Add comprehensive architecture documentation and related scripts

- Introduced multiple architecture documentation files covering building block view, runtime view, deployment view, concepts, architecture decisions, quality requirements, technical risks, glossary, UI and styling, testing, CI, and development setup.
- Migrated existing content from `architecture_overview.md` and `implementation_plan.md` into structured documentation.
- Created scripts for checking broken links in documentation and formatting Markdown files for consistency.
- Updated quickstart guide to provide clearer setup instructions and usage overview.
- Removed outdated MVP features and testing strategy documents to streamline documentation.

docs/architecture/06_runtime_view.md

@@ -0,0 +1,33 @@
+# 06 — Runtime View
+
+Status: skeleton
+
+Describe runtime aspects: request flows, lifecycle of key interactions, and runtime components.
+
+## Reporting Pipeline and UI Integration
+
+1. **Data Sources**
+
+   - Scenario-linked calculations (costs, consumption, production) produce raw figures stored in dedicated tables (`capex`, `opex`, `consumption`, `production_output`).
+   - Monte Carlo simulations (currently transient) generate arrays of `{ "result": float }` tuples that the dashboard or downstream tooling passes directly to reporting endpoints.
+
+2. **API Contract**
+
+   - `POST /api/reporting/summary` accepts a JSON array of result objects and validates shape through `_validate_payload` in `routes/reporting.py`.
+   - On success it returns a structured payload (`ReportSummary`) containing count, mean, median, min/max, standard deviation, and percentile values, all as floats.
+
+3. **Service Layer**
+
+   - `services/reporting.generate_report` converts the sanitized payload into descriptive statistics using Python’s standard library (`statistics` module) to avoid external dependencies.
+   - The service remains stateless; no database read/write occurs, which keeps summary calculations deterministic and idempotent.
+   - Extended KPIs (surfaced in the API and dashboard):
+     - `variance`: population variance computed as the square of the population standard deviation.
+     - `percentile_5` and `percentile_95`: lower and upper tail interpolated percentiles for sensitivity bounds.
+     - `value_at_risk_95`: 5th percentile threshold representing the minimum outcome within a 95% confidence band.
+     - `expected_shortfall_95`: mean of all outcomes at or below the `value_at_risk_95`, highlighting tail exposure.
+
+4. **UI Consumption**
+
+   - `templates/Dashboard.html` posts the user-provided dataset to the summary endpoint, renders metric cards for each field, and charts the distribution using Chart.js.
+   - `SUMMARY_FIELDS` now includes variance, 5th/10th/90th/95th percentiles, and tail-risk metrics (VaR/Expected Shortfall at 95%); tooltip annotations surface the tail metrics alongside the percentile line chart.
+   - Error handling surfaces HTTP failures inline so users can address malformed JSON or backend availability issues without leaving the page.