Ai PodcastSAFE

inference.sh Agent skills for using our API to give your agents access to hundreds of apps and other agents

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: ai-podcast
description: "Generate multi-person talking head podcast videos from scratch using AI — character creation, TTS, avatar animation, and video stitching. Use when the user wants to create a podcast, talking head video, or multi-speaker conversation video."
allowed-tools: Bash, Read, Write, Agent, Glob, Grep
---

## AI Podcast Generator

Create multi-person talking head podcast videos using the inference.sh pipeline: portrait generation → TTS audio → avatar video → merge. Supports real humans (via Phota), 3D mascots, illustrated characters, and mixed casts.

Use when the user wants to create a podcast, talking head video, demo reel, promotional conversation, or any multi-speaker video content.

### Pipeline Overview

```
Characters (images) → TTS (audio per turn) → Avatar (video per turn) → Merge (final video)
```

### Process

#### Step 1: Character Creation

Choose the right tool per character type:

| Character Type | Tool | Notes |
|---------------|------|-------|
| **Real human (new)** | `pruna/p-image` | 16:9, `prompt_upsampling: true`. Quick, no training needed, but identity won't be consistent across multiple generations. |
| **Real human (consistent ID)** | `phota/generate` with `[[profile_id]]` | Consistent identity across all shots. Requires a trained Phota profile first (see below). |
| **Brand mascot / logo character** | `google/gemini-3-pro-image` | Pass logo + character sheet as reference images |
| **Illustrated / stylized** | `google/gemini-3-pro-image` | Pass style reference as input image |

**Training a Phota identity (optional but recommended for humans):**

If you need a real human character with consistent identity across multiple angles and shots, train a Phota profile first:

```bash
belt app run phota/train --input '{
  "images": ["url1.jpg", "url2.jpg", ...],
  "wait": true
}' --save profile.json
```

- Requires 30-50 face images of the subject
- Training takes a few minutes with `wait: true`
- Returns a `profile_id` you then use in `phota/generate` as `[[profile_id]]` in prompts
- The profile is reusable forever — train once, generate unlimited shots

If you don't need cross-shot consistency (e.g. single-speaker video, one angle only), `pruna/p-image` is simpler and cheaper.

**Character sheets first, podcast frames second:**
1. Generate a character sheet (plain white background, multiple angles) for each character
2. Then place characters into the podcast studio setting using the sheet as reference

**For branded characters (logo on clothing):**
1. Generate the character with a plain version of the garment
2. Use `phota/edit` with the logo as a second reference image to add the logo
3. Always pass the logo image alongside character references when generating new angles

#### Step 2: Alternate Angles

Generate at least 2 angles per character for visual variety:

| Angle | When to use |
|-------|-------------|
| **Front/medium** | Establishing shots, opening, closing |
| **Close-up** | Reactions, emotional moments, punchy lines |

For close-ups, prompt for "tight framing, chest up, shallow depth of field" — not "turned to the side" (which just makes them look away).

**Identity consistency rules:**
- For real humans with a Phota profile: use `phota/generate` or `phota/edit` for new angles — Gemini does not preserve facial identity and will produce a different person
- For real humans without a Phota profile: try to generate all needed angles in one go with `pruna/p-image`, or consider training a Phota profile if you need many shots
- For mascots/illustrations: Gemini 3 Pro is fine, pass the established frame as reference

**Framing rule:** Use tight framing on individual speakers. Wide shots with multiple seats show empty chairs when only one person is on screen.

#### Step 3: QA Frames

Before proceeding, visually inspect all frames for:
- Extra people in the background
- Multiple microphones (should be single mic per shot)
- Wrong or distorted logos
- Inconsistent character identity across angles
- Weird artifacts (extra limbs, merged objects)

Fix issues before generating video — re-rendering video is the most expensive step in the pipeline.

#### Step 4: Write the Script

**Rules for natural conversation:**
- Write it like a real conversation, NOT like people reading ad copy in turns
- Include reactions ("wait, hold on", "that is wild"), interruptions, and follow-up questions
- Vary turn length — short reactions (1 sentence) mixed with longer explanations (2-3 sentences)
- The host should ask real questions, not set up obvious talking points
- Keep total duration target in mind: ~2.5 words/second for natural speech at 1.05x rate

**Duration guide:**
| Target | Words |
|--------|-------|
| 15s | ~38 words |
| 30s | ~75 words |
| 60s | ~150 words |

#### Step 5: Generate TTS Audio

Use `inworld/text-to-speech-2` for each turn.

```bash
belt app run inworld/text-to-speech-2 --input '{
  "text": "...",
  "voice_id": "...",
  "speaking_rate": 1.05,
  "audio_encoding": "MP3"
}' --save output.json
```

**Voice selection:**
- Generate samples with the same line across candidate voices BEFORE committing
- Let the user listen and approve voices
- Good podcast voices: Tyler, Nate, Lauren, Kelsey, Naomi, Anjali (EN_US)
- Use `inworld/text-to-speech-2:voices` to list all available voices

**Speaking rate:**
- Default to **1.05** for natural podcast pacing
- Use **1.1** for short snappy reactions
- **NEVER go below 1.0** — sounds slow and disengaging
- Keep rate consistent per character across all their turns

All TTS turns can run in parallel (cheap, fast ~2-8s each).

#### Step 6: Generate Video Clips

Use `pruna/p-video-avatar` for each turn.

```bash
belt app run pruna/p-video-avatar --input '{
  "image": "<character_frame_url>",
  "audio": "<tts_audio_url>",
  "resolution": "720p",
  "video_prompt": "..."
}' --save output.json
```

**Critical: Run clips SEQUENTIALLY, not in parallel.** Parallel runs hit the same GPU and cause CUDA OOM failures. Eac

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