Vibecode Masterchannel
track this build5 steps, step by step0%KINDA · weekend project
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Masterchannel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Masterchannel indie build ## Goal Build the smallest trustworthy replacement for the core Masterchannel workflow for one developer or a tiny team. ## Scope A local CLI that analyzes a reference master, matches your mix's spectrum and dynamics to it, then normalizes to a chosen LUFS target with a true peak ceiling and exports streaming-ready files. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Masterchannel instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local command line audio mastering tool in Python 3.11. No web UI, no accounts, no cloud, no telemetry. Stack: Python 3.11, matchering for reference matching, pyloudnorm for loudness measurement, soundfile and numpy for IO and DSP, ffmpeg via subprocess for format conversion and MP3 export. Use a venv and a requirements.txt. No Docker. CLI, using argparse: master --input mix.wav --reference ref.wav --target-lufs -14 --true-peak -1.0 --out ./out Behavior: 1. Decode input and reference to 44.1kHz stereo 32-bit float WAV in a temp dir using ffmpeg, accepting wav, aiff, flac and mp3 inputs. 2. Print a before report for the input: integrated LUFS, loudness range, sample peak, estimated true peak, stereo correlation, and a rough 8-band spectral balance in dB. 3. Run matchering to match the input's spectrum and dynamics to the reference. 4. Apply a final loudness stage: measure integrated LUFS with pyloudnorm, apply gain to hit --target-lufs, then apply a simple lookahead true peak limiter (5ms lookahead, 50ms release) so estimated true peak never exceeds --true-peak. 5. Print an after report in the same format as the before report, plus the total gain applied and the number of limiter gain reduction events. 6. Export three files to --out: master_24bit.wav at 44.1kHz, master_16bit.wav dithered with TPDF, and master.mp3 at 320kbps via ffmpeg. 7. Write report.json next to them with all measured values for both stages. Also support --no-reference, which skips matchering and does loudness plus limiting only. Print a warning that this is a normalizer, not a master. Safety: never clip. Refuse to run and exit non-zero if the input is already above -6 LUFS integrated or if its estimated true peak is above 0.0 dBTP, with a message telling the user to export a quieter mix with headroom. Include a README with install steps, one worked example, and a short note that the reference track choice matters more than any flag. Ship three pytest tests: gain math hits the LUFS target within 0.3 dB, the limiter never exceeds the ceiling on a synthetic clipping signal, and the headroom guard exits non-zero. No stems, no batch mode, no plugin hosting, no genre presets. ## Required capabilities - Python 3.11 and ffmpeg installed locally - At least one commercial reference track in a lossless format - Headphones or monitors you actually trust - Basic willingness to A/B and re-run with different references ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Masterchannel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Masterchannel indie build ## Goal Build the smallest trustworthy replacement for the core Masterchannel workflow for one developer or a tiny team. ## Scope A local CLI that analyzes a reference master, matches your mix's spectrum and dynamics to it, then normalizes to a chosen LUFS target with a true peak ceiling and exports streaming-ready files. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Masterchannel instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local command line audio mastering tool in Python 3.11. No web UI, no accounts, no cloud, no telemetry. Stack: Python 3.11, matchering for reference matching, pyloudnorm for loudness measurement, soundfile and numpy for IO and DSP, ffmpeg via subprocess for format conversion and MP3 export. Use a venv and a requirements.txt. No Docker. CLI, using argparse: master --input mix.wav --reference ref.wav --target-lufs -14 --true-peak -1.0 --out ./out Behavior: 1. Decode input and reference to 44.1kHz stereo 32-bit float WAV in a temp dir using ffmpeg, accepting wav, aiff, flac and mp3 inputs. 2. Print a before report for the input: integrated LUFS, loudness range, sample peak, estimated true peak, stereo correlation, and a rough 8-band spectral balance in dB. 3. Run matchering to match the input's spectrum and dynamics to the reference. 4. Apply a final loudness stage: measure integrated LUFS with pyloudnorm, apply gain to hit --target-lufs, then apply a simple lookahead true peak limiter (5ms lookahead, 50ms release) so estimated true peak never exceeds --true-peak. 5. Print an after report in the same format as the before report, plus the total gain applied and the number of limiter gain reduction events. 6. Export three files to --out: master_24bit.wav at 44.1kHz, master_16bit.wav dithered with TPDF, and master.mp3 at 320kbps via ffmpeg. 