodes(ids[1], { limit: 1 }),
  arcmira.occurrences({ channelIds: ids, types: ["organization", "product"], after, limit: 40 }),
  arcmira.sponsors(ids[0]), arcmira.sponsors(ids[1]),
]);
const inOther = new Map(sp1.sponsors.map(x => [x.entity.id, x.ad_reads]));
return {
  window: occ.window,
  shows: ids.map((id, i) => ({ name: names[i], channel_id: id, assumed: Boolean(assumed[i]), why: assumed[i], videos_indexed: [s0, s1][i].channel.searchable_videos, indexed_through: [s0, s1][i].channel.indexed_through, latest: [e0, e1][i].episodes[0]?.title ?? null,
    top: occ.rows.filter(x => x.channel_id === id).slice(0, 5).map(x => [x.name, x.count]) })),
  both_discussed: occ.shared.slice(0, 5).map(x => ({ name: x.name, id: x.entity_id, episodes_by_show: x.by_channel.map(c => [c.channel_name, c.count]) })),
  shared_sponsors: sp0.sponsors.filter(x => inOther.has(x.entity.id)).map(x => ({ name: x.entity.name, id: x.entity.id, ad_reads: [x.ad_reads, inOther.get(x.entity.id)] })),
};

A good answer

  • Names both shows as resolved, with their channel ids.
  • A side-by-side: videos indexed and indexed_through for each, the latest episode, and each show's top subjects with episode counts.
  • What both covered, from shared, with episodes per show, and the shared sponsors with ad reads per show, for a stated window.

Traps

  • With two or more channelIds, occurrences puts the overlap in shared; rows alone never shows it.
  • Never count episodes to size a show; status({ channelId }).channel.searchable_videos is the count.
  • An empty shared in a short window is an answer (no overlap in the window), not an error; widen the window only if the user asked for a longer one.

Docs: https://arcmira.com/docs/mcp-server

After the answer

Monitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The company-watch skill sets one up.

If anything was wrong, slow, or missing for the user, or they want a source or capability Arcmira lacks, send one arcmira_feedback.

Frequently asked questions

What does the Compare Shows AI skill do?

Compares two podcasts or YouTube shows side by side: size, latest episode, what each talks about, what both cover, and shared sponsors. Uses arcmira.

Why use Compare Shows on TypingMind?

Because you install it once and use it with any model. Compare Shows 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 Compare Shows in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/arcmira/mcp/tree/master/plugins/arcmira/skills/compare-shows. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Compare Shows?

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 Compare Shows?

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

Is the Compare Shows AI skill free?

It is published on GitHub by arcmira. Check the repository for licensing terms. 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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