feat(services): enhance services with rollback and observability

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

backend/app/services/agent_service.py

@@ -7,12 +7,13 @@ import json
 import uuid
 import logging
 from datetime import UTC, datetime
+from time import perf_counter
 from typing import Any, AsyncGenerator
 import asyncio
 from openai import BadRequestError
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
-from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 
 from app.database import async_session
 from app.logging_utils import summarize_llm_config
@@ -21,10 +22,24 @@ from app.models.conversation import Conversation, Message
 from app.models.user import User
 from app.agents.graph import get_agent_graph
 from app.agents.context import set_current_user, clear_current_user
+from app.agents.learning.jobs import schedule_retrospective_job
+from app.agents.learning.retrospector import build_session_retrospective
+from app.agents.learning.session_search import SessionRetrospectiveSearch, summarize_retrospective
+from app.agents.orchestration.task_graph import build_bounded_task_graph
+from app.agents.learning.store import append_retrospective_attachment
+from app.agents.schemas.orchestration import (
+    RuntimeRequestContext,
+    assess_parallel_worthiness,
+    render_runtime_request_context_summary,
+)
+from app.agents.schemas.skills import SkillActivationRecord
 from app.agents.skills.registry import get_skill_registry
+from app.agents.skills.retriever import shortlist_skills_for_request
 from app.services import memory_service
 from app.services.brain_service import BrainService
 from app.services.llm_service import create_llm_from_config, resolve_provider_capabilities
+from app.services.rollback_controller import RollbackController
+from app.services.runtime_observability import build_runtime_observability_report
 from app.agents.tools.time_reasoning import extract_reference_datetime
 from app.agents.state import initial_state
 
@@ -36,6 +51,7 @@ MEMORY_SECTION_HEADERS = (
     "【之前对话摘要】",
     "【知识大脑】",
 )
+MEMORY_INLINE_HEADERS = {"[关于你的记忆]"}
 
 
 def _split_memory_context_sections(memory_context: str | None) -> dict[str, str]:
@@ -81,6 +97,41 @@ def _derive_role_memory_contexts(memory_context: str | None) -> dict[str, str |
     }
 
