Vibecode Reel Farm
track this build5 steps, step by step0%KINDA · weekend project
The pipeline here is not a secret: an LLM writes a short script, a TTS API reads it, stock or generated clips get stitched behind it, and word-level timings drive burned-in captions. ffmpeg does the heavy lifting and an agent can wire the whole chain in a weekend, including a queue that renders fifty variations overnight. Where it stops being fun is everything after the render: scheduled posting to TikTok, Instagram and YouTube means real API access, app review, tokens that expire, and platform rules that change without warning. You will also spend more time than you expect on the boring parts, safe-area layout for captions, loudness normalization, and clips that do not visually repeat every third video. Build it if you want control over the script and the look, pay if the value you actually want is the post button.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Reel Farm.
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 =====
# Reel Farm indie build
## Goal
Build the smallest trustworthy replacement for the core Reel Farm workflow for one developer or a tiny team.
## Scope
A local CLI that takes a topic, writes a short script, synthesizes a voiceover, stitches stock clips with word-timed burned-in captions, and renders a vertical MP4 batch.
## 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:
- Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps
- Hosted rendering, so long batches tie up your own machine
- Curated templates and caption styles that already look native to each platform
- Any built-in sense of what is performing, analytics loops and hook variants
- Someone else absorbing model and stock-footage cost changes
If those capabilities are essential, use Reel Farm 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 tool that mass-produces vertical faceless short-form videos. No web UI, no accounts, no telemetry, no cloud storage.
Stack: TypeScript on Node 20, run with tsx. Use commander for the CLI, zod for config validation, and fluent-ffmpeg shelling out to a locally installed ffmpeg. SQLite via better-sqlite3 for the job queue and render history. No frontend framework, no Docker.
Secrets in .env, loaded with dotenv: OPENAI_API_KEY, ELEVENLABS_API_KEY, PEXELS_API_KEY. Fail loudly on startup if a key needed by the chosen command is missing.
Commands:
1. reelfarm script --topic "..." --count 5 : call an LLM to produce N short scripts as JSON, each with a hook line, 3 to 5 body beats, and a closing line, target 25 to 40 seconds when spoken. Store each as a pending job row.
2. reelfarm voice --job-id X : send the script to ElevenLabs TTS, save MP3 to media/voice, and normalize loudness to -14 LUFS with the ffmpeg loudnorm filter in two passes.
3. reelfarm captions --job-id X : transcribe the rendered voiceover with whisper.cpp if a local binary is configured, otherwise the OpenAI transcription API with word timestamps, and write an ASS subtitle file with 2 to 4 words per screen, centered in the middle third, thick outline, no more than 90 percent of frame width.
4. reelfarm clips --job-id X : pull vertical stock clips from Pexels matched to keywords extracted from each beat, cache downloads in media/clips, and never reuse the same clip twice within one batch.
5. reelfarm render --job-id X : with ffmpeg, scale and center-crop clips to 1080x1920, cut between them on beat boundaries, overlay the ASS captions, mix voiceover over an optional background track at -22 dB, and write out/{job-id}.mp4 with faststart, H.264, yuv420p, 30 fps.
6. reelfarm batch --topic "..." --count 10 : run the whole chain end to end, serially, resumable, skipping steps whose output already exists on disk.
Explicitly out of scope: posting or scheduling to any social platform, analytics, AI video generation models, multi-user anything. Print the output path and let the human upload.
Write a config.example.json with caption style, voice id, target duration, and clip source. Include a README with the ffmpeg install line, the .env template, and one working batch example. Add a smoke test that renders a 5 second clip from a bundled sample audio file and a solid-color background so the pipeline can be verified without spending API credits.
