/fix planner count question

Scalar "how many X" questions pulled raw rows and let the assembler LLM
tally them, which miscounted (188 vs actual 207). Add a count-aggregate
recipe (R2b) in planner.md plus a few-shot (Example L) so the planner
emits a COUNT(*) IR with the filter, returning the exact count in one
row. No tool/IR/compiler change; verified live (planner now plans a
single retrieve_data count task, answer = 207).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/agents/planner/examples.py

@@ -843,6 +843,68 @@ _EXAMPLE_K = TaskList(
 )
 
 
+# --------------------------------------------------------------------------- #
+# Example L — scalar count with a filter (no grouping, no analyze step).
+# "How many orders have zero revenue?"
+# Shows: a "how many rows match X" question is answered by a SINGLE retrieve_data
+# IR whose select is a COUNT(*) aggregate ({"kind": "agg", "fn": "count"}, with
+# column_id omitted) plus the filter — it returns the exact number in one row.
+# Do NOT select the raw column and count the returned rows: that caps at `limit`
+# and leaves the tally to be eyeballed. count(*) omits column_id (the validator
+# lets only 'count' do so); no group_by is needed for a scalar count.
+# --------------------------------------------------------------------------- #
+
+_EXAMPLE_L = TaskList(
+    plan_id="example_l",
+    goal_restated="Count how many orders have revenue equal to 0.",
+    assumptions=[],
+    open_questions=[],
+    tasks=[
+        Task(
+            id="t1",
+            stage="data_understanding",
+            objective="Confirm the sales source exposes order revenue.",
+            tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
+            expected_output="source_shape",
+            success_criteria="Produced the orders table schema; revenue is present.",
+            depends_on=[],
+            estimated_cost="low",
+        ),
+        Task(
+            id="t2",
+            stage="evaluation",
+            objective="Count the orders whose revenue equals 0.",
+            tool_calls=[
+                ToolCall(
+                    tool="retrieve_data",
+                    args={
+                        "ir": {
+                            "source_id": "src_sales",
+                            "table_id": "t_orders",
+                            "select": [
+                                {"kind": "agg", "fn": "count", "alias": "order_count"}
+                            ],
+                            "filters": [
+                                {
+                                    "column_id": "c_revenue",
+                                    "op": "=",
+                                    "value": 0,
+                                    "value_type": "decimal",
+                                }
+                            ],
+                        }
+                    },
+                )
+            ],
+            expected_output="zero_revenue_count",
+            success_criteria="Produced one row holding the count of zero-revenue orders.",
+            depends_on=["t1"],
+            estimated_cost="low",
+        ),
+    ],
+)
+
+
 EXAMPLES: list[tuple[str, TaskList]] = [
     ("Which product categories drove last quarter's revenue?", _EXAMPLE_A),
     ("How has monthly revenue trended by region this year, and what's unusual?", _EXAMPLE_B),
@@ -859,6 +921,7 @@ EXAMPLES: list[tuple[str, TaskList]] = [
     ),
     ("Show me a bar chart of total revenue per product category.", _EXAMPLE_J),
     ("Plot the customer churn rate by month as a line chart.", _EXAMPLE_K),
+    ("How many orders have zero revenue?", _EXAMPLE_L),
 ]
 
 

src/config/prompts/planner.md

@@ -46,6 +46,7 @@ recipe verbatim; a genuinely multi-part question composes recipes.
 |---|---|---|
 | R1 descriptive | a summary/distribution of columns | `retrieve_data` → `analyze_descriptive` |
 | R2 aggregate / top-N | totals or averages per group, "top N by …" | ONE grouped `retrieve_data` IR (± `analyze_aggregate`) |
+| R2b scalar count/total | a single number with NO grouping — "how many rows match X", "berapa banyak …", "total …" | ONE `retrieve_data` IR with a `count`/`sum` aggregate + filter, NO `group_by`, NO `analyze_*` step |
 | R3 trend | movement over time | `retrieve_data` → `analyze_trend` |
 | R4 correlation | the relationship between numeric columns | `retrieve_data` → `analyze_correlation` |
 | R5 two-metric merge | "which X has both A and B" | `retrieve_data` ×2 → `analyze_merge` → … |
@@ -75,6 +76,15 @@ recipe verbatim; a genuinely multi-part question composes recipes.
   so an `analyze_*` fed from them finds no columns to analyze and fails.
   `check_data` is only for inspecting *what exists*; always `retrieve_data` to
   pull the rows before analyzing them.
+- **Counting rows is a `count` aggregate, not a manual tally.** For a "how many
+  rows match X" / "berapa banyak" question — a single scalar count with no
+  grouping — emit ONE `retrieve_data` IR whose `select` is
+  `[{"kind": "agg", "fn": "count"}]` (COUNT(*); `column_id` omitted) plus the
+  filter. It returns the exact number in one row. Do **NOT** `select` the raw
+  column and let a later step (or the reader) count the returned rows — that caps
+  at `limit` and leaves the tally to be eyeballed, so the count comes out wrong.
+  A scalar total/min/max/avg (no grouping) works the same way: aggregate it in the
+  IR, don't pull raw rows.
 - **Measure by a dimension in another table (joins).** When the number you are
   aggregating and the grouping dimension live in DIFFERENT tables of the same
   database source, add a `joins` entry to the `retrieve_data` IR. **Join ONLY on a