 
+def _extract_memory_highlights(memory_context: str | None, *, limit: int = 5) -> list[str]:
+    text = (memory_context or "").strip()
+    if not text:
+        return []
+
+    highlights: list[str] = []
+    for raw_line in text.splitlines():
+        line = raw_line.strip()
+        if not line or line in MEMORY_SECTION_HEADERS or line in MEMORY_INLINE_HEADERS:
+            continue
+        if line.startswith("-"):
+            normalized = line[1:].strip()
+        else:
+            normalized = line
+        if normalized:
+            highlights.append(normalized)
+        if len(highlights) >= limit:
+            break
+    return highlights
+
+
+def _summarize_retrospective(retrospective: Any) -> str:
+    summary = str(getattr(retrospective, "summary", "") or "").strip()
+    task_type = str(getattr(retrospective, "task_type", "") or "").strip()
+    execution_mode = str(getattr(retrospective, "execution_mode", "") or "").strip()
+    outcome = str(getattr(retrospective, "outcome", "") or "").strip()
+
+    parts = [summary[:80] or task_type or "历史复盘"]
+    if execution_mode:
+        parts.append(f"mode={execution_mode}")
+    if outcome:
+        parts.append(f"outcome={outcome}")
+    return "；".join(parts)
+
+
 def _is_streaming_rejection_error(error: Exception, user_llm_config: dict | None) -> bool:
     capabilities = resolve_provider_capabilities(user_llm_config)
     error_text = str(error).lower()
@@ -461,18 +512,27 @@ class AgentService:
     async def _build_agent_state(
         self,
         *,
+        request_id: str,
         user_id: str,
         conversation: Conversation,
+        raw_user_query: str,
         full_message: str,
         memory_context: str | None,
         current_datetime_context: str,
         current_datetime_reference: dict[str, str],
         user_llm_config: dict | None,
+        runtime_request_context: RuntimeRequestContext,
+        recalled_retrospectives: list[dict[str, Any]],
+        skill_shortlist: list[dict[str, Any]],
     ) -> dict[str, Any]:
         state = initial_state(user_id, conversation.id)
+        runtime_summary = render_runtime_request_context_summary(runtime_request_context)
         state.update(
             {
-                "messages": [HumanMessage(content=full_message)],
+                "messages": [
+                    SystemMessage(content=runtime_summary),
+                    HumanMessage(content=full_message),
+                ],
                 "memory_context": memory_context,
                 "current_datetime_context": current_datetime_context,
                 "current_datetime_reference": current_datetime_reference,
@@ -482,9 +542,119 @@ class AgentService:
         previous_snapshot = await self._load_continuity_snapshot(conversation)
         if previous_snapshot:
             state.update(previous_snapshot)
-            state["messages"] = [HumanMessage(content=full_message)]
+            state["messages"] = [
+                SystemMessage(content=runtime_summary),
+                HumanMessage(content=full_message),
+            ]
+        state.update(
+            {
+                "runtime_request_context": runtime_request_context.model_dump(mode="json"),
+                "task_graph": (
+                    runtime_request_context.task_graph.model_dump(mode="json")
+                    if runtime_request_context.task_graph is not None
+                    else None
+                ),
+                "feature_flags": RollbackController().snapshot_flags(),
+                "recalled_retrospectives": recalled_retrospectives,
+                "retrospective_shortlist": recalled_retrospectives,
+                "skill_shortlist": skill_shortlist,
+                "skill_activation_records": [
+                    SkillActivationRecord(
+                        skill_name=item.get("skill_name"),
+                        source=item.get("source", "runtime"),
+                        source_id=item.get("source_id"),
+                        status=item.get("status", "active"),
+                        injection_mode=item.get("injection_mode", "metadata_only"),
+                        matched_terms=item.get("matched_terms", []),
+                        rationale=item.get("rationale"),
+                    ).model_dump(mode="json")
+                    for item in skill_shortlist
+                    if item.get("skill_name")
+                ],
+                "parallel_worthiness": runtime_request_context.parallel_worthiness.model_dump(
+                    mode="json"
+                ),
+            }
+        )
         return state
 
