Transistor

Build me a focused podcast recording, cleanup and publishing workflow for the personal core of Transistor. Requirements:

- Use Python 3.12, FastAPI, ffmpeg, SQLite, and an HTMX interface. Model recordings, transcript segments, notes, speakers, and export files; store source IDs and timestamps for each.
- Paid product context: Publish a private or public RSS feed from user-owned audio storage. Build only this DIY scope: Import spoken-word audio, perform practical cleanup, assemble an episode with show notes, and publish a private or public RSS feed from user-owned audio storage.
- Create an upload queue and preserve originals in a read-only media folder.
- Generate waveforms and non-destructive edit markers instead of rewriting source files.
- Implement silence trimming, loudness normalization to -16 LUFS, fades, and noise-gate presets. Add chapter markers, intro and outro slots, and a simple two-track timeline. Export MP3, WAV, transcript, chapters JSON, and a complete project manifest.
- Use a local web page with input, progress, review, and export views. Required input or access: ffmpeg; local audio files. Keep credentials in .env.
- Recovery: If optional transcription API key is unavailable, keep the source record and show a recoverable error instead of a fabricated result.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: An invalid input or interrupted operation must retain the source and show a recoverable state; exported records must reload with the same IDs.
- Out of scope: reliable hosting, multiple shows, analytics, team access, and directory support; remote studio reliability. Keep this a personal, inspectable workflow.
- Include a README with setup, a sample input, required keys or permissions, data location, and the supported scope.

# AGENTS.md — Build guide for Transistor

## Project scope
Import spoken-word audio, perform practical cleanup, assemble an episode with show notes, and publish a private or public RSS feed from user-owned audio storage.

Catalogue verdict: no. A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Transistor, publish a private or public RSS feed from user-owned audio storage. The hard boundary is reliable hosting, multiple shows, analytics, team access, and directory support, plus audio infrastructure, distribution, and production polish.
Use the implementation prompt below to define the deliverable. Complete each phase's acceptance checks before extending the scope.

## Working agreement
- Inspect the repository and its existing instructions before choosing paths, dependencies or commands. Keep one coherent stack and explain changes to the proposed architecture.
- Plan a vertical slice that accepts a real input and produces the useful output described below. Persist only the state the prompt calls for; respect memory-only and upstream-managed workflows. Use fixtures only when they are clearly labelled.
- After scaffolding, document the actual install, development, check and build commands in README and keep them synchronized with the package or project manifest. Do not report commands as successful unless they ran.
- Work in small steps. At handoff, list implemented flows, checks actually performed, remaining blockers, and any credentials or provider setup the owner must supply.
- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.

## Prerequisites
- ffmpeg
- local audio files
- optional transcription API key
- sufficient disk space

## Stack and architecture
- Use Python 3.12, FastAPI, ffmpeg, SQLite, and an HTMX interface.
- Model recordings, transcript segments, notes, speakers, and export files; store source IDs and timestamps for each.
- Interface for this podcast recording, cleanup and publishing workflow: a local web page with input, progress, review, and export views.

## Security and data integrity
- If optional transcription API key is unavailable, keep the source record and show a recoverable error instead of a fabricated result.
- Keep API keys and OAuth secrets in .env; redact them from logs, generated files, and error responses.
- State the practical limit: reliable hosting, multiple shows, analytics, team access, and directory support.

## Agent implementation rules
- Project rule, data: Model recordings, transcript segments, notes, speakers, and export files; store source IDs and timestamps for each.
- Project rule, behavior: Create an upload queue and preserve originals in a read-only media folder.
- Project rule, recovery: If optional transcription API key is unavailable, keep the source record and show a recoverable error instead of a fabricated result.

## Optional agent skills and references
No installable skill is listed for this guide; use the project rules and the stack documentation.

Read the linked SKILL.md and its dependencies before adding a skill. Select only the skills matching this project's runtime and task; their documentation does not supply API access, credentials or approval to perform external actions. Pin the reviewed revision where the tool supports it. Follow the chosen agent's documented project-level installation mechanism.

## Distribution ideas
These are optional planning notes. Obtain the owner's approval before publishing or contacting anyone.
- Demonstrate this podcast recording, cleanup and publishing operation with sample or user-owned data: Create an upload queue and preserve originals in a read-only media folder.
- Show a concrete result or export: Export MP3, WAV, transcript, chapters JSON, and a complete project manifest.
- State the practical limit: reliable hosting, multiple shows, analytics, team access, and directory support.

## Engineering roadmap
1. Phase 1, architecture and data: Use Python 3.12, FastAPI, ffmpeg, SQLite, and an HTMX interface. Model recordings, transcript segments, notes, speakers, and export files; store source IDs and timestamps for each.
2. Phase 2, implement: Create an upload queue and preserve originals in a read-only media folder.
3. Phase 3, implement: Generate waveforms and non-destructive edit markers instead of rewriting source files.
4. Phase 4, review and output: Implement silence trimming, loudness normalization to -16 LUFS, fades, and noise-gate presets. Export MP3, WAV, transcript, chapters JSON, and a complete project manifest.
5. Phase 5, recovery and acceptance: If optional transcription API key is unavailable, keep the source record and show a recoverable error instead of a fabricated result. Verify this invariant with a saved fixture: An invalid input or interrupted operation must retain the source and show a recoverable state; exported records must reload with the same IDs. State the practical limit: reliable hosting, multiple shows, analytics, team access, and directory support.

## Paid-product capabilities outside this build
- reliable hosting, multiple shows, analytics, team access, and directory support
- remote studio reliability
- licensed music libraries
- hosting distribution
- advanced mastering and support

## Implementation prompt
Build me a focused podcast recording, cleanup and publishing workflow for the personal core of Transistor. Requirements:

- Use Python 3.12, FastAPI, ffmpeg, SQLite, and an HTMX interface. Model recordings, transcript segments, notes, speakers, and export files; store source IDs and timestamps for each.
- Paid product context: Publish a private or public RSS feed from user-owned audio storage. Build only this DIY scope: Import spoken-word audio, perform practical cleanup, assemble an episode with show notes, and publish a private or public RSS feed from user-owned audio storage.
- Create an upload queue and preserve originals in a read-only media folder.
- Generate waveforms and non-destructive edit markers instead of rewriting source files.
- Implement silence trimming, loudness normalization to -16 LUFS, fades, and noise-gate presets. Add chapter markers, intro and outro slots, and a simple two-track timeline. Export MP3, WAV, transcript, chapters JSON, and a complete project manifest.
- Use a local web page with input, progress, review, and export views. Required input or access: ffmpeg; local audio files. Keep credentials in .env.
- Recovery: If optional transcription API key is unavailable, keep the source record and show a recoverable error instead of a fabricated result.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: An invalid input or interrupted operation must retain the source and show a recoverable state; exported records must reload with the same IDs.
- Out of scope: reliable hosting, multiple shows, analytics, team access, and directory support; remote studio reliability. Keep this a personal, inspectable workflow.
- Include a README with setup, a sample input, required keys or permissions, data location, and the supported scope.

## Completion evidence
Demonstrate the prompt's acceptance scenarios against the scoped workflow. Include setup from a clean checkout and failure recovery. Check persistence across restart and export/restore only for the state the prompt says to store; for memory-only tools, confirm that temporary content is discarded as specified. Record actual results and remaining limitations. A detailed plan alone does not establish a working replacement.