Keyword Chef

Build me a focused keyword research workflow for the personal core of Keyword Chef. Requirements:

- Use exactly this stack: Python 3.12 + Typer + SQLite + Playwright. Model sites, crawl pages, keyword snapshots, audit findings, and report dates; keep stable source IDs and timestamps.
- Paid product context: Credit-based keyword discovery and SERP filtering for low-competition topics. Build only this DIY scope: Build a focused keyword research audit for user-owned sites that crawls pages, records technical signals, and exports a prioritized CSV and HTML report.
- For keyword research, crawl a user-owned site or import licensed search data with fetch timestamps.
- Compute the named technical or ranking findings from stored source observations.
- Show prioritized findings and export a CSV or HTML report with source URLs.
- Use a CLI with a dry-run or preview command and an explicit output path. Required input or access: Playwright browsers; permission to crawl the target site.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: A failed crawl retains its last dated snapshot; a report must not imply backlink or keyword coverage the input lacks.
- Out of scope: historical competitor datasets and support; fresh proprietary keyword, backlink, or 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 Keyword Chef

## Project scope
Build a focused keyword research audit for user-owned sites that crawls pages, records technical signals, and exports a prioritized CSV and HTML report.

Catalogue verdict: no. Do not mistake the interface for the product. Keyword Chef's durable value is proprietary data, index, crawl scale, 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
- Playwright browsers
- permission to crawl the target site
- 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 + Playwright.
- Model sites, crawl pages, keyword snapshots, audit findings, and report dates; keep stable source IDs and timestamps.
- Interface for this keyword research workflow: a CLI with a dry-run or preview command and an explicit output path.

## Security and data integrity
- A failed crawl retains its last dated snapshot; a report must not imply backlink or keyword coverage the input lacks.
- Validate imported records and file paths; keep user data within the storage location named in the prompt.
- State the practical limit: historical competitor datasets and support.

## Agent implementation rules
- Project rule, data: Model sites, crawl pages, keyword snapshots, audit findings, and report dates; keep stable source IDs and timestamps.
- Project rule, behavior: For keyword research, crawl a user-owned site or import licensed search data with fetch timestamps.
- Project rule, recovery: A failed crawl retains its last dated snapshot; a report must not imply backlink or keyword coverage the input lacks.

## 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 keyword research operation with sample or user-owned data: For keyword research, crawl a user-owned site or import licensed search data with fetch timestamps.
- Show a concrete result or export: Show prioritized findings and export a CSV or HTML report with source URLs.
- State the practical limit: historical competitor datasets and support.

## Engineering roadmap
1. Phase 1, architecture and data: Use exactly this stack: Python 3.12 + Typer + SQLite + Playwright. Model sites, crawl pages, keyword snapshots, audit findings, and report dates; keep stable source IDs and timestamps.
2. Phase 2, implement: For keyword research, crawl a user-owned site or import licensed search data with fetch timestamps.
3. Phase 3, implement: Compute the named technical or ranking findings from stored source observations.
4. Phase 4, review and output: Show prioritized findings and export a CSV or HTML report with source URLs.
5. Phase 5, recovery and acceptance: Verify this invariant with a saved fixture: A failed crawl retains its last dated snapshot; a report must not imply backlink or keyword coverage the input lacks. State the practical limit: historical competitor datasets and support.

## Paid-product capabilities outside this build
- historical competitor datasets and support
- fresh proprietary keyword, backlink, or ranking data
- proprietary web and keyword indexes
- fresh rank and clickstream data
- large-scale crawling infrastructure

## Implementation prompt
Build me a focused keyword research workflow for the personal core of Keyword Chef. Requirements:

- Use exactly this stack: Python 3.12 + Typer + SQLite + Playwright. Model sites, crawl pages, keyword snapshots, audit findings, and report dates; keep stable source IDs and timestamps.
- Paid product context: Credit-based keyword discovery and SERP filtering for low-competition topics. Build only this DIY scope: Build a focused keyword research audit for user-owned sites that crawls pages, records technical signals, and exports a prioritized CSV and HTML report.
- For keyword research, crawl a user-owned site or import licensed search data with fetch timestamps.
- Compute the named technical or ranking findings from stored source observations.
- Show prioritized findings and export a CSV or HTML report with source URLs.
- Use a CLI with a dry-run or preview command and an explicit output path. Required input or access: Playwright browsers; permission to crawl the target site.
- Acceptance: with one labelled sample, show the input, saved intermediate state, and exported result; verify this invariant: A failed crawl retains its last dated snapshot; a report must not imply backlink or keyword coverage the input lacks.
- Out of scope: historical competitor datasets and support; fresh proprietary keyword, backlink, or 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.