+    async def _build_runtime_request_context(
+        self,
+        *,
+        request_id: str,
+        user_id: str,
+        conversation: Conversation,
+        user_query: str,
+        memory_context: str | None,
+    ) -> tuple[RuntimeRequestContext, list[dict[str, Any]], list[dict[str, Any]]]:
+        started_at = perf_counter()
+        retrospectives_started = perf_counter()
+        recent_retrospectives = await SessionRetrospectiveSearch(self.db).shortlist(
+            user_id=user_id,
+            query_text=user_query,
+            conversation_id=conversation.id,
+            limit=3,
+        )
+        retrospective_ms = (perf_counter() - retrospectives_started) * 1000
+        feature_flags = RollbackController().snapshot_flags()
+        shortlist_started = perf_counter()
+        skill_shortlist = await shortlist_skills_for_request(
+            self.db,
+            user_id=user_id,
+            user_query=user_query,
+            memory_context=memory_context,
+            retrospectives=[item.model_dump(mode="json") for item in recent_retrospectives],
+            include_learned=feature_flags["ENABLE_LEARNED_SKILL_LOADING"],
+            limit=4,
+        )
+        skill_shortlist_ms = (perf_counter() - shortlist_started) * 1000
+        parallel_worthiness = assess_parallel_worthiness(
+            user_query,
+            retrospective_count=len(recent_retrospectives),
+            skill_count=len(skill_shortlist),
+        )
+        recommended_runtime_mode = (
+            "collaboration" if parallel_worthiness.preferred_mode != "direct" else "direct"
+        )
+        task_graph = (
+            build_bounded_task_graph(
+                query_text=user_query,
+                parallel_worthiness=parallel_worthiness,
+            )
+            if feature_flags["ENABLE_PARALLEL_TASK_GRAPH"]
+            else None
+        )
+        runtime_request_context = RuntimeRequestContext(
+            request_id=request_id,
+            session_id=conversation.id,
+            conversation_id=conversation.id,
+            user_id=user_id,
+            query_text=user_query,
+            raw_user_query=user_query,
+            recalled_memories=_extract_memory_highlights(memory_context),
+            recalled_retrospectives=[
+                summarize_retrospective(retrospective) for retrospective in recent_retrospectives
+            ],
+            shortlisted_skills=[entry.skill_name for entry in skill_shortlist],
+            skill_shortlist=skill_shortlist,
+            current_agent_role="master",
+            execution_mode=recommended_runtime_mode,
+            conversation_state_ref=conversation.id,
+            parallel_worthiness=parallel_worthiness,
+            task_graph=task_graph,
+            recommended_runtime_mode=recommended_runtime_mode,
+            assembly_metrics={
+                "retrospective_ms": round(retrospective_ms, 3),
+                "skill_shortlist_ms": round(skill_shortlist_ms, 3),
+                "total_ms": round((perf_counter() - started_at) * 1000, 3),
+            },
+        )
+        return (
+            runtime_request_context,
+            [item.model_dump(mode="json") for item in recent_retrospectives],
+            [item.model_dump(mode="json") for item in skill_shortlist],
+        )
+
     async def chat(
         self,
         user_id: str,
@@ -610,21 +780,38 @@ class AgentService:
         async def run_agent():
             collected = ""
             state: dict[str, Any] | None = None
+            runtime_request_context: RuntimeRequestContext | None = None
             set_current_user(user_id)
             try:
                 graph = get_agent_graph()
                 current_datetime_context, current_datetime_reference = (
                     self._build_current_datetime_context()
                 )
-
+                (
-                state = await self._build_agent_state(
+                    runtime_request_context,
+                    recalled_retrospectives,
+                    skill_shortlist,
+                ) = await self._build_runtime_request_context(
+                    request_id=assistant_msg.id,
                     user_id=user_id,
                     conversation=conv,
+                    user_query=message,
+                    memory_context=memory_ctx,
+                )
+
+                state = await self._build_agent_state(
+                    request_id=assistant_msg.id,
+                    user_id=user_id,
+                    conversation=conv,
+                    raw_user_query=message,
                     full_message=full_message,
                     memory_context=memory_ctx,
                     current_datetime_context=current_datetime_context,
                     current_datetime_reference=current_datetime_reference,
                     user_llm_config=user_llm_config,
+                    runtime_request_context=runtime_request_context,
+                    recalled_retrospectives=recalled_retrospectives,
+                    skill_shortlist=skill_shortlist,
                 )
                 state.update(_derive_role_memory_contexts(memory_ctx))
 
