Vibecode Revid AI

You are building a lean indie version of Revid AI.

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 =====
# Revid AI indie build

## Goal

Build the smallest trustworthy replacement for the core Revid AI workflow for one developer or a tiny team.

## Scope

Write a script, define a reusable style file, generate each shot through Seedance 2.0 or Gemini Omni Flash, narrate with ElevenLabs, burn captions from the word timings, and stitch to a single vertical mp4 with ffmpeg.

## 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:
- the 3M+ viral video library to remix, which is a licensing problem and not a coding one
- one-click publishing to TikTok, Instagram, and YouTube
- AI avatars, face swaps, and the 100+ prebuilt tools around the core generator
- one predictable bill instead of three metered APIs you can overspend on in an afternoon
- auto-mode workers grinding out videos while you are asleep

If those capabilities are essential, use Remotion 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 me a Claude Code skill that makes short-form videos to replace Revid AI. Requirements:

- The deliverable is a skill folder, not a web app: SKILL.md plus a Node + TypeScript CLI
  run with tsx, no build step. ffmpeg does every stitch, mux, and caption burn-in.
- Projects live in ./projects/<slug>/ with brief.md, script.md, shots.json, and a render/
  folder, so everything about one video sits in one directory I can delete.
- Styles are files, not prompts I retype: styles/<name>.json holds the look, aspect ratio,
  pacing, caption font and position, voice id, and a shot-prompt preamble. A project names
  one style and inherits all of it.
- Generate each shot with Seedance 2.0 when it needs native audio or lip sync, or Gemini
  Omni Flash when I want to revise it conversationally without re-prompting · the choice
  is per shot in shots.json. Keys in .env.
- Narrate with ElevenLabs where a shot has voiceover rather than native dialogue, and burn
  captions from the returned word timings, styled by the style file.
- Cache every clip by a hash of its prompt plus style, so re-rendering a project never
  pays twice for a shot that did not change.
- SKILL.md must tell the agent to read the live API docs before writing any call:
  replicate.com/bytedance/seedance-2.0, ai.google.dev/gemini-api/docs/video, and
  elevenlabs.io/docs. These models ship fast, so never write a request shape from memory.
- No accounts, no telemetry, everything on my machine except the three model APIs.
  Secrets in .env with a committed .env.example.
- Out of scope: publishing to TikTok, Instagram, or YouTube, and any stock or viral-clip
  library. Do not build a web UI, the interface is the skill and the CLI.
- README: the .env keys, how to install ffmpeg, and a worked cost estimate for a 30 second
  video at current per-second model prices, so I know what a render costs before I run it.

## Required capabilities

- an agent that can read API docs and run code, such as Claude Code
- Seedance 2.0 access via fal or Replicate
- Google Gemini API key for Gemini Omni Flash
- ElevenLabs API key
- ffmpeg

## 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 Revid AI.

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 =====
# Revid AI indie build

## Goal

Build the smallest trustworthy replacement for the core Revid AI workflow for one developer or a tiny team.

## Scope

Write a script, define a reusable style file, generate each shot through Seedance 2.0 or Gemini Omni Flash, narrate with ElevenLabs, burn captions from the word timings, and stitch to a single vertical mp4 with ffmpeg.

## 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:
- the 3M+ viral video library to remix, which is a licensing problem and not a coding one
- one-click publishing to TikTok, Instagram, and YouTube
- AI avatars, face swaps, and the 100+ prebuilt tools around the core generator
- one predictable bill instead of three metered APIs you can overspend on in an afternoon
- auto-mode workers grinding out videos while you are asleep

If those capabilities are essential, use Remotion 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 me a Claude Code skill that makes short-form videos to replace Revid AI. Requirements:

- The deliverable is a skill folder, not a web app: SKILL.md plus a Node + TypeScript CLI
  run with tsx, no build step. ffmpeg does every stitch, mux, and caption burn-in.
- Projects live in ./projects/<slug>/ with brief.md, script.md, shots.json, and a render/
  folder, so everything about one video sits in one directory I can delete.
- Styles are files, not prompts I retype: styles/<name>.json holds the look, aspect ratio,
  pacing, caption font and position, voice id, and a shot-prompt preamble. A project names
  one style and inherits all of it.
- Generate each shot with Seedance 2.0 when it needs native audio or lip sync, or Gemini
  Omni Flash when I want to revise it conversationally without re-prompting · the choice
  is per shot in shots.json. Keys in .env.
- Narrate with ElevenLabs where a shot has voiceover rather than native dialogue, and burn
  captions from the returned word timings, styled by the style file.
- Cache every clip by a hash of its prompt plus style, so re-rendering a project never
  pays twice for a shot that did not change.
- SKILL.md must tell the agent to read the live API docs before writing any call:
  replicate.com/bytedance/seedance-2.0, ai.google.dev/gemini-api/docs/video, and
  elevenlabs.io/docs. These models ship fast, so never write a request shape from memory.
- No accounts, no telemetry, everything on my machine except the three model APIs.
  Secrets in .env with a committed .env.example.
- Out of scope: publishing to TikTok, Instagram, or YouTube, and any stock or viral-clip
  library. Do not build a web UI, the interface is the skill and the CLI.
- README: the .env keys, how to install ffmpeg, and a worked cost estimate for a 30 second
  video at current per-second model prices, so I know what a render costs before I run it.

