Zencoder

Build me a focused AI coding agents workflow for the personal core of Zencoder. Requirements:

- Use exactly this stack: Python 3.12 + Typer + SQLite. Model selected repositories or projects, operations, results, revisions, and run logs; keep stable source IDs and timestamps.
- Paid product context: IDE agents for code generation, refactoring, tests, and repository tasks. Build only this DIY scope: Build a repository-local AI coding agents assistant that indexes one codebase, calls one model, proposes diffs, runs tests, and records every change.
- For AI coding agents, read one selected workspace and define which files may be indexed or edited.
- Offer an inspectable suggestion or edit preview before modifying the editor buffer.
- Apply or reject the change and show its source context and diff.
- Use a CLI with a dry-run or preview command and an explicit output path. Required input or access: Git; OpenAI API key in .env. Keep credentials in .env.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: Rejecting a suggestion must leave the buffer unchanged; accepting it must be reversible with the editor undo stack.
- Out of scope: IDE-wide polish and latency; enterprise policy, telemetry, and support. 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 Zencoder

## Project scope
Build a repository-local AI coding agents assistant that indexes one codebase, calls one model, proposes diffs, runs tests, and records every change.

Catalogue verdict: no. Do not mistake the interface for the product. Zencoder's durable value is model, context, integration, which a solo one-shot build cannot reproduce responsibly. The prompt therefore builds only the closest honest personal consolation tool.
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
- Python 3.12
- Git
- OpenAI API key in .env
- Explicit README warning that this is a consolation build, not a production replacement

## Stack and architecture
- Use exactly this stack: Python 3.12 + Typer + SQLite.
- Model selected repositories or projects, operations, results, revisions, and run logs; keep stable source IDs and timestamps.
- Interface for this AI coding agents workflow: a CLI with a dry-run or preview command and an explicit output path.

## Security and data integrity
- Rejecting a suggestion must leave the buffer unchanged; accepting it must be reversible with the editor undo stack.
- Keep API keys and OAuth secrets in .env; redact them from logs, generated files, and error responses.
- State the practical limit: IDE-wide polish and latency.

## Agent implementation rules
- Project rule, data: Model selected repositories or projects, operations, results, revisions, and run logs; keep stable source IDs and timestamps.
- Project rule, behavior: For AI coding agents, read one selected workspace and define which files may be indexed or edited.
- Project rule, recovery: Rejecting a suggestion must leave the buffer unchanged; accepting it must be reversible with the editor undo stack.

## 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 coding agents operation with sample or user-owned data: For AI coding agents, read one selected workspace and define which files may be indexed or edited.
- Show a concrete result or export: Apply or reject the change and show its source context and diff.
- State the practical limit: IDE-wide polish and latency.

## Engineering roadmap
1. Phase 1, architecture and data: Use exactly this stack: Python 3.12 + Typer + SQLite. Model selected repositories or projects, operations, results, revisions, and run logs; keep stable source IDs and timestamps.
2. Phase 2, implement: For AI coding agents, read one selected workspace and define which files may be indexed or edited.
3. Phase 3, implement: Offer an inspectable suggestion or edit preview before modifying the editor buffer.
4. Phase 4, review and output: Apply or reject the change and show its source context and diff.
5. Phase 5, recovery and acceptance: Verify this invariant with a saved fixture: Rejecting a suggestion must leave the buffer unchanged; accepting it must be reversible with the editor undo stack. State the practical limit: IDE-wide polish and latency.

## Paid-product capabilities outside this build
- IDE-wide polish and latency
- enterprise policy, telemetry, and support
- frontier coding model quality
- large-context infrastructure

## Implementation prompt
Build me a focused AI coding agents workflow for the personal core of Zencoder. Requirements:

- Use exactly this stack: Python 3.12 + Typer + SQLite. Model selected repositories or projects, operations, results, revisions, and run logs; keep stable source IDs and timestamps.
- Paid product context: IDE agents for code generation, refactoring, tests, and repository tasks. Build only this DIY scope: Build a repository-local AI coding agents assistant that indexes one codebase, calls one model, proposes diffs, runs tests, and records every change.
- For AI coding agents, read one selected workspace and define which files may be indexed or edited.
- Offer an inspectable suggestion or edit preview before modifying the editor buffer.
- Apply or reject the change and show its source context and diff.
- Use a CLI with a dry-run or preview command and an explicit output path. Required input or access: Git; OpenAI API key in .env. Keep credentials in .env.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: Rejecting a suggestion must leave the buffer unchanged; accepting it must be reversible with the editor undo stack.
- Out of scope: IDE-wide polish and latency; enterprise policy, telemetry, and support. 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.