@@ -749,7 +936,7 @@ class AgentService:
                     if collected:
                         assistant_msg.content = collected
                         continuity_snapshot = _build_continuity_snapshot(state or {})
-                        assistant_msg.attachments = (
+                        attachments = (
                             [
                                 {
                                     "kind": "agent_continuity_state",
@@ -757,8 +944,26 @@ class AgentService:
                                 }
                             ]
                             if continuity_snapshot
-                            else None
+                            else []
                         )
+                        if state is not None and runtime_request_context is not None:
+                            retrospective = build_session_retrospective(
+                                request_id=assistant_msg.id,
+                                session_id=conversation_id,
+                                user_query=message,
+                                state=state,
+                                runtime_context=runtime_request_context,
+                            )
+                            attachments = append_retrospective_attachment(attachments, retrospective)
+                            attachments.append(
+                                {
+                                    "kind": "runtime_observability",
+                                    "payload": build_runtime_observability_report(
+                                        state=state,
+                                        feature_flags=state.get("feature_flags") or {},
+                                    ),
+                                }
+                            )
                         conv.agent_state = (
                             {
                                 "kind": "agent_continuity_state",
@@ -771,8 +976,18 @@ class AgentService:
                             user_id,
                             **_build_assistant_event_payload(collected),
                         )
+                        assistant_msg.attachments = attachments or None
                         await self.db.commit()
                         await self.db.refresh(assistant_msg)
+                        schedule_retrospective_job(
+                            user_id=user_id,
+                            conversation_id=conversation_id,
+                            request_message_id=user_msg.id,
+                            response_message_id=assistant_msg.id,
+                            query_text=message,
+                            final_response=collected,
+                            state=state,
+                        )
                 except Exception:
                     logger.exception("save_assistant_message_failed")
                 asyncio.create_task(self._try_auto_summarize_background(user_id, conversation_id))
@@ -863,14 +1078,30 @@ class AgentService:
             current_datetime_context, current_datetime_reference = (
                 self._build_current_datetime_context()
             )
-            state = await self._build_agent_state(
+            (
+                runtime_request_context,
+                recalled_retrospectives,
+                skill_shortlist,
+            ) = await self._build_runtime_request_context(
+                request_id=assistant_msg.id,
                 user_id=user_id,
                 conversation=conv,
+                user_query=message,
+                memory_context=memory_ctx,
+            )
+            state = await self._build_agent_state(
+                request_id=assistant_msg.id,
+                user_id=user_id,
+                conversation=conv,
+                raw_user_query=message,
                 full_message=message,
                 memory_context=memory_ctx,
                 current_datetime_context=current_datetime_context,
                 current_datetime_reference=current_datetime_reference,
                 user_llm_config=user_llm_config,
+                runtime_request_context=runtime_request_context,
+                recalled_retrospectives=recalled_retrospectives,
+                skill_shortlist=skill_shortlist,
             )
             state.update(_derive_role_memory_contexts(memory_ctx))
             result_state = await graph.ainvoke(state)
@@ -900,7 +1131,7 @@ class AgentService:
         continuity_snapshot = (
             _build_continuity_snapshot(result_state) if "result_state" in locals() else None
         )
-        assistant_msg.attachments = (
+        attachments = (
             [
                 {
                     "kind": "agent_continuity_state",
@@ -908,8 +1139,26 @@ class AgentService:
                 }
             ]
             if continuity_snapshot
-            else None
+            else []
         )
+        if "result_state" in locals() and "runtime_request_context" in locals():
+            retrospective = build_session_retrospective(
+                request_id=assistant_msg.id,
+                session_id=conversation_id,
+                user_query=message,
+                state=result_state,
+                runtime_context=runtime_request_context,
+            )
+            attachments = append_retrospective_attachment(attachments, retrospective)
+            attachments.append(
+                {
+                    "kind": "runtime_observability",
+                    "payload": build_runtime_observability_report(
+                        state=result_state,
+                        feature_flags=result_state.get("feature_flags") or {},
+                    ),
+                }
+            )
         conv.agent_state = (
             {
                 "kind": "agent_continuity_state",
@@ -918,7 +1167,17 @@ class AgentService:
             if continuity_snapshot
             else None
         )
+        assistant_msg.attachments = attachments or None
         await self.db.commit()
         await self.db.refresh(assistant_msg)
+        schedule_retrospective_job(
+            user_id=user_id,
+            conversation_id=conversation_id,
+            request_message_id=user_msg.id,
+            response_message_id=assistant_msg.id,
+            query_text=message,
+            final_response=response_content,
+            state=result_state if "result_state" in locals() else None,
+        )
 
         return conversation_id, assistant_msg.id, response_content, model_name_used

backend/app/services/rollback_controller.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from app.config import settings
+
+
+FEATURE_FLAG_NAMES = (
+    "ENABLE_RETROSPECTIVE",
+    "ENABLE_SESSION_RETROSPECTIVE_SEARCH",
+    "ENABLE_RUNTIME_SKILL_SHORTLIST",
+    "ENABLE_LEARNING_SIGNALS",
+    "ENABLE_SKILL_PROMOTION",
+    "ENABLE_LEARNED_SKILL_LOADING",
+    "ENABLE_PARALLEL_TASK_GRAPH",
+)
+
+
+class RollbackController:
+    def snapshot_flags(self) -> dict[str, bool]:
+        return {
+            flag_name: bool(getattr(settings, flag_name, False))
+            for flag_name in FEATURE_FLAG_NAMES
+        }
+
+    def is_enabled(self, flag_name: str) -> bool:
+        return bool(getattr(settings, flag_name, False))

