# Future Ideas — website-evaluator

## M2: Claude API Analysis Layer

**Problem:** The report is structured data — Claude still has to read it and form recommendations
manually each time.

**Idea:** Add an optional `--analyze` flag. After generating the report, call the Claude API
with the report + screenshots (vision) and append an AI-generated "Recommendations" section
with prioritised, actionable fixes.

**Open questions:**
- Should the recommendations overwrite a section or be a separate file?
- Which Claude model for analysis? Sonnet for cost, Opus for depth?
- How to handle the screenshot as base64 input to the API?

**Depends on:** M1 verified and stable.

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## M3: Comparison Mode

**Problem:** It's hard to tell if improvements actually moved the needle without a before/after view.

**Idea:** Accept two URLs (or a URL + cached baseline) and produce a diff report highlighting
changes in scores, new/resolved issues, and visual diffs between screenshots.

**Open questions:**
- Store baselines as JSON in the output dir? Or accept two full report.md files?
- Visual diff: pixel-diff the screenshots or just note score changes?

**Depends on:** M1 stable. M2 optional.

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## Batch Mode

**Problem:** Evaluating 10 pages of a site requires running the container 10 times.

**Idea:** Accept a newline-delimited list of URLs and produce one report per URL plus
a summary roll-up.

**Open questions:**
- Output structure: one folder per URL? Or a single multi-page report?

**Depends on:** M1 stable.

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## CI Integration Helper

**Problem:** Running the evaluator manually is fine for one-offs but doesn't scale to
catching regressions in CI.

**Idea:** Produce a machine-readable `scores.json` alongside the Markdown report.
Add a `--fail-under` flag that exits non-zero if any Lighthouse category drops below
a threshold. This makes it usable as a CI gate.

**Depends on:** M1 stable.