## Required capabilities
- ffmpeg and ffprobe installed locally
- An LLM API key for scripting
- A TTS API key, or a local TTS model
- A stock footage API key, or your own clip library
- whisper.cpp or a transcription API for word-level caption timings
- Disk space and patience for render queues
## 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 Reel Farm.
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 =====
# Reel Farm indie build
## Goal
Build the smallest trustworthy replacement for the core Reel Farm workflow for one developer or a tiny team.
## Scope
A local CLI that takes a topic, writes a short script, synthesizes a voiceover, stitches stock clips with word-timed burned-in captions, and renders a vertical MP4 batch.
## 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:
- Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps
- Hosted rendering, so long batches tie up your own machine
- Curated templates and caption styles that already look native to each platform
- Any built-in sense of what is performing, analytics loops and hook variants
- Someone else absorbing model and stock-footage cost changes
If those capabilities are essential, use Reel Farm 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 tool that mass-produces vertical faceless short-form videos. No web UI, no accounts, no telemetry, no cloud storage.
Stack: TypeScript on Node 20, run with tsx. Use commander for the CLI, zod for config validation, and fluent-ffmpeg shelling out to a locally installed ffmpeg. SQLite via better-sqlite3 for the job queue and render history. No frontend framework, no Docker.
Secrets in .env, loaded with dotenv: OPENAI_API_KEY, ELEVENLABS_API_KEY, PEXELS_API_KEY. Fail loudly on startup if a key needed by the chosen command is missing.
Commands:
1. reelfarm script --topic "..." --count 5 : call an LLM to produce N short scripts as JSON, each with a hook line, 3 to 5 body beats, and a closing line, target 25 to 40 seconds when spoken. Store each as a pending job row.
2. reelfarm voice --job-id X : send the script to ElevenLabs TTS, save MP3 to media/voice, and normalize loudness to -14 LUFS with the ffmpeg loudnorm filter in two passes.
3. reelfarm captions --job-id X : transcribe the rendered voiceover with whisper.cpp if a local binary is configured, otherwise the OpenAI transcription API with word timestamps, and write an ASS subtitle file with 2 to 4 words per screen, centered in the middle third, thick outline, no more than 90 percent of frame width.
4. reelfarm clips --job-id X : pull vertical stock clips from Pexels matched to keywords extracted from each beat, cache downloads in media/clips, and never reuse the same clip twice within one batch.
5. reelfarm render --job-id X : with ffmpeg, scale and center-crop clips to 1080x1920, cut between them on beat boundaries, overlay the ASS captions, mix voiceover over an optional background track at -22 dB, and write out/{job-id}.mp4 with faststart, H.264, yuv420p, 30 fps.
6. reelfarm batch --topic "..." --count 10 : run the whole chain end to end, serially, resumable, skipping steps whose output already exists on disk.
Explicitly out of scope: posting or scheduling to any social platform, analytics, AI video generation models, multi-user anything. Print the output path and let the human upload.
Write a config.example.json with caption style, voice id, target duration, and clip source. Include a README with the ffmpeg install line, the .env template, and one working batch example. Add a smoke test that renders a 5 second clip from a bundled sample audio file and a solid-color background so the pipeline can be verified without spending API credits.
## Required capabilities
- ffmpeg and ffprobe installed locally
- An LLM API key for scripting
- A TTS API key, or a local TTS model
- A stock footage API key, or your own clip library
- whisper.cpp or a transcription API for word-level caption timings
- Disk space and patience for render queues
## 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 Reel Farm.
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 =====
# Reel Farm product brief
## Problem
The pipeline here is not a secret: an LLM writes a short script, a TTS API reads it, stock or generated clips get stitched behind it, and word-level timings drive burned-in captions. ffmpeg does the heavy lifting and an agent can wire the whole chain in a weekend, including a queue that renders fifty variations overnight. Where it stops being fun is everything after the render: scheduled posting to TikTok, Instagram and YouTube means real API access, app review, tokens that expire, and platform rules that change without warning. You will also spend more time than you expect on the boring parts, safe-area layout for captions, loudness normalization, and clips that do not visually repeat every third video. Build it if you want control over the script and the look, pay if the value you actually want is the post button.
