feat(skill-learning): preserve base skill during synthesis

app-instance/backend/beaver/skills/learning/service.py

@@ -205,7 +205,13 @@ class SkillLearningService:
             )
         if candidate.kind == "merge_skills":
             target_name = self._suggest_skill_name(candidate, packet)
-            payload = await self.synthesizer.synthesize_merge(candidate, packet, provider, model)
+            payload = await self.synthesizer.synthesize_merge(
+                candidate,
+                packet,
+                provider,
+                model,
+                base_skill=self._merged_base_skill_snapshot(candidate.related_skill_names),
+            )
             return self.draft_service.create_merge_draft(
                 skill_name=target_name,
                 base_version=None,
@@ -217,7 +223,13 @@ class SkillLearningService:
             )
         target_skill = candidate.related_skill_names[0]
         base_version = candidate.evidence.get("skill_version")
-        payload = await self.synthesizer.synthesize_revision(candidate, packet, provider, model)
+        payload = await self.synthesizer.synthesize_revision(
+            candidate,
+            packet,
+            provider,
+            model,
+            base_skill=self._base_skill_snapshot(target_skill, base_version),
+        )
         return self.draft_service.create_revision_draft(
             skill_name=target_skill,
             base_version=base_version,
@@ -228,6 +240,46 @@ class SkillLearningService:
             evidence_refs=[{"run_id": item} for item in candidate.source_run_ids],
         )
 
+    def _base_skill_snapshot(self, skill_name: str, version: str | None) -> dict[str, Any] | None:
+        loaded = self.draft_service.store.read_published_skill(skill_name, version)
+        if loaded is None:
+            return None
+        return {
+            "skill_name": loaded.version.skill_name,
+            "version": loaded.version.version,
+            "frontmatter": dict(loaded.version.frontmatter),
+            "content": loaded.content,
+            "summary": loaded.version.summary,
+            "tool_hints": list(loaded.version.tool_hints),
+        }
+
+    def _merged_base_skill_snapshot(self, skill_names: list[str]) -> dict[str, Any] | None:
+        snapshots = [
+            snapshot
+            for name in skill_names
+            if (snapshot := self._base_skill_snapshot(name, None)) is not None
+        ]
+        if not snapshots:
+            return None
+        return {
+            "skill_name": "merge:" + ",".join(str(item["skill_name"]) for item in snapshots),
+            "version": "mixed",
+            "frontmatter": {"merged_skills": [item["frontmatter"] for item in snapshots]},
+            "content": "\n\n".join(
+                f"<!-- base skill: {item['skill_name']} {item['version']} -->\n{item['content']}"
+                for item in snapshots
+            ),
+            "summary": "\n".join(str(item["summary"]) for item in snapshots if item.get("summary")),
+            "tool_hints": list(
+                dict.fromkeys(
+                    tool
+                    for item in snapshots
+                    for tool in item.get("tool_hints", [])
+                    if str(tool).strip()
+                )
+            ),
+        }
+
     def rescore_skill_versions(self) -> list[SkillPerformanceSnapshot]:
         snapshots: list[SkillPerformanceSnapshot] = []
         grouped: dict[tuple[str, str], list[SkillEffectRecord]] = {}