chore(shared): S4 — remove in-repo shared/ ahead of subtree re-add from commons

The directory returns in the next commit as a git subtree of portfolio-optimiser-commons (source of truth), same path, so tests and the PORTFOLIO_SHARED_ROOT default resolver are unaffected. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01AaQCFnfsh3tfq1VfzdJpoi
2026-07-03 05:35:59 +02:00 · 2026-07-03 05:35:59 +02:00 · 8e2daaee62
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 # Konseptet
 *En beskrivelse i klartekst av hva dette prosjektet er og hvorfor det betyr noe — skrevet
 for en ikke-spesialist, f.eks. en forretningsutvikler i et annet selskap. Framework-nøytral:
 den beskriver metoden, ikke en bestemt implementasjon.*
 ---
 ## Problemet
 Mange virksomheter driver en **portefølje av uavhengige prosjekter** samtidig —
 byggeprosjekter, IT-tjenester, anlegg, produksjonslinjer. Inne i *hvert enkelt* prosjekt
 ligger det skjulte kostnadsbesparelser: et materiale som kan byttes, en dimensjonering som
 er unødig konservativ, en innkjøpsavtale som kan reforhandles. Å finne dem krever at en
 erfaren fagperson setter seg grundig inn i nettopp dét prosjektet — og den kompetansen er
 dyr og skalerer dårlig over en hel portefølje. Så besparelsene blir liggende igjen.
 Generativ AI kan foreslå ideer, men en forretningsutvikler ser umiddelbart to hindre: du
 kan ikke stole på tall en språkmodell *gjetter*, og modellen kjenner ikke din bransjes
 faktiske regler og erfaring. Konseptet er bygget nettopp for å fjerne disse to hindrene.
 ## Slik virker det
 For hvert prosjekt samles alt som er kjent om det — prosjektdokumenter, fagets
 vurderingsmetodikk, relevant faglitteratur, og de harde rammene (budsjett, hva som ikke kan
 endres, regulatoriske krav) — i en **kuratert kunnskapsbase per prosjekt**. Den er bygget på
 en åpen, leverandørnøytral standard (Googles Open Knowledge Format), så den er portabel og
 ikke innelåst i én leverandør.
 Et lag med AI-agenter leser denne konteksten, foreslår konkrete tiltak og diskuterer dem mot
 hverandre — én foreslår, en annen kritiserer. Men — og dette er kjernen — **agentene får
 aldri avgjøre verdien selv.** Hvert tall sendes til en separat, **deterministisk
 beregningsmotor** (matematisk optimering pluss risikosimulering) som regner ut den faktiske
 besparelsen. AI-en foreslår; matematikken avgjør. Det er tillitsankeret som skiller dette
 fra «spør en chatbot».
 De tiltakene som overlever beregningen, legges fram for en **menneskelig fagekspert** som
 gir en dom: godkjent, forbedre eller forkast.
 ## Det som gjør det verdifullt over tid
 Her ligger differensieringen. Systemet **lærer av ekspertenes dommer.** Det er nesten alltid
 et gap mellom hva en modell *beregner* og hva en erfaren fagperson faktisk *godkjenner* —
 fordi eksperten vet hvordan tiltak oppfører seg i praksis, ikke bare i teorien. Systemet
 fanger det gapet og mater det tilbake, så forslagene blir skarpere på *din* virksomhets
 virkelighet, ikke generiske gjennomsnitt. Jo flere dommer, desto bedre.
 Og det respekterer hvordan eksperter faktisk jobber: noen ganger svarer de der og da, andre
 ganger går det dager eller uker. Eksperten legger rett og slett vurderingen sin i en
 **mappe**, og systemet plukker den opp når den enn kommer. Ingen krav om sanntid, og ingen
 trenger å sitte klar.
 ## Hva det er — og ikke er
 Det er et **rent teknisk rammeverk**, ikke et ferdig compliance-produkt. Virksomheten som
 tar det i bruk eier selv formål, personvern og styring; rammeverket leverer bare de tekniske
 forutsetningene (kjøre lokalt, sporbarhet på hvert forslag, ingen data som lekker ut i det
 stille). Det kan kjøre lokalt eller i skyen, og publiseres åpent så andre kan ta det i bruk.
 ## Hvorfor det bygges to ganger
 Det samme konseptet bygges på **to ulike AI-agent-plattformer** — Microsofts Agent Framework
 og Claudes Agent SDK — på nøyaktig samme eksempel, slik at de kan sammenlignes rettferdig.
