Fit every model request to its context window

Every turn re-sent the whole transcript with nothing checking it against
the model's window. On a local model that fails silently: Ollama drops
the start of an over-long prompt, which is where the system prompt and
the plan are. Measured on the Jetson's llama3.2, cut at 4096 tokens with
no error.

- The model catalog reads max_input_tokens and max_output_tokens from
  LiteLLM's model_info, and startup refuses a role on a model with no
  declared window
- BudgetedClient wraps both routes. It fits the canonical events before
  rendering: older tool results cut to head and tail, then the oldest
  turns dropped with a note, keeping calls with their results; a request
  that still cannot fit fails with ContextOverflow and is never sent.
  The output ceiling is the smaller of config and the model's limit
- Tokens are estimated from characters, pessimistically, and the
  estimate is corrected from reported prompt sizes, only ever upward
- The executor takes its scope from the log: the latest plan, which is
  the approved milestone plan, goes in its system prompt, and it sees
  only events after it
- Tool output keeps its head and, mostly, its tail, so a test runner's
  failure summary survives truncation
- tuning.context.keep_recent_turns and safety_margin are new knobs
- scripts/probe_context.py checks a declared window against what the
  server does; it confirmed llama3.2 cuts at the declared 4096
- The tuning guide covers the budget, declaring a local model's limits,
  and the probe

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

nisse-dev/src/nisse_dev/executor.py

@@ -15,7 +15,7 @@ from dataclasses import dataclass
 from typing import Any, Literal
 
 from nisse_dev.cancel import Cancel, skip_calls
-from nisse_dev.canonical import ToolCall, ToolResult
+from nisse_dev.canonical import PlanSubmitted, StoredEvent, ToolCall, ToolResult
 from nisse_dev.delegation import Delegation
 from nisse_dev.expert import ASK_EXPERT_TOOL, BudgetExhausted, ExpertDesk
 from nisse_dev.llm import LLMClient, LLMError, RoleMap
@@ -24,7 +24,7 @@ from nisse_dev.tools import EXECUTOR_TOOLS, MUTATING_TOOLS, ToolOutcome, Workspa
 
 EXECUTOR_SYSTEM = """You are nisse-dev's executor, doing mechanical work.
 
-Follow the approved plan above exactly. It was written to be executed
+Follow the approved plan below exactly. It was written to be executed
 verbatim; do not redesign it, expand its scope, or skip steps.
 
 Work by calling tools. Verify your work by running the project's tests.
@@ -145,12 +145,34 @@ class LLMExecutor:
         except LLMError as exc:
             return ToolOutcome(f"the consultant did not answer: {exc}", success=False)
 
+    def _scope(self) -> tuple[str | None, int]:
+        """The approved plan, and where this milestone's events begin.
+
+        The most recent plan in the log is the approved milestone plan: no
+        plan is submitted between approval and execution, or during review
+        responses. The executor sees that plan and what came after it, and
+        not the planning conversation or earlier milestones, which a small
+        context window cannot afford and the plan already distils.
+        """
+        latest: StoredEvent | None = None
+        for stored in self._store.read_events():
+            if isinstance(stored.event, PlanSubmitted):
+                latest = stored
+        if latest is None:
+            return None, 0
+        assert isinstance(latest.event, PlanSubmitted)
+        return latest.event.plan, latest.id
+
     def run(self, milestone: int) -> ExecutorOutcome:
         model = self._roles.resolve("executor")
+        plan, after = self._scope()
         system = (
             f"{EXECUTOR_SYSTEM}\n\n{self._workspace.describe()}"
             f"\n\nYou are executing milestone {milestone}."
         )
+        if plan is not None:
+            # In the system prompt, so the context budget never drops it.
+            system += f"\n\nThe approved plan:\n{plan}"
         consecutive_failures = 0
         did_work = False
         challenged = False
@@ -162,7 +184,7 @@ class LLMExecutor:
                 return ExecutorOutcome("stopped", f"before turn {turn + 1}")
             try:
                 produced = self._client.complete(
-                    self._store.read_events(),
+                    self._store.read_events(after=after),
                     tools=self._tools(),
                     model=model,
                     system=system,