M7.2: share the loop's parts

Shared parts, not a shared scheduler (agreed): nisse-review will have its
own small scheduler; a common one waits until two exist to compare.

- nisse_core.loop.driver: the driver run, moved; the agent names its
  waiting tools per run (nisse-dev's are WAITING_TOOLS), and execution
  goes to any Executor (ToolOutcome moves with it).
- nisse_core.loop.tools: the tool helper and the tools any agent offers
  (notes, memory, memory_done, ask_expert); nisse_core.loop.expert.
- nisse_core.knowledge.handlers: Knowledge, the vault and memory as
  control tools, the index and prompt text, out of LoopAgent.
- nisse_core.config: file reading, references, people, tuning sections of
  the agent's own, LLM numbers, the vault.
- New core tests: a driver run with a non-workspace executor and the
  agent's own waiting set; Knowledge alone; the shared config pieces.

Docs: the Development Plan marks M7.2 done; Architecture Part L and the
nisse-core README name the new parts.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01J5Utyfzw7gF7UhjRDEtjTk

nisse-core/tests/test_shared_config.py

@@ -0,0 +1,70 @@
+"""Configuration any agent builds on: references resolved from the
+environment, people lists, tuning sections of the agent's own, the LLM's
+numbers, and the vault; every problem collected, none raised one by one."""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from nisse_core.config import (
+    flatten,
+    llm_numbers,
+    parse_tuning,
+    parse_vault,
+    people,
+    resolve,
+)
+
+
+@dataclass(frozen=True)
+class Review:
+    max_rounds: int = 3
+    wait_s: float = 60.0
+
+
+@dataclass(frozen=True)
+class Tuning:
+    review: Review = field(default_factory=Review)
+
+
+def test_an_agents_own_tuning_sections() -> None:
+    errors: list[str] = []
+    tuning = parse_tuning({"review": {"max_rounds": 2}}, Tuning, errors)
+    assert tuning == Tuning(Review(max_rounds=2)) and errors == []
+    assert parse_tuning(None, Tuning, errors) == Tuning()
+    parse_tuning(
+        {"review": {"max_rounds": 0, "x": 1}, "old": {}, "new": {}},
+        Tuning,
+        errors,
+        retired={"old": "use review"},
+    )
+    assert errors == [
+        "tuning.review.max_rounds: must be a positive number",
+        "tuning.review.x: unknown setting",
+        "tuning.old: retired — use review",
+        "tuning.new: unknown section",
+    ]
+
+
+def test_references_and_people() -> None:
+    errors: list[str] = []
+    env = {"TOKEN": "s3cret"}
+    assert resolve("${TOKEN}", "gitea.token", env, errors) == "s3cret"
+    resolve("${NOPE}", "llm.token", env, errors)
+    assert errors == ["llm.token: references ${NOPE}, which is unset or empty"]
+    assert people("don, ,ann,") == {"don", "ann"}
+    assert flatten({"llm": {"base_url": "u"}, "tuning": {"a": {}}}) == {
+        "llm.base_url": "u",
+        "tuning": {"a": {}},
+    }
+
+
+def test_llm_numbers_and_the_vault(tmp_path: Path) -> None:
+    errors: list[str] = []
+    assert llm_numbers({"llm.max_output_tokens": 100}, errors)[0] == 100
+    llm_numbers({"llm.request_timeout_s": -1}, errors)
+    assert errors == ["llm.request_timeout_s: must be a positive number of seconds"]
+    vault = parse_vault({"root": str(tmp_path), "folders": {"notes": "ro"}}, [])
+    assert vault is not None and vault.folders == {"notes": "ro"}
+    bad: list[str] = []
+    assert parse_vault({"root": "/v", "folders": {"../x": "rw"}}, bad) is None
+    assert bad == ["vault.folders.../x: a folder inside the vault"]