 Det gir både en åpen referanse andre kan kopiere, og et ærlig grunnlag for å vurdere hvilken
 plattform som passer best til oppgaven.
 ## Konkret
 Tenk energieffektivisering av en bygningsportefølje. Hvert bygg er et prosjekt med sine egne
 tiltaksmuligheter (ventilasjon, belysning, isolasjon). Beregningsmotoren regner ut
 *modellert* besparelse for en tiltakspakke under et budsjett. Men en energirådgiver vet at
 modellert og faktisk besparelse sjelden er like — atferd, måleusikkerhet og samspill mellom
 tiltak skaper et gap. Ekspertens dommer lærer systemet å lukke det gapet over tid. Det er
 nettopp i et slikt fagfelt — der det finnes ekte ekspertskjønn *utover* ren beregning —
 konseptet kommer til sin rett.
--- a/shared/README.md
+++ b/shared/README.md
@ -1,44 +0,0 @@
 # shared/ — framework-neutral core
 This directory holds the parts of the project that are **independent of any AI agent
 framework** and are meant to be **shared, unchanged, between both reference
 implementations**:
 - **this repository** — the method built on Microsoft Agent Framework (MAF);
 - **a sibling repository** (built later, in sequence) — the same method on the
  **Claude Agents SDK**.
 Sharing one identical core is what makes the two implementations a *fair comparison*:
 both consume the same concept, the same example data, and the same expected outcomes,
 so the only thing that differs is the agent framework itself.
 ## Contents (growing)
 - [`CONCEPT.md`](CONCEPT.md) — the business concept, written for a non-specialist
  (e.g. a business developer at another company).
 - [`examples/bygg-energi-mikro/`](examples/bygg-energi-mikro/) — the first example knowledge
  bundle (OKF / LLM-wiki): one office building, one LED-retrofit measure, with a seed expert
  verdict encoding the realization gap and a golden-suite of expected validator outcomes. A
  small **dev fixture** for exercising the agentic loop; a realistic full-scale example comes later.
 - [`skills/expert-reviewer/`](skills/expert-reviewer/) — the **expert-reviewer persona** as a
  framework-neutral Agent Skill: a `SKILL.md` persona prompt (energy-advisor / M&V role + the
  realization-gap methodology the validator cannot compute) and a canonical
  `references/example-verdict.json`. Both reference implementations instantiate the reviewer from
  this one artifact; `shared/` stays pure data (each stack reads the JSON with its own loader).
 - [`method-spec.md`](method-spec.md) — the **normative method specification**, framework-neutral
  (the prose never names a concrete agent toolkit — enforced by a guard test): the 8-step loop,
  the verdict JSON contract, the inbox/outbox folder contract, the promotion-gate semantics, the
  IR projection + golden suite as the only ground truth, and the budget/provenance requirements.
  The sibling implementation is built from this spec alone, without reverse-engineering the
  reference code.
 ## Rules
 - **Nothing in here may import or depend on a specific agent framework.** If it does,
  it does not belong in `shared/`.
 - **Repo layout (decision R1, 2026-06-26):** the shared core lives here for now. When
  work on the sibling repository begins, it will be extracted into its own repository
  (e.g. `portfolio-optimiser-commons`) via `git subtree split`, and both implementation
  repos will consume it. This defers cross-repo plumbing until it is actually needed.
 See the target picture for the full architecture: `docs/plan/2026-06-26-maalbilde-agentic-loop.md`.
--- a/shared/examples/bygg-energi-mikro/bygg-kontor-nord.md
+++ b/shared/examples/bygg-energi-mikro/bygg-kontor-nord.md
@ -1,40 +0,0 @@
 ---
 type: project
 title: "Kontorbygg Nord"
 description: "Fiktivt næringsbygg (kontor) brukt som mikro-eksempel. Energibaseline og rammer for ett effektiviseringstiltak."
 resource: BYGG-KONTOR-NORD
 tags: [kontorbygg, energibaseline, naeringsbygg]
 timestamp: 2026-06-29
 ---
 # Kontorbygg Nord (BYGG-KONTOR-NORD)
 Fiktivt kontorbygg. Tallene er **illustrative men forankret i typiske norske verdier** —
 ikke et ekte bygg. En produksjons-deployer erstatter denne med en ekte kunnskapsbase.
