chore: add mypy CI, extract review prompt, add component tests (#4312)

## Summary

Three high-impact improvements from the TAC audit (scored 7.8/10, Level
4 — Orchestrated):

- **Add mypy type checking to CI** (LP6, Impact 8) — adds `[tool.mypy]`
config to `pyproject.toml` with gradual-adoption overrides for 11
modules with systemic library-interaction issues. New mypy step in
`ci-lint.yml` checks `api/` and `core/` on every Python change.
- **Extract impl-review.yml inline prompt** (LP3, Impact 7) —
consolidates the ~131-line inline prompt into
`prompts/workflow-prompts/ai-quality-review.md` and replaces it with a
thin 5-line wrapper, matching the pattern used by `impl-generate.yml`.
- **Add frontend component tests** (LP9, Impact 7) — installs
testing-library + jsdom, configures vitest for component testing, adds
25 new tests across 4 files: `useLocalStorage` (7), `useFilterState`
(5), `ErrorBoundary` (5), `ImageCard` (8).

## Test plan

- [x] `uv run --extra typecheck mypy api core --pretty` — 0 errors (32
files checked)
- [x] `uv run ruff check core/ api/ && uv run ruff format --check core/
api/` — all passing
- [x] `uv run pytest tests/unit/ -v` — 1044 passed
- [x] `cd app && yarn test` — 44 passed (6 suites)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: MarkusNeusinger

.github/workflows/ci-lint.yml

@@ -73,6 +73,10 @@ jobs:
       if: steps.check.outputs.should_lint == 'true'
       run: uv run ruff format --check .
 
+    - name: Run type checking
+      if: steps.check.outputs.should_lint == 'true'
+      run: uv run --extra typecheck mypy api core --pretty
+
     - name: Skip notice
       if: steps.check.outputs.should_lint == 'false'
       run: echo "::notice::Linting skipped - no Python files changed"

.github/workflows/impl-review.yml

@@ -132,136 +132,13 @@ jobs:
           claude_args: "--model opus"
           allowed_bots: '*'
           prompt: |
-            ## Task: AI Quality Review for **${{ steps.pr.outputs.library }}** (Attempt ${{ steps.attempts.outputs.display }}/3)
+            Read `prompts/workflow-prompts/ai-quality-review.md` and follow those instructions.
 
