AI cost/ROI page: pricing table, cost per call, Cost tab, ROI endpoint

- templates/web/pricing.py: pricing table with source and as-of date; cost_for_call
  (input rate + (output + thinking) at output rate, long-context above threshold;
  unknown model raises naming the known models).
- Cost tab: coverage pill, total/this-month/tokens tiles, month-by-month table,
  per-run tokens and cost ("not priced", never $0), by-hand panel with editable
  assumptions labelled as such.
- Contract: RoiRun and GET /roi; optional GET /models and POST /runs/{id}/retry.
- SKILL.md: cost page wiring, model picker and retry-on-other-model as optional sections.
- Tests: test_pricing.py self-check, page_check_ai/test_ai_cost.py.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

rpa-dashboard-ai/templates/web/pricing.py

@@ -0,0 +1,32 @@
+"""Pricing table and cost per call for the cost page. The backend imports it.
+
+Rates are USD per million tokens. Thinking tokens bill at the output rate. A model
+with "long_threshold" switches both rates to its *_long rates when a call's input
+tokens go above that threshold.
+
+Replace the table with the rates of the provider and models the project really uses,
+copied from the provider's pricing page, and update PRICING_SOURCE and PRICING_AS_OF.
+Never guess a rate: a model missing here is an error, never a cost of zero.
+"""
+
+PRICING_SOURCE = "https://ai.google.dev/gemini-api/docs/pricing"
+PRICING_AS_OF = "2026-08-11"
+
+PRICING = {
+    "gemini-2.5-pro": {"input": 1.25, "output": 10.00,
+                       "long_threshold": 200_000, "input_long": 2.50, "output_long": 15.00},
+    "gemini-2.5-flash": {"input": 0.30, "output": 2.50},
+    "gemini-2.5-flash-lite": {"input": 0.10, "output": 0.40},
+}
+
+
+def cost_for_call(model, input_tokens, output_tokens, thinking_tokens=0):
+    """USD for one call. Raises ValueError, naming the known models, for a model not in PRICING."""
+    if model not in PRICING:
+        raise ValueError(f"No pricing for model '{model}'. Add it to PRICING from {PRICING_SOURCE}. "
+                         f"Known models: {', '.join(PRICING)}")
+    p = PRICING[model]
+    in_rate, out_rate = p["input"], p["output"]
+    if "long_threshold" in p and input_tokens > p["long_threshold"]:
+        in_rate, out_rate = p["input_long"], p["output_long"]
+    return (input_tokens * in_rate + (output_tokens + thinking_tokens) * out_rate) / 1_000_000