 ## Energibaseline
 | Størrelse | Verdi | Merknad |
 |---|---|---|
 | Oppvarmet bruksareal (BRA) | ~2 500 m² | [I] illustrativt |
 | Totalt elforbruk | **300 000 kWh/år** | [I]; ~120 kWh/m²/år — typisk norsk kontor |
 | Herav belysning | ~54 000 kWh/år (~18 %) | 200 armaturer × 90 W × 3 000 t/år |
 | Variabel energikostnad | **1,00 NOK/kWh** ekskl. mva | [V-forankret] kraftpris + nettleie-energiledd + elavgift |
 **Energiprisen** (1,00 NOK/kWh) er den marginale variable kostnaden et spart kWh faktisk
 unngår, ekskl. mva (næring trekker fra mva). Sammensetning, forankret i SSB Q1 2026:
 kraftpris tjenesteytende næringer ~0,80–1,12 NOK/kWh + nettleie energiledd ~0,10–0,13 +
 elavgift 0,0713. Den varierer kraftig med prisområde (NO4 ~0,13 vs NO2 ~0,96 i kraftpris
 alene) og sesong — derfor er den **konfigurerbar**, og usikkerheten håndteres i Monte
 Carlo-steget (band 0,70–1,40 NOK/kWh). Se [kilder-realiseringsgap.md](kilder-realiseringsgap.md).
 ## Rammer (constraints)
 - Tiltak vurderes **inne i** dette prosjektet (ikke på tvers av en portefølje).
 - Budsjett og tekniske rammer eies av deployer; her holdes de minimale.
 - Bygget driftes i normal kontortid; belysning styres delvis på timeplan (relevant for
  realiseringsgapet — se [verdict-led-fro.md](verdict-led-fro.md)).
 ## Kandidat-tiltak
 - [tiltak-led-retrofit.md](tiltak-led-retrofit.md) — LED-retrofit av belysning.
--- a/shared/examples/bygg-energi-mikro/golden.json
+++ b/shared/examples/bygg-energi-mikro/golden.json
@ -1,21 +0,0 @@
 {
  "_note": "Golden-suite (forventet utfall) for bygg-energi-mikro. To deler: (1) 'validator' fryser den deterministiske (seeded, _MC_SEED=20260624) outputen av validate_proposal pa validator-input.json — en regresjon som fanger utilsiktede endringer i validator/MC. Den MENINGSFULLE assertionen er validates=true (claimed < P90). (2) 'learning_surface' koder realiseringsgapet validatoren IKKE kan regne — ExpeL-froet fra verdict-led-fro.md. expected_actual = realization_rate * modelled.",
  "validator": {
    "outcome": "ValidatedProposal",
    "validates": true,
    "claimed_saving_nok": 30000,
    "nominal_feasible": 90000.0,
    "p10": 68543.08886748762,
    "p50": 95443.98966314227,
    "p90": 121057.08845805985,
    "_percentile_meaning": "OVRE FEASIBLE GRENSE (30% av samplet energikostnad), IKKE LED-besparelsens fysiske band. Se tiltak-led-retrofit.md, 'Mapping til validatoren'."
  },
  "learning_surface": {
    "modelled_saving_nok": 30000,
    "realization_rate": 0.82,
    "expected_actual_saving_nok": 24600,
    "gap_source": "hours-of-use-overestimation",
    "context_key": "kontorbygg; HOU-kilde=timeplan-stipulert",
    "_meaning": "Det validatoren IKKE kan regne fra parameterne. ExpeL-frøet loopens steg 1 skal hente og anvende. Se verdict-led-fro.md."
  }
 }
--- a/shared/examples/bygg-energi-mikro/index.md
+++ b/shared/examples/bygg-energi-mikro/index.md
@ -1,42 +0,0 @@
 ---
 type: index
 okf_version: 0.1
 title: "Bygg-energi mikro-eksempel — kontorbygg, LED-retrofit"
 description: "Minimal OKF-bundle for ett kontorbygg med ett energieffektiviseringstiltak (LED-belysningsretrofit). Utviklings-fixture for den agentiske loopen: kontekst → hypotese → deterministisk validering → ekspert-dom → læring."
 tags: [energieffektivisering, M&V, IPMVP, mikro-eksempel, fixture]
 timestamp: 2026-06-29
 ---
 # Bygg-energi mikro-eksempel
 En **mikro OKF-bundle** (Open Knowledge Format): ett kontorbygg, ett kandidat-tiltak.