backend/app/services/runtime_observability.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing import Any
+
+from app.agents.orchestration.monitor import build_parallel_runtime_metrics
+
+
+def build_runtime_observability_report(
+    *,
+    state: dict[str, Any],
+    feature_flags: dict[str, bool] | None = None,
+) -> dict[str, Any]:
+    task_graph = state.get("task_graph") if isinstance(state.get("task_graph"), dict) else None
+    scheduled_subtasks = (
+        state.get("scheduled_subtasks") if isinstance(state.get("scheduled_subtasks"), list) else []
+    )
+    task_results = state.get("task_results") if isinstance(state.get("task_results"), list) else []
+    merge_report = state.get("merge_report") if isinstance(state.get("merge_report"), dict) else None
+
+    return {
+        "execution_mode": state.get("execution_mode"),
+        "verification_status": state.get("verification_status"),
+        "skill_shortlist_count": len(state.get("skill_shortlist") or []),
+        "retrospective_shortlist_count": len(state.get("retrospective_shortlist") or []),
+        "feature_flags": feature_flags or {},
+        "parallel_metrics": build_parallel_runtime_metrics(
+            task_graph=task_graph,
+            scheduled_subtasks=scheduled_subtasks,
+            task_results=task_results,
+            merge_report=merge_report,
+        ),
+    }

backend/app/services/skill_service.py

@@ -3,9 +3,13 @@ Skill Service - 技能管理服务层
 负责技能的创建、查询、更新、删除等操作
 """
 
+import hashlib
+from datetime import UTC, datetime, timedelta
 from typing import Optional
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, and_, or_
+from app.agents.schemas.learning import SkillCandidate
+from app.agents.skills.models import SkillLifecycleDecision
 from app.models.skill import Skill
 from app.models.user import User
 
@@ -28,6 +32,10 @@ class SkillService:
             visibility=data.get("visibility", "private"),
             team_id=data.get("team_id"),
             is_active=data.get("is_active", True),
+            status=data.get("status", "active"),
+            scope=data.get("scope", []),
+            effectiveness=data.get("effectiveness", 0.0),
+            review_after=data.get("review_after"),
         )
         self.db.add(skill)
         await self.db.commit()
@@ -41,6 +49,17 @@ class SkillService:
         )
         return result.scalar_one_or_none()
 
+    async def get_by_name_for_user(self, user_id: str, name: str) -> Optional[Skill]:
+        access_scope = or_(
+            Skill.owner_id == user_id,
+            Skill.visibility == "market",
+            Skill.team_id == user_id,
+        )
+        result = await self.db.execute(
+            select(Skill).where(and_(Skill.name == name, access_scope))
+        )
+        return result.scalar_one_or_none()
+
     async def list_for_user(
         self,
         user_id: str,
@@ -56,7 +75,7 @@ class SkillService:
             Skill.team_id == user_id,
         )
 
-        filters = [access_scope, Skill.is_active == True]
+        filters = [access_scope, Skill.is_active == True, Skill.status != "retired"]
 
         if agent_type:
             filters.append(Skill.agent_type == agent_type)
@@ -83,7 +102,7 @@ class SkillService:
         update_fields = [
             "name", "description", "instructions", "agent_type",
             "tools", "required_context", "output_format", "visibility",
-            "team_id", "is_active"
+            "team_id", "is_active", "status", "scope", "effectiveness", "review_after"
         ]
 