## Product outcome
A local CLI that takes a topic, writes a short script, synthesizes a voiceover, stitches stock clips with word-timed burned-in captions, and renders a vertical MP4 batch.
## Target user
A serious builder who needs a maintainable product foundation rather than a one-off demo.
## Required capabilities
- ffmpeg and ffprobe installed locally
- An LLM API key for scripting
- A TTS API key, or a local TTS model
- A stock footage API key, or your own clip library
- whisper.cpp or a transcription API for word-level caption timings
- Disk space and patience for render queues
## Explicit non-goals for v1
- Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps
- Hosted rendering, so long batches tie up your own machine
- Curated templates and caption styles that already look native to each platform
- Any built-in sense of what is performing, analytics loops and hook variants
- Someone else absorbing model and stock-footage cost changes
## 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 tool that mass-produces vertical faceless short-form videos. No web UI, no accounts, no telemetry, no cloud storage.
Stack: TypeScript on Node 20, run with tsx. Use commander for the CLI, zod for config validation, and fluent-ffmpeg shelling out to a locally installed ffmpeg. SQLite via better-sqlite3 for the job queue and render history. No frontend framework, no Docker.
Secrets in .env, loaded with dotenv: OPENAI_API_KEY, ELEVENLABS_API_KEY, PEXELS_API_KEY. Fail loudly on startup if a key needed by the chosen command is missing.
Commands:
1. reelfarm script --topic "..." --count 5 : call an LLM to produce N short scripts as JSON, each with a hook line, 3 to 5 body beats, and a closing line, target 25 to 40 seconds when spoken. Store each as a pending job row.
2. reelfarm voice --job-id X : send the script to ElevenLabs TTS, save MP3 to media/voice, and normalize loudness to -14 LUFS with the ffmpeg loudnorm filter in two passes.
3. reelfarm captions --job-id X : transcribe the rendered voiceover with whisper.cpp if a local binary is configured, otherwise the OpenAI transcription API with word timestamps, and write an ASS subtitle file with 2 to 4 words per screen, centered in the middle third, thick outline, no more than 90 percent of frame width.
4. reelfarm clips --job-id X : pull vertical stock clips from Pexels matched to keywords extracted from each beat, cache downloads in media/clips, and never reuse the same clip twice within one batch.
5. reelfarm render --job-id X : with ffmpeg, scale and center-crop clips to 1080x1920, cut between them on beat boundaries, overlay the ASS captions, mix voiceover over an optional background track at -22 dB, and write out/{job-id}.mp4 with faststart, H.264, yuv420p, 30 fps.
6. reelfarm batch --topic "..." --count 10 : run the whole chain end to end, serially, resumable, skipping steps whose output already exists on disk.
Explicitly out of scope: posting or scheduling to any social platform, analytics, AI video generation models, multi-user anything. Print the output path and let the human upload.
Write a config.example.json with caption style, voice id, target duration, and clip source. Include a README with the ffmpeg install line, the .env template, and one working batch example. Add a smoke test that renders a 5 second clip from a bundled sample audio file and a solid-color background so the pipeline can be verified without spending API credits.
## 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 Reel Farm capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.README.md
# Reel Farm indie build ## Goal Build the smallest trustworthy replacement for the core Reel Farm workflow for one developer or a tiny team. ## Scope A local CLI that takes a topic, writes a short script, synthesizes a voiceover, stitches stock clips with word-timed burned-in captions, and renders a vertical MP4 batch. ## 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: - Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps - Hosted rendering, so long batches tie up your own machine - Curated templates and caption styles that already look native to each platform - Any built-in sense of what is performing, analytics loops and hook variants - Someone else absorbing model and stock-footage cost changes If those capabilities are essential, use Reel Farm 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 tool that mass-produces vertical faceless short-form videos. No web UI, no accounts, no telemetry, no cloud storage.