 Den er liten med vilje — formålet er **rask småskala-testing og validering** av den
 agentiske loopen gjennom hele utviklingsløpet. Et **realistisk fullskala-eksempel**
 bygges senere (målbilde §8); dette er forløperen.
 > Dette er en framework-nøytral artefakt (null kode-avhengighet). Den deles uendret
 > mellom MAF- og Claude-SDK-implementasjonene. Se [shared/README.md](../../README.md).
 ## Hvorfor energieffektivisering
 Domenet ble valgt for sin **lærings-overflate**: et reelt, dokumentert gap mellom det
 en deterministisk validator kan *regne* (modellert besparelse fra parametere) og det en
 fagekspert *kjenner* (faktisk realisert besparelse i drift). Det gapet — realiseringsgraden
 — er det lærings-sløyfa (ExpeL) skal lære. Se [verdict-led-fro.md](verdict-led-fro.md).
 ## Innhold (progressiv disclosure)
 - [bygg-kontor-nord.md](bygg-kontor-nord.md) — `type: project` — bygget og energibaseline.
 - [tiltak-led-retrofit.md](tiltak-led-retrofit.md) — `type: hypothesis` — kandidat-tiltaket
  (LED-retrofit) med alle parametere for den modellerte besparelsen.
 - [metode-ipmvp-a.md](metode-ipmvp-a.md) — `type: methodology` — M&V-metoden (IPMVP Option A).
 - [kilder-realiseringsgap.md](kilder-realiseringsgap.md) — `type: reference` — verifisert
  litteratur om realiseringsgrad og dets årsaker.
 - [verdict-led-fro.md](verdict-led-fro.md) — `type: verdict` — frøsatt ekspert-dom som koder
  realiseringsgapet. **ExpeL-frøet** loopens steg 1 henter fra.
 ## Hvordan den kjøres i dag
 `validator-input.json` er IR-projeksjonen den **eksisterende deterministiske validatoren**
 konsumerer uendret (energitiltaket mappet inn i kost-IR-en); `golden.json` fryser det
 forventede utfallet. Se [tiltak-led-retrofit.md](tiltak-led-retrofit.md) §«Mapping til validatoren».
--- a/shared/examples/bygg-energi-mikro/kilder-realiseringsgap.md
+++ b/shared/examples/bygg-energi-mikro/kilder-realiseringsgap.md
@ -1,46 +0,0 @@
 ---
 type: reference
 title: "Realiseringsgrad og energy performance gap — verifisert litteratur"
 description: "Kildebelagte tall for gapet mellom modellert og faktisk realisert besparelse, og dets systematiske årsaker. Grunnlaget for verdict-frøets realiseringsgrad."
 tags: [realization-rate, performance-gap, M&V, evaluering, kilder]
 timestamp: 2026-06-29
 ---
 # Realiseringsgrad (realization rate) — verifisert litteratur
 **Realiseringsgrad (RR)** = faktisk evaluert besparelse (ex-post) ÷ modellert/påstått
 besparelse (ex-ante). RR < 1 betyr at drift leverte mindre enn modellen lovte. Avviket
 kalles *energy performance gap*. Alle tall under er verifisert mot primærkilde [V].
 | Nivå | Funn | Kilde |
 |---|---|---|
 | Program (regulatorisk default) | Default gross RR **0,90** for kWh/kW/therm; ex-ante «generally over-estimated» | CPUC Resolution E-4952 |
 | Program (lys, drift lavere) | Operational adjustment ned til **81,1 %** (metrede driftstimer 15 % lavere); coincidence factor **0,566** vs antatt 1,0 | National Grid SBS 2010 (DNV KEMA) |
 | Program (lys, drift høyere) | Hours-of-Use RR **106,5 %**; coincidence 72,2 % — gapet går **begge veier** | Massachusetts Impact Evaluation 2010 |
 | Parameter (driftstimer) | Metret **3 053 t/år** vs antatt **3 772 t/år** (≈19 % lavere); CV ≈ 0,5 | Efficiency Maine 2021 |
 | Portefølje | Commercial lighting **98 %** vs residential **61 %** vs total **93 %** | LADWP Retrospective FY15/16–19/20 |
 | Bygg (grønne næringsbygg) | Predikert besparelse **1,5–3×** realisert; ~⅓ av LEED-bygg bruker mer energi | "Mind the energy performance gap", ScienceDirect |
 | Måleterskel | Besparelse bør overstige **~10 % av baseline** for å skilles pålitelig fra støy | FEMP/RDH M&V-veiledning |
 ## Systematiske årsaker (hvorfor faktisk < modellert) [V]
 1. **Driftstimer / Hours-of-Use** — dominerende. Timeplan-baserte estimat (det Option A
   stipulerer) treffer sjelden metret brenntid (3 053 vs 3 772).
