Diagram generator takes a locale (en|zh) via a label dictionary; render.sh
now emits img/ and img/zh/.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Bg6jx9vHNHzB91qyV64GQ7
Seven hand-authored SVG diagrams (context, runtime, memory, safety chain,
stack, contracts, evaluation) rendered via headless Chrome; generator and
render script included for regeneration and the Chinese edition.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Bg6jx9vHNHzB91qyV64GQ7
- 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
Four-layer eval design (LLM tasks / skills / safety chain / end-to-end
decision quality), datasets built on event-log replay (I7), change
gates mapping each change type to required evals — envelope widening
approvable only on shadow/online L4 data — and measurable definitions
for the proposal's core KPIs.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019u5SLNweVio6ozJX7yfxQr🔮 View transcript: https://logs.lojong.info/s/e8u90k3t33w590r7b5y7yzqh
Consolidates plane-boundary ports, service/event contracts, and the
canonical domain-object table (merged with peer review §12), plus the
TS↔Python strategy: zod as schema source, committed JSON Schema
artifacts, generated pydantic models, golden-fixture contract tests
in both CIs, and cross-language data-representation rules.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019u5SLNweVio6ozJX7yfxQr🔮 View transcript: https://logs.lojong.info/s/e8u90k3t33w590r7b5y7yzqh