7. Write report.json next to them with all measured values for both stages. Also support --no-reference, which skips matchering and does loudness plus limiting only. Print a warning that this is a normalizer, not a master. Safety: never clip. Refuse to run and exit non-zero if the input is already above -6 LUFS integrated or if its estimated true peak is above 0.0 dBTP, with a message telling the user to export a quieter mix with headroom. Include a README with install steps, one worked example, and a short note that the reference track choice matters more than any flag. Ship three pytest tests: gain math hits the LUFS target within 0.3 dB, the limiter never exceeds the ceiling on a synthetic clipping signal, and the headroom guard exits non-zero. No stems, no batch mode, no plugin hosting, no genre presets. ## Required capabilities - Python 3.11 and ffmpeg installed locally - At least one commercial reference track in a lossless format - Headphones or monitors you actually trust - Basic willingness to A/B and re-run with different references ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Masterchannel. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Masterchannel product brief ## Problem 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. ## Product outcome A local CLI that analyzes a reference master, matches your mix's spectrum and dynamics to it, then normalizes to a chosen LUFS target with a true peak ceiling and exports streaming-ready files. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and ffmpeg installed locally - At least one commercial reference track in a lossless format - Headphones or monitors you actually trust - Basic willingness to A/B and re-run with different references ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a local command line audio mastering tool in Python 3.11. No web UI, no accounts, no cloud, no telemetry. Stack: Python 3.11, matchering for reference matching, pyloudnorm for loudness measurement, soundfile and numpy for IO and DSP, ffmpeg via subprocess for format conversion and MP3 export. Use a venv and a requirements.txt. No Docker. CLI, using argparse: master --input mix.wav --reference ref.wav --target-lufs -14 --true-peak -1.0 --out ./out Behavior: 1. Decode input and reference to 44.1kHz stereo 32-bit float WAV in a temp dir using ffmpeg, accepting wav, aiff, flac and mp3 inputs. 2. Print a before report for the input: integrated LUFS, loudness range, sample peak, estimated true peak, stereo correlation, and a rough 8-band spectral balance in dB. 3. Run matchering to match the input's spectrum and dynamics to the reference. 4. Apply a final loudness stage: measure integrated LUFS with pyloudnorm, apply gain to hit --target-lufs, then apply a simple lookahead true peak limiter (5ms lookahead, 50ms release) so estimated true peak never exceeds --true-peak. 5. Print an after report in the same format as the before report, plus the total gain applied and the number of limiter gain reduction events. 6. Export three files to --out: master_24bit.wav at 44.1kHz, master_16bit.wav dithered with TPDF, and master.mp3 at 320kbps via ffmpeg. 7. Write report.json next to them with all measured values for both stages. Also support --no-reference, which skips matchering and does loudness plus limiting only. Print a warning that this is a normalizer, not a master. Safety: never clip. Refuse to run and exit non-zero if the input is already above -6 LUFS integrated or if its estimated true peak is above 0.0 dBTP, with a message telling the user to export a quieter mix with headroom. Include a README with install steps, one worked example, and a short note that the reference track choice matters more than any flag. Ship three pytest tests: gain math hits the LUFS target within 0.3 dB, the limiter never exceeds the ceiling on a synthetic clipping signal, and the headroom guard exits non-zero. No stems, no batch mode, no plugin hosting, no genre presets. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Masterchannel capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Masterchannel indie build ## Goal Build the smallest trustworthy replacement for the core Masterchannel workflow for one developer or a tiny team. ## Scope A local CLI that analyzes a reference master, matches your mix's spectrum and dynamics to it, then normalizes to a chosen LUFS target with a true peak ceiling and exports streaming-ready files. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Masterchannel instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a local command line audio mastering tool in Python 3.11. No web UI, no accounts, no cloud, no telemetry. Stack: Python 3.11, matchering for reference matching, pyloudnorm for loudness measurement, soundfile and numpy for IO and DSP, ffmpeg via subprocess for format conversion and MP3 export. Use a venv and a requirements.txt. No Docker. CLI, using argparse: master --input mix.wav --reference ref.wav --target-lufs -14 --true-peak -1.0 --out ./out Behavior: 1. Decode input and reference to 44.1kHz stereo 32-bit float WAV in a temp dir using ffmpeg, accepting wav, aiff, flac and mp3 inputs. 2. Print a before report for the input: integrated LUFS, loudness range, sample peak, estimated true peak, stereo correlation, and a rough 8-band spectral balance in dB. 3. Run matchering to match the input's spectrum and dynamics to the reference. 4. Apply a final loudness stage: measure integrated LUFS with pyloudnorm, apply gain to hit --target-lufs, then apply a simple lookahead true peak limiter (5ms lookahead, 50ms release) so estimated true peak never exceeds --true-peak. 5. Print an after report in the same format as the before report, plus the total gain applied and the number of limiter gain reduction events. 6. Export three files to --out: master_24bit.wav at 44.1kHz, master_16bit.wav dithered with TPDF, and master.mp3 at 320kbps via ffmpeg. 