 2. **Baseline- og værjustering** — over-predikert baseline blåser opp absolutt besparelse.
 3. **Coincidence / diversity factor** — for effekt(kW): andel last under nett-topp ~0,57–0,72, ikke 1,0.
 4. **HVAC interactive effects** — mindre spillvarme → endret kjøle-/varmebehov; «too small to measure», stipuleres.
 5. **In-service rate, drift & persistens** — ikke alt installeres/forblir; styringer overstyres; degradering.
 6. **Måleusikkerhet** — under ~10 %-terskelen drukner signalet i støy.
 7. **Rebound / atferd** — mer lys på, lengre, fordi det «koster mindre».
 ## Kilder (URL)
 - EVO IPMVP Generally Accepted M&V Principles (okt. 2018): https://evo-world.org/images/corporate_documents/IPMVP-Generally-Accepted-Principles_Final_26OCT2018.pdf
 - DOE/NREL Uniform Methods Project, Ch. 2 Commercial & Industrial Lighting (NREL 68558): https://docs.nrel.gov/docs/fy17osti/68558.pdf
 - Massachusetts Impact Evaluation of 2010 Prescriptive Lighting: https://ma-eeac.org/wp-content/uploads/Impact-Evaluation-of-2010-Prescriptive-Lighting-Installations-Final-Report-6-21-13.pdf
 - National Grid SBS 2010 Prescriptive Lighting (DNV KEMA): https://www.nationalgridus.com/media/pdfs/our-company/eereports/2014-ngrid-sbs-impact-eval-final-prot.pdf
 - Efficiency Maine Retail & Distributor Lighting 2021: https://www.efficiencymaine.com/docs/Retail-and-Distributor-Lighting-Final-Impact-Evaluation-Report-2021.pdf
 - LADWP Retrospective Impact Evaluation FY15/16–19/20: https://www.ladwp.com/sites/default/files/2024-01/LADWP%20Retrospective%20Report%20FINAL%20V4.pdf
 - CPUC Resolution E-4952: https://docs.cpuc.ca.gov/publisheddocs/published/g000/m232/k459/232459122.pdf
 - "Mind the energy performance gap" (ScienceDirect): https://www.sciencedirect.com/science/article/abs/pii/S0921344918303860
 - SSB Elektrisitetspriser (kraftpris tjenesteytende næringer, Q1 2026): https://www.ssb.no/energi-og-industri/energi/statistikk/elektrisitetspriser
--- a/shared/examples/bygg-energi-mikro/metode-ipmvp-a.md
+++ b/shared/examples/bygg-energi-mikro/metode-ipmvp-a.md
@ -1,40 +0,0 @@
 ---
 type: methodology
 title: "IPMVP Option A — Retrofit Isolation, Key Parameter Measurement"
 description: "M&V-metoden for å verifisere besparelsen fra ett isolert tiltak ved å måle nøkkelparameteren (effekt) og estimere resten (driftstimer)."
 methodology: IPMVP
 option: A
 tags: [IPMVP, M&V, EVO, retrofit-isolation]
 timestamp: 2026-06-29
 ---
 # M&V-metode: IPMVP Option A
 **IPMVP** (International Performance Measurement and Verification Protocol) er
 konsensus-rammeverket for å måle og verifisere energibesparelser, eid og vedlikeholdt av
 **EVO** (Efficiency Valuation Organization). Kjerneinnsikten som begrunner hele
 lærings-sløyfa står eksplisitt i protokollen [V]:
 > *"Savings cannot be directly measured, because savings represent the absence of energy use."*
 Besparelse er en **kontrafaktisk** størrelse — det finnes ingen måler for «det som ikke ble
 brukt». Den *beregnes*: `Baseline-energi − Rapporterings-energi ± justeringer` (IPMVP Eq. 1).
