Add basic version: SemIf decision engine, urgency queue, skill tree, skill loop, decision log, dream pass, CLI

semif_agent/skill.py

@@ -0,0 +1,65 @@
+"""The skill execution loop: observe -> predict -> act -> observe -> assess.
+
+Every SemIf decision made during a run is logged as a training row; the
+assessment outcome is kept on the row so the dream pass can weigh failed runs.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from .decisions import Request
+from .llm import Assessment, LLMClient
+from .log import DecisionLog
+from .skills import ActionContext, Prediction, Skill
+
+
+@dataclass
+class RunResult:
+    skill: str
+    success: bool
+    summary: str
+    action_log: str
+    new_state: str
+    updated_request: str | None = None
+    decisions_logged: int = 0
+    error: str | None = None
+
+
+class SkillRunner:
+    def __init__(self, ctx: ActionContext, llm: LLMClient, log: DecisionLog):
+        self.ctx = ctx
+        self.llm = llm
+        self.log = log
+
+    def run(self, skill: Skill, request: Request) -> RunResult:
+        baseline = request.text
+        try:
+            prediction = skill.predict(self.ctx, request) if skill.predict else Prediction(text="")
+            action = skill.act(self.ctx, request, prediction)
+            observed = action.new_state
+            assessment: Assessment = self.llm.assess(skill.name, baseline, action.action_log)
+        except Exception as exc:
+            return RunResult(
+                skill=skill.name,
+                success=False,
+                summary="",
+                action_log="",
+                new_state=baseline,
+                error=str(exc),
+            )
+
+        run_ok = assessment.success
+        decisions = getattr(prediction, "decisions", [])
+        for decision, result in decisions:
+            self.log.append(decision, result, extra={"skill": skill.name, "run_ok": run_ok})
+
+        return RunResult(
+            skill=skill.name,
+            success=assessment.success,
+            summary=assessment.summary,
+            action_log=action.action_log,
+            new_state=observed,
+            updated_request=assessment.updated_request,
+            decisions_logged=len(decisions),
+        )