         for field in update_fields:
@@ -117,6 +136,7 @@ class SkillService:
                 and_(
                     Skill.agent_type == agent_type,
                     Skill.is_active == True,
+                    Skill.status == "active",
                     or_(
                         Skill.visibility == "market",
                         Skill.visibility == "private"
@@ -125,3 +145,234 @@ class SkillService:
             )
         )
         return list(result.scalars().all())
+
+    async def list_runtime_candidates(
+        self,
+        user_id: str,
+        *,
+        agent_type: Optional[str] = None,
+        include_shadow: bool = True,
+        include_learned: bool = True,
+    ) -> list[Skill]:
+        allowed_statuses = ["active", "shadow"] if include_shadow else ["active"]
+        access_scope = or_(
+            Skill.owner_id == user_id,
+            Skill.visibility == "market",
+            Skill.team_id == user_id,
+        )
+
+        filters = [
+            access_scope,
+            Skill.is_active == True,
+            Skill.status.in_(allowed_statuses),
+        ]
+        if not include_learned:
+            filters.append(Skill.is_builtin == True)
+        if agent_type:
+            filters.append(Skill.agent_type == agent_type)
+
+        result = await self.db.execute(select(Skill).where(and_(*filters)))
+        return list(result.scalars().all())
+
+    async def upsert_learned_candidate(
+        self,
+        *,
+        user_id: str,
+        candidate: SkillCandidate,
+        primary_agent: str | None,
+        evidence_refs: list[dict] | None = None,
+    ) -> SkillLifecycleDecision:
+        source_hash = self._build_candidate_source_hash(candidate)
+        skill = await self.get_by_name_for_user(user_id, candidate.name)
+        if skill is None:
+            review_after = datetime.now(UTC) + timedelta(days=7)
+            skill = Skill(
+                owner_id=user_id,
+                name=candidate.name,
+                description=candidate.summary,
+                instructions=candidate.summary,
+                agent_type=primary_agent or "master",
+                tools=[],
+                required_context=[],
+                output_format=None,
+                visibility="private",
+                is_active=True,
+                status="candidate",
+                scope=[primary_agent or "master", "learned", candidate.candidate_type],
+                effectiveness=candidate.confidence,
+                review_after=review_after,
+                candidate_count=1,
+                candidate_source_hashes=[source_hash],
+            )
+            self.db.add(skill)
+            await self.db.commit()
+            await self.db.refresh(skill)
+            return SkillLifecycleDecision(
+                skill_name=skill.name,
+                action="created_candidate",
+                previous_status=None,
+                new_status="candidate",
+                reason="First learned candidate created from retrospective evidence.",
+                evidence_refs=evidence_refs or [],
+                confidence=candidate.confidence,
+                review_after=review_after,
+            )
+
+        previous_status = skill.status
+        known_hashes = list(skill.candidate_source_hashes or [])
+        is_duplicate_candidate = source_hash in known_hashes
+        if not is_duplicate_candidate:
+            skill.candidate_count = int(skill.candidate_count or 0) + 1
+            known_hashes.append(source_hash)
+        skill.candidate_source_hashes = known_hashes
+        current_effectiveness = float(skill.effectiveness or 0.0)
+        skill.effectiveness = round(max(current_effectiveness, float(candidate.confidence or 0.0)), 3)
+        skill.review_after = datetime.now(UTC) + timedelta(days=7)
+        if primary_agent and primary_agent not in (skill.scope or []):
+            skill.scope = [*(skill.scope or []), primary_agent]
+
+        action = "no_change"
+        reason = "Candidate evidence refreshed."
+        if is_duplicate_candidate:
+            reason = "Duplicate candidate evidence ignored for promotion counting."
+        if (
+            not is_duplicate_candidate
+            and skill.status == "candidate"
+            and skill.candidate_count >= 2
+            and skill.effectiveness >= 0.6
+        ):
+            skill.status = "shadow"
+            action = "promoted_to_shadow"
+            reason = "Repeated candidate evidence promoted the learned skill to shadow."
+
+        await self.db.commit()
+        await self.db.refresh(skill)
+        return SkillLifecycleDecision(
+            skill_name=skill.name,
+            action=action,
+            previous_status=previous_status,
+            new_status=skill.status,
+            reason=reason,
+            evidence_refs=evidence_refs or [],
+            confidence=skill.effectiveness,
+            review_after=skill.review_after,
+        )
+