 ## De fire opsjonene (EVO, offisielle navn) [V]
 - **Option A — Retrofit Isolation: Key Parameter Measurement.** Måler nøkkelparameteren
  (typisk effekt) på det berørte utstyret; øvrige parametere (typisk driftstimer) *estimeres*.
 - **Option B — Retrofit Isolation: All Parameter Measurement.** Måler alle relevante parametere.
 - **Option C — Whole Facility.** Besparelse fra byggets hovedmåler, med rutinejustering (vær/produksjon).
 - **Option D — Calibrated Simulation.** Besparelse via simuleringsmodell kalibrert mot måledata.
 ## Hvorfor Option A for dette tiltaket [V]
 EVOs egen tabell bruker nettopp et **lysarmatur-retrofit** som den kanoniske Option A-saken:
 effekt før/etter måles (billig, presist), mens **driftstimer stipuleres** fra byggets
 timeplan. Det gjør Option A enklest og billigst for ett isolert tiltak.
 **Kritisk for lærings-overflaten:** parameteren Option A tillater å *estimere* — driftstimer
 — er nøyaktig der realiseringsgapet oppstår. Den stipulerte timeplanen treffer sjelden den
 faktiske, metrede brenntiden. Se [verdict-led-fro.md](verdict-led-fro.md) og
 [kilder-realiseringsgap.md](kilder-realiseringsgap.md).
--- a/shared/examples/bygg-energi-mikro/tiltak-led-retrofit.md
+++ b/shared/examples/bygg-energi-mikro/tiltak-led-retrofit.md
@ -1,66 +0,0 @@
 ---
 type: hypothesis
 title: "LED-retrofit av kontorbelysning"
 description: "Bytte 200 lysrørarmaturer (3-rørs T8 troffer, ~90 W) til LED-paneler (~40 W). Kandidat-tiltak med modellert besparelse og usikkerhet."
 resource: BYGG-KONTOR-NORD
 measure_id: LED-RETROFIT-01
 tags: [LED, belysning, retrofit, ECM]
 timestamp: 2026-06-29
 ---
 # Tiltak: LED-retrofit av kontorbelysning
 Bytte av 200 lysrørarmaturer (2×4 fluorescerende troffer) til LED-paneler. Dette er det
 vanligste enkelt-ECM-et (Energy Conservation Measure) og IPMVPs egen kanoniske
 Option A-illustrasjon — se [metode-ipmvp-a.md](metode-ipmvp-a.md).
 ## Parametere
 | Parameter | Verdi | Status | Kilde/forankring |
 |---|---|---|---|
 | Antall armaturer | 200 | [I] | mikro-skala valgt |
 | Effekt før (T8 troffer m/ ballast) | 90 W | [V] | 3×32 W ≈ 90–96 W m/ ballastfaktor |
 | Effekt etter (LED-panel) | 40 W | [V] | kommersielt 2×4 LED-panel ~40 W |
 | Reduksjon per armatur (ΔW) | 50 W | beregnet | 90 − 40 |
 | Driftstimer (HOU) | 3 000 t/år | [I] | forankret i metret 3 053 t (Efficiency Maine) |
 | Variabel energipris | 1,00 NOK/kWh | [V-forankret] | se [bygg-kontor-nord.md](bygg-kontor-nord.md) |
 ## Modellert besparelse (ex-ante)
 Lysligning (DOE/NREL Uniform Methods Project, Eq. 3):
 `kWh = Σ (W_før − W_etter) × antall × HOU / 1000`
 > ΔW = 90 − 40 = **50 W/armatur**
 > kWh/år = 50 × 200 × 3 000 / 1 000 = **30 000 kWh/år**
 > kr/år = 30 000 × 1,00 = **30 000 NOK/år**
 HVAC-interaktiv effekt (effektivt lys → mindre spillvarme → endret kjøle-/varmebehov,
 UMP Eq. 6) er ~+5 % i elektrisk kjølte bygg, men **utelatt fra kjernetallet** her
 (konservativt; den lille interaktive justeringen er en ex-post-vurdering eksperten kan
 legge til). Modellert kjernebesparelse: **30 000 kWh/år ≈ 30 000 NOK/år**.
