WIN-JHFT4D3SIVT\caoxiaozhu
36c93a764f
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
73 lines
3.1 KiB
Python
73 lines
3.1 KiB
Python
from __future__ import annotations
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from app.agents.schemas.learning import LearningSignal, SessionRetrospective
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class RetrospectiveSignalExtractor:
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def extract(self, retrospective: SessionRetrospective) -> list[LearningSignal]:
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signals: list[LearningSignal] = []
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if retrospective.outcome == "completed":
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signals.append(
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LearningSignal(
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signal_type="workflow",
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confidence=0.8,
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evidence_refs=retrospective.evidence_refs[:3],
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explanation="Completed runs can be mined as workflow hints later.",
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payload={
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"task_type": retrospective.task_type,
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"execution_mode": retrospective.execution_mode,
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},
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)
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)
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if len(retrospective.task_refs) > 1:
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context_snapshot = retrospective.context_snapshot or {}
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merge_report = dict(context_snapshot.get("merge_report") or {})
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verification_report = dict(context_snapshot.get("verification_report") or {})
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effectiveness_score = 1.0
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if merge_report.get("status") == "conflicted":
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effectiveness_score = 0.45
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elif merge_report.get("status") == "fallback":
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effectiveness_score = 0.25
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elif verification_report.get("status") == "failed":
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effectiveness_score = 0.3
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signals.append(
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LearningSignal(
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signal_type="decomposition",
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confidence=0.7,
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evidence_refs=retrospective.task_refs[:3],
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explanation="Multiple completed task refs indicate a decomposition pattern.",
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payload={
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"task_count": len(retrospective.task_refs),
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"scheduled_subtask_count": context_snapshot.get("scheduled_subtask_count", 0),
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"effectiveness_score": effectiveness_score,
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"merge_status": merge_report.get("status"),
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},
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)
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)
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if retrospective.used_skill_names:
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signals.append(
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LearningSignal(
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signal_type="tool_success",
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confidence=0.65 if retrospective.outcome == "completed" else 0.35,
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evidence_refs=retrospective.evidence_refs[:2],
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explanation="Task-scoped skill shortlist was available during this run.",
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payload={"skills": retrospective.used_skill_names[:3]},
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)
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)
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if retrospective.outcome == "failed":
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signals.append(
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LearningSignal(
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signal_type="correction",
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confidence=0.75,
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evidence_refs=retrospective.evidence_refs[:2],
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explanation="Failed retrospectives should remain auditable before any promotion.",
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payload={"verification_status": retrospective.verification_status},
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)
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)
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return signals
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