+    async def record_activation_feedback(
+        self,
+        *,
+        user_id: str,
+        skill_name: str,
+        outcome_score: float,
+        evidence_refs: list[dict] | None = None,
+    ) -> SkillLifecycleDecision | None:
+        skill = await self.get_by_name_for_user(user_id, skill_name)
+        if skill is None or skill.status not in {"shadow", "active", "deprecated"}:
+            return None
+
+        previous_status = skill.status
+        previous_activation_count = int(skill.activation_count or 0)
+        skill.activation_count = previous_activation_count + 1
+        skill.last_activated_at = datetime.now(UTC)
+
+        previous_effectiveness = float(skill.effectiveness or 0.0)
+        if previous_activation_count <= 0:
+            skill.effectiveness = round(outcome_score, 3)
+        else:
+            skill.effectiveness = round(
+                ((previous_effectiveness * previous_activation_count) + outcome_score)
+                / skill.activation_count,
+                3,
+            )
+
+        action = "feedback_recorded"
+        reason = "Activation outcome recorded."
+        if skill.status == "shadow" and skill.activation_count >= 2 and skill.effectiveness >= 0.7:
+            skill.status = "active"
+            action = "promoted_to_active"
+            reason = "Shadow skill proved effective enough to become active."
+        elif skill.status == "active" and skill.activation_count >= 3 and skill.effectiveness < 0.35:
+            skill.status = "deprecated"
+            action = "degraded_to_deprecated"
+            reason = "Active skill underperformed repeatedly and was deprecated."
+        elif skill.status == "deprecated" and skill.activation_count >= 4 and skill.effectiveness < 0.2:
+            skill.status = "retired"
+            action = "retired"
+            reason = "Deprecated skill stayed ineffective and was retired."
+        elif skill.status == "deprecated" and skill.effectiveness >= 0.65 and outcome_score >= 0.8:
+            skill.status = "active"
+            action = "reactivated"
+            reason = "Deprecated skill recovered with strong positive feedback."
+
+        skill.review_after = datetime.now(UTC) + timedelta(days=7)
+        await self.db.commit()
+        await self.db.refresh(skill)
+        return SkillLifecycleDecision(
+            skill_name=skill.name,
+            action=action,
+            previous_status=previous_status,
+            new_status=skill.status,
+            reason=reason,
+            evidence_refs=evidence_refs or [],
+            confidence=skill.effectiveness,
+            review_after=skill.review_after,
+        )
+
+    async def run_decay_review(
+        self,
+        *,
+        user_id: str,
+        as_of: datetime | None = None,
+    ) -> list[SkillLifecycleDecision]:
+        review_time = as_of or datetime.now(UTC)
+        result = await self.db.execute(
+            select(Skill).where(
+                and_(
+                    Skill.owner_id == user_id,
+                    Skill.is_active == True,
+                    Skill.status.in_(["shadow", "active", "deprecated"]),
+                    Skill.review_after.is_not(None),
+                    Skill.review_after <= review_time,
+                )
+            )
+        )
+        skills = list(result.scalars().all())
+        decisions: list[SkillLifecycleDecision] = []
+        for skill in skills:
+            previous_status = skill.status
+            action = "no_change"
+            reason = "Review completed without status change."
+
+            if skill.status == "shadow" and float(skill.effectiveness or 0.0) < 0.45:
+                skill.status = "deprecated"
+                action = "degraded_to_deprecated"
+                reason = "Shadow skill review found low effectiveness."
+            elif skill.status == "deprecated" and float(skill.effectiveness or 0.0) < 0.2:
+                skill.status = "retired"
+                action = "retired"
+                reason = "Deprecated skill remained weak through review."
+
+            skill.review_after = review_time + timedelta(days=7)
+            decisions.append(
+                SkillLifecycleDecision(
+                    skill_name=skill.name,
+                    action=action,
+                    previous_status=previous_status,
+                    new_status=skill.status,
+                    reason=reason,
+                    confidence=skill.effectiveness,
+                    review_after=skill.review_after,
+                )
+            )
+
+        await self.db.commit()
+        return decisions
+
+    @staticmethod
+    def _build_candidate_source_hash(candidate: SkillCandidate) -> str:
+        raw = (
+            f"{candidate.name}|{candidate.summary}|"
+            f"{','.join(candidate.source_pattern_ids)}|"
+            f"{len(candidate.evidence_refs)}"
+        ).encode("utf-8")
+        return hashlib.sha1(raw).hexdigest()

