Zencastr

Build me a focused remote podcast recording workflow for the personal core of Zencastr. Requirements:

- Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React. Model source media, edit decisions, processing jobs, and exported assets; keep stable source IDs and timestamps.
- Paid product context: Remote audio and video recording, editing, hosting, and distribution. Build only this DIY scope: Build a local remote podcast recording workflow that imports user-owned media, applies the core edit or transformation, previews it, and exports a standard file.
- For remote podcast recording, probe the user-owned source with FFmpeg and record stream, duration, and timebase metadata.
- Apply the requested edit to a working copy and record edit intervals in source time coordinates.
- Preview the transformed clip and export a standard file plus an edit manifest.
- Use a local web page with input, progress, review, and export views. Required input or access: FFmpeg; desktop with sufficient storage.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: The original hash must remain unchanged; cuts and captions must stay aligned after export despite a variable input frame rate.
- Out of scope: professional codecs, effects, and collaboration; production codecs, rendering speed, and media templates. 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 Zencastr

## Project scope
Build a local remote podcast recording workflow that imports user-owned media, applies the core edit or transformation, previews it, and exports a standard file.

Catalogue verdict: no. Do not mistake the interface for the product. Zencastr's durable value is media pipeline, hosting, distribution, which a solo one-shot build cannot reproduce responsibly. The prompt therefore builds only the closest honest personal consolation tool.
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
- Python 3.12
- FFmpeg
- desktop with sufficient storage
- Explicit README warning that this is a consolation build, not a production replacement

## Stack and architecture
- Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
- Model source media, edit decisions, processing jobs, and exported assets; keep stable source IDs and timestamps.
- Interface for this remote podcast recording workflow: a local web page with input, progress, review, and export views.

## Security and data integrity
- The original hash must remain unchanged; cuts and captions must stay aligned after export despite a variable input frame rate.
- Validate imported records and file paths; keep user data within the storage location named in the prompt.
- State the practical limit: professional codecs, effects, and collaboration.

## Agent implementation rules
- Project rule, data: Model source media, edit decisions, processing jobs, and exported assets; keep stable source IDs and timestamps.
- Project rule, behavior: For remote podcast recording, probe the user-owned source with FFmpeg and record stream, duration, and timebase metadata.
- Project rule, recovery: The original hash must remain unchanged; cuts and captions must stay aligned after export despite a variable input frame rate.

## 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 remote podcast recording operation with sample or user-owned data: For remote podcast recording, probe the user-owned source with FFmpeg and record stream, duration, and timebase metadata.
- Show a concrete result or export: Preview the transformed clip and export a standard file plus an edit manifest.
- State the practical limit: professional codecs, effects, and collaboration.

## Engineering roadmap
1. Phase 1, architecture and data: Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React. Model source media, edit decisions, processing jobs, and exported assets; keep stable source IDs and timestamps.
2. Phase 2, implement: For remote podcast recording, probe the user-owned source with FFmpeg and record stream, duration, and timebase metadata.
3. Phase 3, implement: Apply the requested edit to a working copy and record edit intervals in source time coordinates.
4. Phase 4, review and output: Preview the transformed clip and export a standard file plus an edit manifest.
5. Phase 5, recovery and acceptance: Verify this invariant with a saved fixture: The original hash must remain unchanged; cuts and captions must stay aligned after export despite a variable input frame rate. State the practical limit: professional codecs, effects, and collaboration.

## Paid-product capabilities outside this build
- professional codecs, effects, and collaboration
- production codecs, rendering speed, and media templates
- hosted recording reliability
- large-scale rendering
- distribution and podcast hosting

## Implementation prompt
Build me a focused remote podcast recording workflow for the personal core of Zencastr. Requirements:

- Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React. Model source media, edit decisions, processing jobs, and exported assets; keep stable source IDs and timestamps.
- Paid product context: Remote audio and video recording, editing, hosting, and distribution. Build only this DIY scope: Build a local remote podcast recording workflow that imports user-owned media, applies the core edit or transformation, previews it, and exports a standard file.
- For remote podcast recording, probe the user-owned source with FFmpeg and record stream, duration, and timebase metadata.
- Apply the requested edit to a working copy and record edit intervals in source time coordinates.
- Preview the transformed clip and export a standard file plus an edit manifest.
- Use a local web page with input, progress, review, and export views. Required input or access: FFmpeg; desktop with sufficient storage.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: The original hash must remain unchanged; cuts and captions must stay aligned after export despite a variable input frame rate.
- Out of scope: professional codecs, effects, and collaboration; production codecs, rendering speed, and media templates. 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.