7. Write report.json next to them with all measured values for both stages. Also support --no-reference, which skips matchering and does loudness plus limiting only. Print a warning that this is a normalizer, not a master. Safety: never clip. Refuse to run and exit non-zero if the input is already above -6 LUFS integrated or if its estimated true peak is above 0.0 dBTP, with a message telling the user to export a quieter mix with headroom. Include a README with install steps, one worked example, and a short note that the reference track choice matters more than any flag. Ship three pytest tests: gain math hits the LUFS target within 0.3 dB, the limiter never exceeds the ceiling on a synthetic clipping signal, and the headroom guard exits non-zero. No stems, no batch mode, no plugin hosting, no genre presets. ## Required capabilities - Python 3.11 and ffmpeg installed locally - At least one commercial reference track in a lossless format - Headphones or monitors you actually trust - Basic willingness to A/B and re-run with different references ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Masterchannel product brief ## Problem 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. ## Product outcome A local CLI that analyzes a reference master, matches your mix's spectrum and dynamics to it, then normalizes to a chosen LUFS target with a true peak ceiling and exports streaming-ready files. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and ffmpeg installed locally - At least one commercial reference track in a lossless format - Headphones or monitors you actually trust - Basic willingness to A/B and re-run with different references ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a local command line audio mastering tool in Python 3.11. No web UI, no accounts, no cloud, no telemetry. Stack: Python 3.11, matchering for reference matching, pyloudnorm for loudness measurement, soundfile and numpy for IO and DSP, ffmpeg via subprocess for format conversion and MP3 export. Use a venv and a requirements.txt. No Docker. CLI, using argparse: master --input mix.wav --reference ref.wav --target-lufs -14 --true-peak -1.0 --out ./out Behavior: 1. Decode input and reference to 44.1kHz stereo 32-bit float WAV in a temp dir using ffmpeg, accepting wav, aiff, flac and mp3 inputs. 2. Print a before report for the input: integrated LUFS, loudness range, sample peak, estimated true peak, stereo correlation, and a rough 8-band spectral balance in dB. 3. Run matchering to match the input's spectrum and dynamics to the reference. 4. Apply a final loudness stage: measure integrated LUFS with pyloudnorm, apply gain to hit --target-lufs, then apply a simple lookahead true peak limiter (5ms lookahead, 50ms release) so estimated true peak never exceeds --true-peak. 5. Print an after report in the same format as the before report, plus the total gain applied and the number of limiter gain reduction events. 6. Export three files to --out: master_24bit.wav at 44.1kHz, master_16bit.wav dithered with TPDF, and master.mp3 at 320kbps via ffmpeg. 7. Write report.json next to them with all measured values for both stages. Also support --no-reference, which skips matchering and does loudness plus limiting only. Print a warning that this is a normalizer, not a master. Safety: never clip. Refuse to run and exit non-zero if the input is already above -6 LUFS integrated or if its estimated true peak is above 0.0 dBTP, with a message telling the user to export a quieter mix with headroom. Include a README with install steps, one worked example, and a short note that the reference track choice matters more than any flag. Ship three pytest tests: gain math hits the LUFS target within 0.3 dB, the limiter never exceeds the ceiling on a synthetic clipping signal, and the headroom guard exits non-zero. No stems, no batch mode, no plugin hosting, no genre presets. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Masterchannel capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Most people paying for AI mastering are not chasing the last two percent of fidelity, they are avoiding a decision. They have a mix, a release date, and no interest in learning about multiband compression or inter-sample true peaks. A web upload that returns something loud and balanced in ninety seconds is worth real money against that, and the reference-free convenience is exactly the part a local script does worst. Engineers and people with a strong reference library will get most of the value from the DIY build; everyone else will keep paying to skip the taste problem.
xReference-free mastering: the service guesses a target for you, your script needs you to pick one
xGenre-aware presets and the taste baked into a trained model
xStem mastering, vocal-forward variants and other per-track intelligence
xA clean web UI with instant previews and revision history
xAnything resembling a second opinion when your mix is the actual problem
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Masterchannel ($29/mo) with one prompt"
Vibecode Masterchannel
Kinda. The core of Masterchannel is buildable in a weekend with the prompt on this page, but there are real gaps: 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. Read the honest list above before committing.
How much does Masterchannel cost?
Masterchannel costs about $29/month (Artist, checked 2026-08-18), which is $348 per year.
What do I lose by replacing Masterchannel?
Honestly: 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. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Masterchannel?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.