backend/app/services/system_service.py

@@ -4,6 +4,9 @@ import os
 import platform
 import socket
 import subprocess
+import time
+
+import httpx
 
 try:
     import psutil
@@ -15,6 +18,10 @@ class SystemService:
     _last_net_bytes_sent: int | None = None
     _last_net_bytes_recv: int | None = None
     _last_net_sample_at: float | None = None
+    _weather_cache: dict | None = None
+    _weather_cached_at: float | None = None
+    _weather_cached_location: str | None = None
+    _weather_cache_ttl_seconds: float = 10 * 60  # 10 minutes
 
     def __init__(self):
         # Import settings here to avoid circular imports
@@ -134,8 +141,95 @@ class SystemService:
             'timestamp': datetime.now(UTC).isoformat(),
         }
 
-    def get_config(self) -> dict:
+    async def _fetch_weather(self, location: str) -> dict:
+        try:
+            timeout = httpx.Timeout(10.0, connect=5.0)
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                response = await client.get(f'https://wttr.in/{location}', params={'format': 'j1'})
+                response.raise_for_status()
+            payload = response.json()
+            current = (payload.get('current_condition') or [{}])[0]
+            weather_code = current.get('weatherCode')
+            temp = current.get('temp_C')
+            parsed_code = int(weather_code) if weather_code is not None and str(weather_code).isdigit() else None
+            if parsed_code is None or temp in (None, ''):
+                return {'weather_code': None, 'weather_summary': 'Weather unavailable'}
+
+            label = self._weather_code_label(parsed_code)
+            return {
+                'weather_code': parsed_code,
+                'weather_summary': f'{label} {temp}°C',
+            }
+        except (httpx.HTTPError, ValueError, TypeError):
+            return {'weather_code': None, 'weather_summary': 'Weather unavailable'}
+
+    @staticmethod
+    def _weather_code_label(code: int | None) -> str:
+        if code == 0:
+            return 'Clear'
+        if code in {1, 2}:
+            return 'Partly Cloudy'
+        if code == 3:
+            return 'Overcast'
+        if code in {45, 48}:
+            return 'Fog'
+        if code in {51, 53, 55, 56, 57}:
+            return 'Drizzle'
+        if code in {61, 63, 65, 66, 67, 80, 81, 82}:
+            return 'Rain'
+        if code in {71, 73, 75, 77, 85, 86}:
+            return 'Snow'
+        if code in {95, 96, 99}:
+            return 'Thunderstorm'
+        return 'Weather'
+
+    async def get_config(self) -> dict:
         """Get public system configuration."""
+        location = self._settings.LOCATION
+        now = time.time()
+        cached_weather = self.__class__._weather_cache
+        cached_at = self.__class__._weather_cached_at
+        cached_location = self.__class__._weather_cached_location
+
+        cache_is_valid = (
+            cached_weather is not None
+            and cached_at is not None
+            and cached_location == location
+            and (now - cached_at) < self.__class__._weather_cache_ttl_seconds
+        )
+
+        if cache_is_valid:
+            return {
+                'location': location,
+                **cached_weather,
+                'weather_cached': True,
+                'weather_cached_at': cached_at,
+            }
+
+        weather = await self._fetch_weather(location)
+
+        # If fetch failed but we have *any* last known weather for same location, return it to avoid UI flicker.
+        if (
+            (weather.get('weather_code') is None)
+            and cached_weather is not None
+            and cached_location == location
+        ):
+            return {
+                'location': location,
+                **cached_weather,
+                'weather_cached': True,
+                'weather_cached_at': cached_at,
+                'weather_stale': True,
+            }
+
+        # Update cache on successful/meaningful payload (or keep "unavailable" if never succeeded).
+        self.__class__._weather_cache = weather
+        self.__class__._weather_cached_at = now
+        self.__class__._weather_cached_location = location
+
         return {
-            'location': self._settings.LOCATION,
+            'location': location,
+            **weather,
+            'weather_cached': False,
+            'weather_cached_at': now,
         }