# AGENTS.md — Build guide for Masterchannel

## Project scope
Analyze a user-owned mix and licensed reference, preview conservative tonal/loudness adjustments and export a chosen master with measurements.

Catalogue verdict: kinda. Mastering is signal processing, and the open source world already solved a big chunk of it: reference matching, loudness normalization and true peak limiting are all off the shelf. An agent can wire matchering, pyloudnorm and ffmpeg into a local CLI that takes your mix plus a commercial reference and spits out a competitive master in an afternoon. What it cannot do is decide, with no reference, what your track should sound like: that judgment is the part these services trained on thousands of masters to fake. So the DIY build is genuinely usable if you already know which records you want to sound like, and mediocre if you don't. Also expect to babysit sample rates, mono compatibility and the occasional inter-sample peak.
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- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.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
- Runtime and tools: Python, FastAPI, SQLite, FFmpeg/ffprobe and a React review interface.
- Before starting: Installed FFmpeg/ffprobe, writable media storage and a short recording whose use is authorized.

## Stack and architecture
- Python, FastAPI, SQLite, FFmpeg/ffprobe and a React review interface
- Data design: Store MixSource, ReferenceSource, Analysis, ProcessingChain and Render; reference matching settings never alter the source and output measurements record the exact rendered file.
- Setup: Installed FFmpeg/ffprobe, writable media storage and a short recording whose use is authorized

## Security and data integrity
- Keep source files immutable and store edit decisions separately. Run media tools with argument arrays, bounded file size/runtime and unique job directories; only rename a completed export into its final location.
- Start with explicit EQ/loudness controls before automated matching. Reference rights, monitoring quality and subjective mastering judgment remain the user's responsibility; no guaranteed professional result.
- Keep secrets outside client bundles and exported projects; document what leaves the device and make retention/deletion controls visible.

## Agent implementation rules
- Project rule — domain: Store MixSource, ReferenceSource, Analysis, ProcessingChain and Render; reference matching settings never alter the source and output measurements record the exact rendered file.
- Project rule — scope and recovery: Start with explicit EQ/loudness controls before automated matching. Reference rights, monitoring quality and subjective mastering judgment remain the user's responsibility; no guaranteed professional result.
- Project rule — acceptance: Use a quiet mix with a loud transient; compare bypass and processed audio, detect clipping and refuse to label an unmeasured export as meeting a target ceiling.
- Project rule — delivery: document real setup commands and permissions; do not claim a build, accuracy level, performance result or security certification that has not been demonstrated.

## Optional agent skills and references
- Recommended skill: [modern-python](https://github.com/trailofbits/skills/blob/main/plugins/modern-python/skills/modern-python/SKILL.md) — structure the Python worker or explicitly optional read-only utility with pinned dependencies, typed boundaries and clear failure handling. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.
- Recommended skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — keep the proposed React work/review views responsive and avoid unnecessary rendering or data-fetch waterfalls. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.

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 working slice using synthetic or explicitly authorized non-sensitive examples: Analyze a user-owned mix and licensed reference, preview conservative tonal/loudness adjustments and export a chosen master with measurements.
- Share a synthetic example export and the acceptance walkthrough; keep real customer, health, financial and source data private: Use a quiet mix with a loud transient; compare bypass and processed audio, detect clipping and refuse to label an unmeasured export as meeting a target ceiling.
- State the limits before asking someone to replace their existing tool: Start with explicit EQ/loudness controls before automated matching. Reference rights, monitoring quality and subjective mastering judgment remain the user's responsibility; no guaranteed professional result.

## Engineering roadmap
1. Phase 1 — Pin the working slice and create its example input: Analyze a user-owned mix and licensed reference, preview conservative tonal/loudness adjustments and export a chosen master with measurements. Confirm setup: Installed FFmpeg/ffprobe, writable media storage and a short recording whose use is authorized.
2. Phase 2 — Implement persistence and write-time invariants before decorating the UI: Store MixSource, ReferenceSource, Analysis, ProcessingChain and Render; reference matching settings never alter the source and output measurements record the exact rendered file.
3. Phase 3 — Connect the working view to real saved state. Keep source files immutable and store edit decisions separately. Run media tools with argument arrays, bounded file size/runtime and unique job directories; only rename a completed export into its final location.
4. Phase 4 — Expose the app-specific limits and recovery path in context: Start with explicit EQ/loudness controls before automated matching. Reference rights, monitoring quality and subjective mastering judgment remain the user's responsibility; no guaranteed professional result.
5. Phase 5 — Walk through this concrete acceptance case and preserve its exported evidence: Use a quiet mix with a loud transient; compare bypass and processed audio, detect clipping and refuse to label an unmeasured export as meeting a target ceiling. Finish the README and backup/restore instructions; report unfinished capabilities explicitly.

## Paid-product capabilities outside this build
- Reference-free mastering: the service guesses a target for you, your script needs you to pick one
- Genre-aware presets and the taste baked into a trained model
- Stem mastering, vocal-forward variants and other per-track intelligence
- A clean web UI with instant previews and revision history
- Anything resembling a second opinion when your mix is the actual problem

## Implementation prompt
Build the following focused alternative to Masterchannel. This is a deliberately limited personal or small-team substitute, not parity with the paid service.

WORKING SLICE
Analyze a user-owned mix and licensed reference, preview conservative tonal/loudness adjustments and export a chosen master with measurements.

SETUP AND ARCHITECTURE
Use Python, FastAPI, SQLite, FFmpeg/ffprobe and a React review interface. Prerequisites: Installed FFmpeg/ffprobe, writable media storage and a short recording whose use is authorized. Before integrating anything, record actual versions and permissions, plus model files or provider limits only where used, in the README; make unavailable dependencies visible rather than simulating success.

DOMAIN MODEL AND INVARIANTS
Store MixSource, ReferenceSource, Analysis, ProcessingChain and Render; reference matching settings never alter the source and output measurements record the exact rendered file.

IMPLEMENTATION CONTRACT
Keep source files immutable and store edit decisions separately. Run media tools with argument arrays, bounded file size/runtime and unique job directories; only rename a completed export into its final location. Provide an input/setup view, the main work view, and a review/export view appropriate to this workflow. Preserve the last saved state if a job or save fails. Include empty, loading, permission-denied, partial and retryable-error states. Log identifiers and error categories without secret values or unnecessary private content.

APP-SPECIFIC BOUNDARY AND RECOVERY
Start with explicit EQ/loudness controls before automated matching. Reference rights, monitoring quality and subjective mastering judgment remain the user's responsibility; no guaranteed professional result.

ACCEPTANCE SCENARIO
Use a quiet mix with a loud transient; compare bypass and processed audio, detect clipping and refuse to label an unmeasured export as meeting a target ceiling. Also reopen the app after an interrupted operation, confirm the saved record/export remains inspectable, and document the recovery action. These are implementation acceptance requirements, not a claim that this guide has been tested.

DELIVERY
Deliver a runnable repository with migrations or project-format versioning, a non-sensitive example, environment/permission setup, the exact manual acceptance steps, and a backup/export-and-restore walkthrough. Implement the working slice before optional integrations; list any deferred paid-product capabilities honestly. Do not add capabilities outside the working slice just to resemble the original product.

PROJECT RULES FOR AGENTS.md
Keep the domain invariants above executable at the write boundary. Propose scope changes before adding providers or permissions. Never fabricate source evidence, publish results, identity matches or successful delivery. Preserve user originals and require an explicit confirmation for destructive changes or external publication.

## 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.