Stack: TypeScript on Node 20, run with tsx. Use commander for the CLI, zod for config validation, and fluent-ffmpeg shelling out to a locally installed ffmpeg. SQLite via better-sqlite3 for the job queue and render history. No frontend framework, no Docker.
Secrets in .env, loaded with dotenv: OPENAI_API_KEY, ELEVENLABS_API_KEY, PEXELS_API_KEY. Fail loudly on startup if a key needed by the chosen command is missing.
Commands:
1. reelfarm script --topic "..." --count 5 : call an LLM to produce N short scripts as JSON, each with a hook line, 3 to 5 body beats, and a closing line, target 25 to 40 seconds when spoken. Store each as a pending job row.
2. reelfarm voice --job-id X : send the script to ElevenLabs TTS, save MP3 to media/voice, and normalize loudness to -14 LUFS with the ffmpeg loudnorm filter in two passes.
3. reelfarm captions --job-id X : transcribe the rendered voiceover with whisper.cpp if a local binary is configured, otherwise the OpenAI transcription API with word timestamps, and write an ASS subtitle file with 2 to 4 words per screen, centered in the middle third, thick outline, no more than 90 percent of frame width.
4. reelfarm clips --job-id X : pull vertical stock clips from Pexels matched to keywords extracted from each beat, cache downloads in media/clips, and never reuse the same clip twice within one batch.
5. reelfarm render --job-id X : with ffmpeg, scale and center-crop clips to 1080x1920, cut between them on beat boundaries, overlay the ASS captions, mix voiceover over an optional background track at -22 dB, and write out/{job-id}.mp4 with faststart, H.264, yuv420p, 30 fps.
6. reelfarm batch --topic "..." --count 10 : run the whole chain end to end, serially, resumable, skipping steps whose output already exists on disk.
Explicitly out of scope: posting or scheduling to any social platform, analytics, AI video generation models, multi-user anything. Print the output path and let the human upload.
Write a config.example.json with caption style, voice id, target duration, and clip source. Include a README with the ffmpeg install line, the .env template, and one working batch example. Add a smoke test that renders a 5 second clip from a bundled sample audio file and a solid-color background so the pipeline can be verified without spending API credits.
## Required capabilities
- ffmpeg and ffprobe installed locally
- An LLM API key for scripting
- A TTS API key, or a local TTS model
- A stock footage API key, or your own clip library
- whisper.cpp or a transcription API for word-level caption timings
- Disk space and patience for render queues
## 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
# Reel Farm product brief ## Problem The pipeline here is not a secret: an LLM writes a short script, a TTS API reads it, stock or generated clips get stitched behind it, and word-level timings drive burned-in captions. ffmpeg does the heavy lifting and an agent can wire the whole chain in a weekend, including a queue that renders fifty variations overnight. Where it stops being fun is everything after the render: scheduled posting to TikTok, Instagram and YouTube means real API access, app review, tokens that expire, and platform rules that change without warning. You will also spend more time than you expect on the boring parts, safe-area layout for captions, loudness normalization, and clips that do not visually repeat every third video. Build it if you want control over the script and the look, pay if the value you actually want is the post button. ## Product outcome A local CLI that takes a topic, writes a short script, synthesizes a voiceover, stitches stock clips with word-timed burned-in captions, and renders a vertical MP4 batch. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - ffmpeg and ffprobe installed locally - An LLM API key for scripting - A TTS API key, or a local TTS model - A stock footage API key, or your own clip library - whisper.cpp or a transcription API for word-level caption timings - Disk space and patience for render queues ## Explicit non-goals for v1 - Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps - Hosted rendering, so long batches tie up your own machine - Curated templates and caption styles that already look native to each platform - Any built-in sense of what is performing, analytics loops and hook variants - Someone else absorbing model and stock-footage cost changes ## 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 tool that mass-produces vertical faceless short-form videos. No web UI, no accounts, no telemetry, no cloud storage.
