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M1: domain schemas, contracts pipeline, ledger and snapshot services - packages/domain: zod schemas for the 07-scenario object set with a schemaRegistry driving schema export, fixture generation, and Python module naming; decimal-string/sha256/curve primitives enforce the docs/11 §3.3 representation rules at the type layer - contracts/: 12 JSON Schemas (draft 2020-12), 12 golden fixtures, 3 invalid fixtures crafted to fail on both sides (float money, bad digest, missing concurrency version) - skills-py: generated pydantic models (committed, never hand-edited), regeneration script, mirror pytest using JSON-mode validation — dual-side contract tests agree on all 15 fixtures - packages/services: content-addressed immutable snapshot store (memory + fs, rejects floats), LedgerService v1 with optimistic concurrency and the P7 constraint cascade (monthly position bounds day-ahead bids; no-anchor bids rejected), quality-gate stub - cascade band shape is an OPEN-QUESTION A5 placeholder (pro-rata daily share ±band) — see ledger.ts checkCascade All green: tsc typecheck, 41 TS tests, 15 Python tests. 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
2026-09-01 21:50:22 -04:00
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M2: skill contracts, Python skill service, L2 eval harness with baseline - 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
2026-09-02 06:29:08 -04:00
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M1: domain schemas, contracts pipeline, ledger and snapshot services - packages/domain: zod schemas for the 07-scenario object set with a schemaRegistry driving schema export, fixture generation, and Python module naming; decimal-string/sha256/curve primitives enforce the docs/11 §3.3 representation rules at the type layer - contracts/: 12 JSON Schemas (draft 2020-12), 12 golden fixtures, 3 invalid fixtures crafted to fail on both sides (float money, bad digest, missing concurrency version) - skills-py: generated pydantic models (committed, never hand-edited), regeneration script, mirror pytest using JSON-mode validation — dual-side contract tests agree on all 15 fixtures - packages/services: content-addressed immutable snapshot store (memory + fs, rejects floats), LedgerService v1 with optimistic concurrency and the P7 constraint cascade (monthly position bounds day-ahead bids; no-anchor bids rejected), quality-gate stub - cascade band shape is an OPEN-QUESTION A5 placeholder (pro-rata daily share ±band) — see ledger.ts checkCascade All green: tsc typecheck, 41 TS tests, 15 Python tests. 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
2026-09-01 21:50:22 -04:00
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M2: skill contracts, Python skill service, L2 eval harness with baseline - 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
2026-09-02 06:29:08 -04:00
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M1: domain schemas, contracts pipeline, ledger and snapshot services - packages/domain: zod schemas for the 07-scenario object set with a schemaRegistry driving schema export, fixture generation, and Python module naming; decimal-string/sha256/curve primitives enforce the docs/11 §3.3 representation rules at the type layer - contracts/: 12 JSON Schemas (draft 2020-12), 12 golden fixtures, 3 invalid fixtures crafted to fail on both sides (float money, bad digest, missing concurrency version) - skills-py: generated pydantic models (committed, never hand-edited), regeneration script, mirror pytest using JSON-mode validation — dual-side contract tests agree on all 15 fixtures - packages/services: content-addressed immutable snapshot store (memory + fs, rejects floats), LedgerService v1 with optimistic concurrency and the P7 constraint cascade (monthly position bounds day-ahead bids; no-anchor bids rejected), quality-gate stub - cascade band shape is an OPEN-QUESTION A5 placeholder (pro-rata daily share ±band) — see ledger.ts checkCascade All green: tsc typecheck, 41 TS tests, 15 Python tests. 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
2026-09-01 21:50:22 -04:00
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M2: skill contracts, Python skill service, L2 eval harness with baseline - 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
2026-09-02 06:29:08 -04:00
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M1: domain schemas, contracts pipeline, ledger and snapshot services - packages/domain: zod schemas for the 07-scenario object set with a schemaRegistry driving schema export, fixture generation, and Python module naming; decimal-string/sha256/curve primitives enforce the docs/11 §3.3 representation rules at the type layer - contracts/: 12 JSON Schemas (draft 2020-12), 12 golden fixtures, 3 invalid fixtures crafted to fail on both sides (float money, bad digest, missing concurrency version) - skills-py: generated pydantic models (committed, never hand-edited), regeneration script, mirror pytest using JSON-mode validation — dual-side contract tests agree on all 15 fixtures - packages/services: content-addressed immutable snapshot store (memory + fs, rejects floats), LedgerService v1 with optimistic concurrency and the P7 constraint cascade (monthly position bounds day-ahead bids; no-anchor bids rejected), quality-gate stub - cascade band shape is an OPEN-QUESTION A5 placeholder (pro-rata daily share ±band) — see ledger.ts checkCascade All green: tsc typecheck, 41 TS tests, 15 Python tests. 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
2026-09-01 21:50:22 -04:00
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M2: skill contracts, Python skill service, L2 eval harness with baseline - 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
2026-09-02 06:29:08 -04:00
"packages/evals": {
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"@vpp/domain": "*",
"@vpp/services": "*"
},
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"@types/node": "^26.4.1",
"tsx": "^4.19.0",
"typescript": "^5.6.0",
"vitest": "^3.0.0"
}
},
M1: domain schemas, contracts pipeline, ledger and snapshot services - packages/domain: zod schemas for the 07-scenario object set with a schemaRegistry driving schema export, fixture generation, and Python module naming; decimal-string/sha256/curve primitives enforce the docs/11 §3.3 representation rules at the type layer - contracts/: 12 JSON Schemas (draft 2020-12), 12 golden fixtures, 3 invalid fixtures crafted to fail on both sides (float money, bad digest, missing concurrency version) - skills-py: generated pydantic models (committed, never hand-edited), regeneration script, mirror pytest using JSON-mode validation — dual-side contract tests agree on all 15 fixtures - packages/services: content-addressed immutable snapshot store (memory + fs, rejects floats), LedgerService v1 with optimistic concurrency and the P7 constraint cascade (monthly position bounds day-ahead bids; no-anchor bids rejected), quality-gate stub - cascade band shape is an OPEN-QUESTION A5 placeholder (pro-rata daily share ±band) — see ledger.ts checkCascade All green: tsc typecheck, 41 TS tests, 15 Python tests. 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
2026-09-01 21:50:22 -04:00
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"typescript": "^5.6.0",
"vitest": "^3.0.0"
}
}
}
}
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