Content at Scale

Build me a focused AI drafting, research and content optimization workflow for the personal core of Content at Scale. Requirements:

- Use Next.js 15, TypeScript, Tailwind CSS, SQLite, Drizzle ORM, and the OpenAI Responses API. Model source documents, editable pages or notes, internal links, revisions, and exports; retain source IDs and timestamps.
- Paid product context: Assemble a rigorous source-grounded content workflow without claiming its proprietary optimization s. Build only this DIY scope: Take a brief, gather user-supplied source material, generate a structured source-grounded draft, and keep citations and revisions attached to each section without claiming a proprietary optimization stack.
- For AI drafting, research and content optimization, accept source text, a named instruction preset, and a user-selected model credential.
- Generate a draft as a new revision, preserving the source and recording prompt/model metadata.
- Let the writer compare revisions, accept edits, and export Markdown.
- Use a local web page with input, progress, review, and export views. Required input or access: OpenAI API key; local or self-hosted deployment. Keep credentials in .env.
- Recovery: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.
- Out of scope: opaque enterprise packaging, proprietary models, and managed content operations; proprietary ranking data. Keep this a personal, inspectable workflow.
- Include a README with setup, a sample input, required keys or permissions, data location, and the supported scope.

# AGENTS.md — Build guide for Content at Scale

## Project scope
Take a brief, gather user-supplied source material, generate a structured source-grounded draft, and keep citations and revisions attached to each section without claiming a proprietary optimization stack.

Catalogue verdict: no. A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Content at Scale, assemble a rigorous source-grounded content workflow without claiming its proprietary optimization stack. The hard boundary is opaque enterprise packaging, proprietary models, and managed content operations, plus workflow, data, and model tuning.
Use the implementation prompt below to define the deliverable. Complete each phase's acceptance checks before extending the scope.

## Working agreement
- Inspect the repository and its existing instructions before choosing paths, dependencies or commands. Keep one coherent stack and explain changes to the proposed architecture.
- Plan a vertical slice that accepts a real input and produces the useful output described below. Persist only the state the prompt calls for; respect memory-only and upstream-managed workflows. Use fixtures only when they are clearly labelled.
- After scaffolding, document the actual install, development, check and build commands in README and keep them synchronized with the package or project manifest. Do not report commands as successful unless they ran.
- Work in small steps. At handoff, list implemented flows, checks actually performed, remaining blockers, and any credentials or provider setup the owner must supply.
- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.

## Prerequisites
- OpenAI API key
- Node.js 22
- local or self-hosted deployment
- user-supplied sources

## Stack and architecture
- Use Next.js 15, TypeScript, Tailwind CSS, SQLite, Drizzle ORM, and the OpenAI Responses API.
- Model source documents, editable pages or notes, internal links, revisions, and exports; retain source IDs and timestamps.
- Interface for this AI drafting, research and content optimization workflow: a local web page with input, progress, review, and export views.

## Security and data integrity
- A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.
- Keep API keys and OAuth secrets in .env; redact them from logs, generated files, and error responses.
- State the practical limit: opaque enterprise packaging, proprietary models, and managed content operations.

## Agent implementation rules
- Project rule, data: Model source documents, editable pages or notes, internal links, revisions, and exports; retain source IDs and timestamps.
- Project rule, behavior: For AI drafting, research and content optimization, accept source text, a named instruction preset, and a user-selected model credential.
- Project rule, recovery: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.

## Optional agent skills and references
No installable skill is listed for this guide; use the project rules and the stack documentation.

Read the linked SKILL.md and its dependencies before adding a skill. Select only the skills matching this project's runtime and task; their documentation does not supply API access, credentials or approval to perform external actions. Pin the reviewed revision where the tool supports it. Follow the chosen agent's documented project-level installation mechanism.

## Distribution ideas
These are optional planning notes. Obtain the owner's approval before publishing or contacting anyone.
- Demonstrate this AI drafting, research and content optimization operation with sample or user-owned data: For AI drafting, research and content optimization, accept source text, a named instruction preset, and a user-selected model credential.
- Show a concrete result or export: Let the writer compare revisions, accept edits, and export Markdown.
- State the practical limit: opaque enterprise packaging, proprietary models, and managed content operations.

## Engineering roadmap
1. Phase 1, architecture and data: Use Next.js 15, TypeScript, Tailwind CSS, SQLite, Drizzle ORM, and the OpenAI Responses API. Model source documents, editable pages or notes, internal links, revisions, and exports; retain source IDs and timestamps.
2. Phase 2, implement: For AI drafting, research and content optimization, accept source text, a named instruction preset, and a user-selected model credential.
3. Phase 3, implement: Generate a draft as a new revision, preserving the source and recording prompt/model metadata.
4. Phase 4, review and output: Let the writer compare revisions, accept edits, and export Markdown.
5. Phase 5, recovery and acceptance: Verify this invariant with a saved fixture: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified. State the practical limit: opaque enterprise packaging, proprietary models, and managed content operations.

## Paid-product capabilities outside this build
- opaque enterprise packaging, proprietary models, and managed content operations
- proprietary ranking data
- brand-trained models
- team workflows
- large template libraries

## Implementation prompt
Build me a focused AI drafting, research and content optimization workflow for the personal core of Content at Scale. Requirements:

- Use Next.js 15, TypeScript, Tailwind CSS, SQLite, Drizzle ORM, and the OpenAI Responses API. Model source documents, editable pages or notes, internal links, revisions, and exports; retain source IDs and timestamps.
- Paid product context: Assemble a rigorous source-grounded content workflow without claiming its proprietary optimization s. Build only this DIY scope: Take a brief, gather user-supplied source material, generate a structured source-grounded draft, and keep citations and revisions attached to each section without claiming a proprietary optimization stack.
- For AI drafting, research and content optimization, accept source text, a named instruction preset, and a user-selected model credential.
- Generate a draft as a new revision, preserving the source and recording prompt/model metadata.
- Let the writer compare revisions, accept edits, and export Markdown.
- Use a local web page with input, progress, review, and export views. Required input or access: OpenAI API key; local or self-hosted deployment. Keep credentials in .env.
- Recovery: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: A model failure cannot overwrite the source; quoted facts must stay traceable to supplied text or be marked unverified.
- Out of scope: opaque enterprise packaging, proprietary models, and managed content operations; proprietary ranking data. Keep this a personal, inspectable workflow.
- Include a README with setup, a sample input, required keys or permissions, data location, and the supported scope.

## Completion evidence
Demonstrate the prompt's acceptance scenarios against the scoped workflow. Include setup from a clean checkout and failure recovery. Check persistence across restart and export/restore only for the state the prompt says to store; for memory-only tools, confirm that temporary content is discarded as specified. Record actual results and remaining limitations. A detailed plan alone does not establish a working replacement.