Stack: TypeScript on Node 20, run with tsx. Use commander for the CLI, zod for config validation, and fluent-ffmpeg shelling out to a locally installed ffmpeg. SQLite via better-sqlite3 for the job queue and render history. No frontend framework, no Docker.
Secrets in .env, loaded with dotenv: OPENAI_API_KEY, ELEVENLABS_API_KEY, PEXELS_API_KEY. Fail loudly on startup if a key needed by the chosen command is missing.
Commands:
1. reelfarm script --topic "..." --count 5 : call an LLM to produce N short scripts as JSON, each with a hook line, 3 to 5 body beats, and a closing line, target 25 to 40 seconds when spoken. Store each as a pending job row.
2. reelfarm voice --job-id X : send the script to ElevenLabs TTS, save MP3 to media/voice, and normalize loudness to -14 LUFS with the ffmpeg loudnorm filter in two passes.
3. reelfarm captions --job-id X : transcribe the rendered voiceover with whisper.cpp if a local binary is configured, otherwise the OpenAI transcription API with word timestamps, and write an ASS subtitle file with 2 to 4 words per screen, centered in the middle third, thick outline, no more than 90 percent of frame width.
4. reelfarm clips --job-id X : pull vertical stock clips from Pexels matched to keywords extracted from each beat, cache downloads in media/clips, and never reuse the same clip twice within one batch.
5. reelfarm render --job-id X : with ffmpeg, scale and center-crop clips to 1080x1920, cut between them on beat boundaries, overlay the ASS captions, mix voiceover over an optional background track at -22 dB, and write out/{job-id}.mp4 with faststart, H.264, yuv420p, 30 fps.
6. reelfarm batch --topic "..." --count 10 : run the whole chain end to end, serially, resumable, skipping steps whose output already exists on disk.
Explicitly out of scope: posting or scheduling to any social platform, analytics, AI video generation models, multi-user anything. Print the output path and let the human upload.
Write a config.example.json with caption style, voice id, target duration, and clip source. Include a README with the ffmpeg install line, the .env template, and one working batch example. Add a smoke test that renders a 5 second clip from a bundled sample audio file and a solid-color background so the pipeline can be verified without spending API credits.
## 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 Reel Farm 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
Because a faceless content operation is not one video, it is two hundred, and the friction that kills it is scheduling and upload, not generation. Getting write access to the major short-form platforms is a bureaucratic slog that nobody wants to do twice, and a hosted tool that already holds those tokens is worth a subscription to people running this as a volume game. There is also the honest fact that the default output of a DIY pipeline looks like a DIY pipeline for the first week, until you fix the caption placement, the loudness, and the clip repetition. If you are making a handful of videos with a specific look you care about, build it. If you are farming, pay.
xScheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps
xHosted rendering, so long batches tie up your own machine
xCurated templates and caption styles that already look native to each platform
xAny built-in sense of what is performing, analytics loops and hook variants
xSomeone else absorbing model and stock-footage cost changes
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Reel Farm ($95/mo) with one prompt"
Vibecode Reel Farm
Kinda. The core of Reel Farm is buildable in a weekend with the prompt on this page, but there are real gaps: Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps, Hosted rendering, so long batches tie up your own machine. Read the honest list above before committing.
How much does Reel Farm cost?
Reel Farm costs about $95/month (Scale, checked 2026-08-18), which is $1140 per year.
What do I lose by replacing Reel Farm?
Honestly: Scheduled auto-posting to TikTok, Instagram Reels and YouTube Shorts, which is the part that needs approved platform apps; Hosted rendering, so long batches tie up your own machine; Curated templates and caption styles that already look native to each platform; Any built-in sense of what is performing, analytics loops and hook variants; Someone else absorbing model and stock-footage cost changes. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Reel Farm?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.