## Required capabilities

- an agent that can read API docs and run code, such as Claude Code
- Seedance 2.0 access via fal or Replicate
- Google Gemini API key for Gemini Omni Flash
- ElevenLabs API key
- ffmpeg

## 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 Revid AI.

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 =====
# Revid AI product brief

## Problem

Revid does not own a model or a render farm. The shots come from Seedance 2.0 and Gemini Omni Flash, the voice comes from ElevenLabs, and the stitch is ffmpeg. All three are public APIs with public docs, so the honest replacement is an agent skill that reads those docs and calls them: a project folder per video, a style file holding the look, aspect ratio, pacing, captions, and voice, and a cache so a re-render does not repay for unchanged shots. This is why it lands on yes where the older video tools on this site land on kinda. Those wrap encoding and editing infrastructure you would have to rebuild. Revid wraps three endpoints. What you are really paying for is that someone already wired them together and pointed them at a publishing button.

## Product outcome

Write a script, define a reusable style file, generate each shot through Seedance 2.0 or Gemini Omni Flash, narrate with ElevenLabs, burn captions from the word timings, and stitch to a single vertical mp4 with ffmpeg.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- an agent that can read API docs and run code, such as Claude Code
- Seedance 2.0 access via fal or Replicate
- Google Gemini API key for Gemini Omni Flash
- ElevenLabs API key
- ffmpeg

## Explicit non-goals for v1

- the 3M+ viral video library to remix, which is a licensing problem and not a coding one
- one-click publishing to TikTok, Instagram, and YouTube
- AI avatars, face swaps, and the 100+ prebuilt tools around the core generator
- one predictable bill instead of three metered APIs you can overspend on in an afternoon
- auto-mode workers grinding out videos while you are asleep

## 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 me a Claude Code skill that makes short-form videos to replace Revid AI. Requirements:

- The deliverable is a skill folder, not a web app: SKILL.md plus a Node + TypeScript CLI
  run with tsx, no build step. ffmpeg does every stitch, mux, and caption burn-in.
- Projects live in ./projects/<slug>/ with brief.md, script.md, shots.json, and a render/
  folder, so everything about one video sits in one directory I can delete.
- Styles are files, not prompts I retype: styles/<name>.json holds the look, aspect ratio,
  pacing, caption font and position, voice id, and a shot-prompt preamble. A project names
  one style and inherits all of it.
- Generate each shot with Seedance 2.0 when it needs native audio or lip sync, or Gemini
  Omni Flash when I want to revise it conversationally without re-prompting · the choice
  is per shot in shots.json. Keys in .env.
- Narrate with ElevenLabs where a shot has voiceover rather than native dialogue, and burn
  captions from the returned word timings, styled by the style file.
- Cache every clip by a hash of its prompt plus style, so re-rendering a project never
  pays twice for a shot that did not change.
- SKILL.md must tell the agent to read the live API docs before writing any call:
  replicate.com/bytedance/seedance-2.0, ai.google.dev/gemini-api/docs/video, and
  elevenlabs.io/docs. These models ship fast, so never write a request shape from memory.
- No accounts, no telemetry, everything on my machine except the three model APIs.
  Secrets in .env with a committed .env.example.
- Out of scope: publishing to TikTok, Instagram, or YouTube, and any stock or viral-clip
  library. Do not build a web UI, the interface is the skill and the CLI.
- README: the .env keys, how to install ffmpeg, and a worked cost estimate for a 30 second
  video at current per-second model prices, so I know what a render costs before I run it.

## 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 Revid AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Revid AI indie build

## Goal

Build the smallest trustworthy replacement for the core Revid AI workflow for one developer or a tiny team.

## Scope

Write a script, define a reusable style file, generate each shot through Seedance 2.0 or Gemini Omni Flash, narrate with ElevenLabs, burn captions from the word timings, and stitch to a single vertical mp4 with ffmpeg.

## 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:
- the 3M+ viral video library to remix, which is a licensing problem and not a coding one
- one-click publishing to TikTok, Instagram, and YouTube
- AI avatars, face swaps, and the 100+ prebuilt tools around the core generator
- one predictable bill instead of three metered APIs you can overspend on in an afternoon
- auto-mode workers grinding out videos while you are asleep

If those capabilities are essential, use Remotion instead of pretending the gap is solved.

