WIN-JHFT4D3SIVT\caoxiaozhu
72a60c698a
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
from __future__ import annotations
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from datetime import UTC, datetime, timedelta
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from app.agents.schemas.learning import SessionRetrospective, SkillCandidate
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from app.agents.skills.models import SkillLifecycleDecision
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from app.services.skill_service import SkillService
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class SkillPromotionEvaluator:
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def __init__(self, db):
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self.db = db
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self.skill_service = SkillService(db)
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async def sync_retrospective(
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self,
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*,
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user_id: str,
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retrospective: SessionRetrospective,
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) -> list[SkillLifecycleDecision]:
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decisions: list[SkillLifecycleDecision] = []
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for candidate in retrospective.skill_candidates:
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decisions.append(
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await self.skill_service.upsert_learned_candidate(
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user_id=user_id,
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candidate=candidate,
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primary_agent=retrospective.primary_agent,
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evidence_refs=candidate.evidence_refs,
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)
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)
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outcome_score = self._derive_outcome_score(retrospective)
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for skill_name in retrospective.used_skill_names:
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decision = await self.skill_service.record_activation_feedback(
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user_id=user_id,
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skill_name=skill_name,
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outcome_score=outcome_score,
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evidence_refs=retrospective.evidence_refs,
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)
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if decision is not None:
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decisions.append(decision)
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return decisions
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@staticmethod
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def _derive_outcome_score(retrospective: SessionRetrospective) -> float:
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if retrospective.verification_status == "passed":
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return 0.9
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if retrospective.verification_status == "skipped":
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return 0.55
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if retrospective.verification_status == "failed":
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return 0.15
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return 0.7 if retrospective.outcome == "completed" else 0.2
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def next_review_after(days: int = 7) -> datetime:
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return datetime.now(UTC) + timedelta(days=days)
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