-            Review the implementation and evaluate if it meets quality standards.
-
-            ### Your Task
-
-            1. **Read the specification**: `plots/${{ steps.pr.outputs.specification_id }}/specification.md`
-
-            2. **Read the implementation**:
-               `plots/${{ steps.pr.outputs.specification_id }}/implementations/${{ steps.pr.outputs.library }}.py`
-
-            3. **Read library rules**: `prompts/library/${{ steps.pr.outputs.library }}.md`
-
-            4. **Read impl-tags guide**: `prompts/impl-tags-generator.md` (for step 8)
-
-            5. **MANDATORY: View the plot image**
-               - You MUST use the Read tool to open `plot_images/plot.png`
-               - Visually analyze the image - this is critical for the review
-               - DO NOT skip this step - a review without seeing the image is invalid
-               - If the image cannot be read, STOP and report the error
-
-            6. **Evaluate against quality criteria** from `prompts/quality-criteria.md`
-
-            7. **Post verdict as PR comment** on PR #${{ steps.pr.outputs.pr_number }}:
-
-            ```markdown
-            ## AI Review - Attempt ${{ steps.attempts.outputs.display }}/3
-
-            ### Image Description
-            > Describe what you see in the plot: colors used, axis labels, title, data representation, overall layout.
-            > This proves you actually looked at the image.
-
-            ### Quality Score: XX/100
-
-            ### Criteria Checklist
-            **Visual Quality (40 pts)**
-            - [ ] VQ-01: Text Legibility (10) - all text readable at full size
-            - [ ] VQ-02: No Overlap (8) - no overlapping text
-            - [ ] VQ-03: Element Visibility (8) - markers/lines sized for data density
-            - [ ] VQ-04: Color Accessibility (5) - colorblind-safe
-            - [ ] VQ-05: Layout Balance (5) - good proportions
-
-            **Spec Compliance (25 pts)**
-            - [ ] SC-01: Plot Type (8) - correct chart type
-            - [ ] SC-02: Data Mapping (5) - X/Y correctly assigned
-            - [ ] SC-03: Required Features (5) - all spec features present
-            - [ ] SC-06: Title Format (2) - uses {spec-id} · {library} · pyplots.ai
-
-            **Data Quality (20 pts)**
-            - [ ] DQ-01: Feature Coverage (8) - shows ALL aspects of plot type
-            - [ ] DQ-02: Realistic Context (7) - plausible scenario
-            - [ ] DQ-03: Appropriate Scale (5) - sensible values
-
-            **Code Quality (10 pts)**
-            - [ ] CQ-01: KISS Structure (3) - no functions/classes
-            - [ ] CQ-02: Reproducibility (3) - fixed seed
-
-            **Library Features (5 pts)**
-            - [ ] LF-01: Uses distinctive library features
-
-            ### Strengths
-            - Strength 1 (keep these aspects)
-            - Strength 2
-
-            ### Weaknesses
-            - Weakness 1 (AI will fix these - let it decide HOW)
-
-            ### Verdict: APPROVED / REJECTED
-            ```
-
-            8. **Save review data to files** (for the workflow to parse):
-               ```bash
-               echo "XX" > quality_score.txt
-
-               # Save structured feedback as JSON (one array per file)
-               echo '["Strength 1", "Strength 2"]' > review_strengths.json
-               echo '["Weakness 1"]' > review_weaknesses.json
-
-               # Save verdict
-               echo "APPROVED" > review_verdict.txt  # or "REJECTED"
-
-               # Save image description (multi-line text)
-               cat > review_image_description.txt << 'EOF'
-               The plot shows a scatter plot with blue markers...
-               [Your full image description here]
-               EOF
-
-               # Save criteria checklist as structured JSON
-               cat > review_checklist.json << 'EOF'
-               {
-                 "visual_quality": {
-                   "score": 36,
-                   "max": 40,
-                   "items": [
-                     {"id": "VQ-01", "name": "Text Legibility", "score": 10, "max": 10, "passed": true, "comment": "All text readable"},
-                     {"id": "VQ-02", "name": "No Overlap", "score": 8, "max": 8, "passed": true, "comment": "No overlapping elements"}
-                   ]
-                 },
-                 "spec_compliance": {"score": 23, "max": 25, "items": [...]},
-                 "data_quality": {"score": 18, "max": 20, "items": [...]},
-                 "code_quality": {"score": 10, "max": 10, "items": [...]},
-                 "library_features": {"score": 5, "max": 5, "items": [...]}
-               }
-               EOF
-               ```
-
-            9. **Generate impl_tags** (based on prompts/impl-tags-generator.md):
-               Analyze the implementation code and create impl_tags with 5 dimensions:
-               - `dependencies`: External packages beyond numpy/pandas/plotting library
-               - `techniques`: Visualization techniques (twin-axes, colorbar, etc.)
-               - `patterns`: Code patterns (data-generation, iteration-over-groups, etc.)
-               - `dataprep`: Data transformations (kde, binning, correlation-matrix, etc.)
-               - `styling`: Visual style (publication-ready, alpha-blending, etc.)
-
-               ```bash
-               cat > review_impl_tags.json << 'EOF'
-               {
-                 "dependencies": [],
-                 "techniques": ["colorbar", "annotations"],
-                 "patterns": ["data-generation"],
-                 "dataprep": [],
-                 "styling": ["publication-ready"]
-               }
-               EOF
-               ```
-
-            10. **DO NOT add ai-approved or ai-rejected labels** - the workflow will add them after updating metadata.
-
-            **IMPORTANT**: Your review MUST include the "Image Description" section. A review without an image description will be considered invalid.
-            **IMPORTANT**: All review data (strengths, weaknesses, image_description, criteria_checklist) is saved to metadata for future regeneration. Be specific!
+            Variables for this run:
+            - LIBRARY: ${{ steps.pr.outputs.library }}
+            - SPEC_ID: ${{ steps.pr.outputs.specification_id }}
+            - PR_NUMBER: ${{ steps.pr.outputs.pr_number }}
+            - ATTEMPT: ${{ steps.attempts.outputs.display }}
 
       - name: Extract quality score
         id: score