feat(learning): add learning runtime with pattern mining

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
2026-04-08 00:11:51 +08:00
parent 72a60c698a
commit 36c93a764f
13 changed files with 1936 additions and 0 deletions
--- a/backend/app/agents/learning/signal_extractor.py
+++ b/backend/app/agents/learning/signal_extractor.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from app.agents.schemas.learning import LearningSignal, SessionRetrospective
+
+
+class RetrospectiveSignalExtractor:
+    def extract(self, retrospective: SessionRetrospective) -> list[LearningSignal]:
+        signals: list[LearningSignal] = []
+
+        if retrospective.outcome == "completed":
+            signals.append(
+                LearningSignal(
+                    signal_type="workflow",
+                    confidence=0.8,
+                    evidence_refs=retrospective.evidence_refs[:3],
+                    explanation="Completed runs can be mined as workflow hints later.",
+                    payload={
+                        "task_type": retrospective.task_type,
+                        "execution_mode": retrospective.execution_mode,
+                    },
+                )
+            )
+
+        if len(retrospective.task_refs) > 1:
+            context_snapshot = retrospective.context_snapshot or {}
+            merge_report = dict(context_snapshot.get("merge_report") or {})
+            verification_report = dict(context_snapshot.get("verification_report") or {})
+            effectiveness_score = 1.0
+            if merge_report.get("status") == "conflicted":
+                effectiveness_score = 0.45
+            elif merge_report.get("status") == "fallback":
+                effectiveness_score = 0.25
+            elif verification_report.get("status") == "failed":
+                effectiveness_score = 0.3
+            signals.append(
+                LearningSignal(
+                    signal_type="decomposition",
+                    confidence=0.7,
+                    evidence_refs=retrospective.task_refs[:3],
+                    explanation="Multiple completed task refs indicate a decomposition pattern.",
+                    payload={
+                        "task_count": len(retrospective.task_refs),
+                        "scheduled_subtask_count": context_snapshot.get("scheduled_subtask_count", 0),
+                        "effectiveness_score": effectiveness_score,
+                        "merge_status": merge_report.get("status"),
+                    },
+                )
+            )
+
+        if retrospective.used_skill_names:
+            signals.append(
+                LearningSignal(
+                    signal_type="tool_success",
+                    confidence=0.65 if retrospective.outcome == "completed" else 0.35,
+                    evidence_refs=retrospective.evidence_refs[:2],
+                    explanation="Task-scoped skill shortlist was available during this run.",
+                    payload={"skills": retrospective.used_skill_names[:3]},
+                )
+            )
+
+        if retrospective.outcome == "failed":
+            signals.append(
+                LearningSignal(
+                    signal_type="correction",
+                    confidence=0.75,
+                    evidence_refs=retrospective.evidence_refs[:2],
+                    explanation="Failed retrospectives should remain auditable before any promotion.",
+                    payload={"verification_status": retrospective.verification_status},
+                )
+            )
+
+        return signals