8796faca63
- packages/domain: ForecastRequest, BidOptimizationRequest/Result,
ReportRequest, SkillReport (+ golden and invalid fixtures, exported to
contracts/ and regenerated as pydantic models).
- skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/
price forecasts (same-day-type EWM point forecast, conformal residual
quantiles — coverage test as acceptance gate); bid-optimization MILP on
HiGHS (binary block participation, hard ledger energy bounds, exact
Decimal fit of the rounded curve inside the bounds, revenue distribution
over quantile paths); report generator whose every figure is a
{tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis
property test that bids respect ledger constraints.
- packages/services: LedgerService.dayAheadBounds (the P7 cascade band
handed to the optimizer); Decimal resolved once for CJS/ESM interop.
- packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy,
naive/hindsight revenue baselines), HTTP skill client, rolling-origin
harness that pushes each bid through the real ledger, CLI with
--check/--write-baseline; committed baseline on the SYNTHETIC dataset
(no historical Hubei data yet — baselines measure the harness, not KPI).
- CI: evals job boots the skill service and fails on baseline digest drift.
- docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6
wired as placeholders. README/CLAUDE.md status → M2 done, M3 next.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
54 lines
1.2 KiB
Python
54 lines
1.2 KiB
Python
# generated by datamodel-codegen:
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# filename: skill_report.json
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from __future__ import annotations
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from enum import StrEnum
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from pydantic import AwareDatetime, BaseModel, ConfigDict, constr
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class Kind(StrEnum):
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DAY_AHEAD_BID_SUMMARY = 'DAY_AHEAD_BID_SUMMARY'
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FORECAST_EVAL = 'FORECAST_EVAL'
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BID_BACKTEST = 'BID_BACKTEST'
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class Ref(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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tool_call_id: constr(min_length=1)
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path: constr(min_length=1)
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class Metric(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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name: constr(min_length=1)
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value: constr(pattern=r'^-?\d+(\.\d+)?$')
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unit: constr(min_length=1)
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ref: Ref
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class Section(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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title: constr(min_length=1)
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metrics: list[Metric]
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notes: list[str]
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class SkillReport(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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id: constr(min_length=1)
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kind: Kind
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market_date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
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sections: list[Section]
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skill_version: constr(pattern=r'^\d+\.\d+\.\d+$')
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generated_at: AwareDatetime
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