Build out all 27 modules + capstone (#1)

Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>

modules/27-evals/lab/eval_set.py

@@ -0,0 +1,37 @@
+"""The eval set for the tasks-app `pending_count` agent task.
+
+An *eval set* is a list of CASES. Each case is three things:
+
+  - a name (so the scorecard is readable),
+  - an input (here: the state a TaskList is in), and
+  - the expected result (here: how many tasks should count as pending).
+
+The grading lives in run_eval.py; this file is just data. Keeping the cases
+separate from any model, prompt, or runner is the whole point — the same eval
+set judges *any* candidate you point it at, which is what makes it useful when
+you swap the model out from under it.
+
+The task we're evaluating: an agent was asked to implement
+`TaskList.pending_count()` so it returns the number of tasks that are NOT done.
+That sounds trivial. The discriminating cases below are the ones a
+"looks-right" implementation quietly fails.
+"""
+
+# Each case: (name, [(title, done), ...], expected_pending_count)
+CASES = [
+    ("empty list has zero pending", [], 0),
+    ("one open task counts as one", [("write tests", False)], 1),
+    (
+        "three open tasks count as three",
+        [("a", False), ("b", False), ("c", False)],
+        3,
+    ),
+    # The discriminating case. A candidate that returns len(tasks) passes
+    # everything above and fails right here. This is the eval earning its keep.
+    (
+        "completed tasks are NOT pending",
+        [("done thing", True), ("open thing", False), ("also done", True)],
+        1,
+    ),
+    ("all done means zero pending", [("x", True), ("y", True)], 0),
+]