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fix: resolve cross-file consistency issues and doc gaps (#5)
- standardize the chapters artifact on chapters.md everywhere; the
post-production workflow wrote chapters.txt while the YouTube publish
workflow looked for chapters.md
- point the launcher's "pi is not installed" error at npm install and
the renamed @earendil-works/pi-coding-agent package
- reduce /phase1 to a thin alias for /full-review and drop it from the
startup widget (also dedupe /cut-clips)
- give the "no transcription backend found" error actionable uv sync
--group commands
- document the OAuth test-user step in the README, testing-mode refresh
token expiry in the publisher skill, uv first-run venv behavior, and
the non-macOS scanner skip rule (AGENTS.md + skill)
- move /login guidance to after first launch in Quick start; note that
Common workflows commands are reference, not required UX
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Copy file name to clipboardExpand all lines: README.md
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@@ -35,10 +35,8 @@ brew install uv ffmpeg
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Notes:
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-`ffmpeg` is used for fixtures, transcription backends, and clip cutting.
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-`uv` runs the Python scripts and optional transcription dependency groups.
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- The video scanner is macOS-only and uses Swift / AVFoundation / Vision.
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Before first use, authenticate pi with `/login` or your usual provider API key setup.
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-`uv` runs the Python scripts and optional transcription dependency groups. The first `uv` command creates a local `.venv/` and may download Python automatically — this is normal and only happens once.
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- The video scanner is macOS-only and uses Swift / AVFoundation / Vision. Everything else works cross-platform (Linux: install `uv` and `ffmpeg` via your package manager).
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Set up a real transcription backend when you are ready to transcribe episodes:
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./podguy
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```
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On first launch, type `/login` inside pi to connect your model provider (or use your usual API key setup).
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Then ask pi for a concrete episode task:
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```text
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## Common workflows
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You don't need to memorize these — pi runs them for you when you ask in natural language. They're here for reference and debugging.
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### Scan a video
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```bash
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### Publish to YouTube
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One-time setup: create a Google Cloud project with the YouTube Data API v3 enabled, create a Desktop-app OAuth client, save the JSON to `~/.config/podguy/youtube/client_secret.json`, then authenticate:
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One-time setup: create a Google Cloud project with the YouTube Data API v3 enabled, create a Desktop-app OAuth client, and save the downloaded JSON to `~/.config/podguy/youtube/client_secret.json`. On the OAuth consent screen, keep the app in Testing mode and add your own Google account as a test user — otherwise the auth flow fails with `access_denied`. Then authenticate:
Copy file name to clipboardExpand all lines: src/podguy-post-production/SKILL.md
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@@ -63,7 +63,7 @@ For synthetic or test fixture inputs, default to a leaner evaluation mindset and
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### Quick pass
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1. Start from the input media file. It can be audio or video as long as `scripts/transcribe_video.py` can process it.
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2. If the input is video, optionally run the visual scanner to find likely interstitials and other non-host inserts. Skip this for audio-only inputs.
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2. If the input is video and you are on macOS, optionally run the visual scanner to find likely interstitials and other non-host inserts. Skip this for audio-only inputs and on non-macOS systems (say why when skipping).
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3. Run the transcript tool on the same media file.
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4. Prepare deterministic transcript artifacts for pi with `scripts/prepare_transcript_analysis.py`.
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