# 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

## Original build brief

Build me a Claude Code skill that makes short-form videos to replace Revid AI. Requirements:

- The deliverable is a skill folder, not a web app: SKILL.md plus a Node + TypeScript CLI
  run with tsx, no build step. ffmpeg does every stitch, mux, and caption burn-in.
- Projects live in ./projects/<slug>/ with brief.md, script.md, shots.json, and a render/
  folder, so everything about one video sits in one directory I can delete.
- Styles are files, not prompts I retype: styles/<name>.json holds the look, aspect ratio,
  pacing, caption font and position, voice id, and a shot-prompt preamble. A project names
  one style and inherits all of it.
- Generate each shot with Seedance 2.0 when it needs native audio or lip sync, or Gemini
  Omni Flash when I want to revise it conversationally without re-prompting · the choice
  is per shot in shots.json. Keys in .env.
- Narrate with ElevenLabs where a shot has voiceover rather than native dialogue, and burn
  captions from the returned word timings, styled by the style file.
- Cache every clip by a hash of its prompt plus style, so re-rendering a project never
  pays twice for a shot that did not change.
- SKILL.md must tell the agent to read the live API docs before writing any call:
  replicate.com/bytedance/seedance-2.0, ai.google.dev/gemini-api/docs/video, and
  elevenlabs.io/docs. These models ship fast, so never write a request shape from memory.
- No accounts, no telemetry, everything on my machine except the three model APIs.
  Secrets in .env with a committed .env.example.
- Out of scope: publishing to TikTok, Instagram, or YouTube, and any stock or viral-clip
  library. Do not build a web UI, the interface is the skill and the CLI.
- README: the .env keys, how to install ffmpeg, and a worked cost estimate for a 30 second
  video at current per-second model prices, so I know what a render costs before I run it.

## Required capabilities

- an agent that can read API docs and run code, such as Claude Code
- Seedance 2.0 access via fal or Replicate
- Google Gemini API key for Gemini Omni Flash
- ElevenLabs API key
- ffmpeg

## 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.

# 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.

# Revid AI product brief

## Problem

Revid does not own a model or a render farm. The shots come from Seedance 2.0 and Gemini Omni Flash, the voice comes from ElevenLabs, and the stitch is ffmpeg. All three are public APIs with public docs, so the honest replacement is an agent skill that reads those docs and calls them: a project folder per video, a style file holding the look, aspect ratio, pacing, captions, and voice, and a cache so a re-render does not repay for unchanged shots. This is why it lands on yes where the older video tools on this site land on kinda. Those wrap encoding and editing infrastructure you would have to rebuild. Revid wraps three endpoints. What you are really paying for is that someone already wired them together and pointed them at a publishing button.

## Product outcome

Write a script, define a reusable style file, generate each shot through Seedance 2.0 or Gemini Omni Flash, narrate with ElevenLabs, burn captions from the word timings, and stitch to a single vertical mp4 with ffmpeg.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- an agent that can read API docs and run code, such as Claude Code
- Seedance 2.0 access via fal or Replicate
- Google Gemini API key for Gemini Omni Flash
- ElevenLabs API key
- ffmpeg

## Explicit non-goals for v1

- the 3M+ viral video library to remix, which is a licensing problem and not a coding one
- one-click publishing to TikTok, Instagram, and YouTube
- AI avatars, face swaps, and the 100+ prebuilt tools around the core generator
- one predictable bill instead of three metered APIs you can overspend on in an afternoon
- auto-mode workers grinding out videos while you are asleep

## 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

## Starting brief

Build me a Claude Code skill that makes short-form videos to replace Revid AI. Requirements:

- The deliverable is a skill folder, not a web app: SKILL.md plus a Node + TypeScript CLI
  run with tsx, no build step. ffmpeg does every stitch, mux, and caption burn-in.
- Projects live in ./projects/<slug>/ with brief.md, script.md, shots.json, and a render/
  folder, so everything about one video sits in one directory I can delete.
- Styles are files, not prompts I retype: styles/<name>.json holds the look, aspect ratio,
  pacing, caption font and position, voice id, and a shot-prompt preamble. A project names
  one style and inherits all of it.
- Generate each shot with Seedance 2.0 when it needs native audio or lip sync, or Gemini
  Omni Flash when I want to revise it conversationally without re-prompting · the choice
  is per shot in shots.json. Keys in .env.
- Narrate with ElevenLabs where a shot has voiceover rather than native dialogue, and burn
  captions from the returned word timings, styled by the style file.
- Cache every clip by a hash of its prompt plus style, so re-rendering a project never
  pays twice for a shot that did not change.
- SKILL.md must tell the agent to read the live API docs before writing any call:
  replicate.com/bytedance/seedance-2.0, ai.google.dev/gemini-api/docs/video, and
  elevenlabs.io/docs. These models ship fast, so never write a request shape from memory.
- No accounts, no telemetry, everything on my machine except the three model APIs.
  Secrets in .env with a committed .env.example.
- Out of scope: publishing to TikTok, Instagram, or YouTube, and any stock or viral-clip
  library. Do not build a web UI, the interface is the skill and the CLI.
- README: the .env keys, how to install ffmpeg, and a worked cost estimate for a 30 second
  video at current per-second model prices, so I know what a render costs before I run it.

## 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.

# 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.

# 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

## 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 Revid AI 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