 ## Usikkerhet (for Monte Carlo P10/P50/P90)
 Den dominerende usikkerheten i en *energibesparelse* ligger i driftstimer (HOU), ikke
 prisen — men den eksisterende validatorens Monte Carlo varierer enhetspris. I denne
 mikro-mappingen brukes derfor **prisbandet 0,70–1,40 NOK/kWh** som usikkerhetsakse
 (region/sesong, jf. [bygg-kontor-nord.md](bygg-kontor-nord.md)). Den fysiske HOU-usikkerheten
 og — viktigere — den *systematiske* HOU-skjevheten håndteres i verdict-laget, ikke her.
 ## Mapping til validatoren (hvorfor `validator-input.json` ser ut som den gjør)
 Den eksisterende deterministiske validatoren er en *feasibility-gate* (`claimed ≤ 30 % av
 affected total`, Monte Carlo over enhetspris) bygd for kostnadskutt. Energitiltaket mappes
 inn **uendret**:
 - `affected_items = [{code: "ENERGI-TOTAL-EL", quantity: 300000 kWh/år, unit_cost: 1.00 NOK/kWh}]`
  → byggets **totale** årlige energikostnad (300 000 NOK). LED-besparelsen er ~10 % av den,
  godt innenfor 30 %-cap-en.
 - `claimed_saving_nok = 30000` → den modellerte LED-besparelsen.
 - `assumptions = {"ENERGI-TOTAL-EL": [0.70, 1.40]}` → prisbandet for Monte Carlo.
 **Ærlig begrensning:** validatorens P10/P50/P90 betyr her «øvre feasible grense» (30 % av
 samplet energikostnad), *ikke* «LED-besparelsens fysiske band». Det er bevisst — den
 domenetro besparelses-modelleringen og realiseringsgapet hører hjemme i verdict-laget
 ([verdict-led-fro.md](verdict-led-fro.md)), som er nettopp det lærings-sløyfa skal lære.
 En energi-bevisst validator (ΔW × antall × HOU) er senere fase-arbeid, ikke dette fixturet.
--- a/shared/examples/bygg-energi-mikro/validator-input.json
+++ b/shared/examples/bygg-energi-mikro/validator-input.json
@ -1,16 +0,0 @@
 {
  "_note": "IR-projeksjon (ir.SavingsProposal) for det eksisterende deterministiske validatoren. Energitiltaket er mappet inn i kost-IR-en UENDRET: affected_items = byggets totale arlige energikostnad; claimed_saving_nok = modellert LED-besparelse (~10% av total, innenfor 30%-cap); assumptions = energipris-band (NOK/kWh) for Monte Carlo. Se tiltak-led-retrofit.md, seksjon 'Mapping til validatoren'.",
  "project_id": "BYGG-KONTOR-NORD",
  "measure": "LED-retrofit av 200 lysrorarmaturer (90 W -> 40 W) i kontorlokaler",
  "affected_items": [
    {
      "code": "ENERGI-TOTAL-EL",
      "quantity": 300000,
      "unit_cost": 1.0
    }
  ],
  "claimed_saving_nok": 30000,
  "assumptions": {
    "ENERGI-TOTAL-EL": [0.70, 1.40]
  }
 }
--- a/shared/examples/bygg-energi-mikro/verdict-led-fro.md
+++ b/shared/examples/bygg-energi-mikro/verdict-led-fro.md
@ -1,56 +0,0 @@
 ---
 type: verdict
 title: "Ekspert-dom (frø): LED-retrofit — godkjent med realiseringskorreksjon"
 description: "Frøsatt ekspert-dom som koder realiseringsgapet for LED-tiltaket. ExpeL-frøet loopens steg 1 henter fra: modellert besparelse korrigeres ned med realiseringsgraden eksperten kjenner fra drift."
 resource: BYGG-KONTOR-NORD
 measure_id: LED-RETROFIT-01
 decision: approved_with_adjustment
 realization_rate: 0.82
 modelled_saving_nok: 30000
 expected_actual_saving_nok: 24600
 gap_source: hours-of-use-overestimation
 context_key: "kontorbygg; HOU-kilde=timeplan-stipulert"
 provenance: "frø — AI-forfattet, forankret i National Grid SBS 2010 (RR 0,81); erstattes av ekte HITL i produksjon"
 tags: [verdict, realization-rate, ExpeL-seed, HITL]
 timestamp: 2026-06-29
 ---
 # Ekspert-dom (frø): LED-retrofit
 > **Dette er et frø**, ikke en ekte dom. I simulering gir en ekspert-persona slike dommer;
 > i produksjon gir et menneske dem via samme mappe-grensesnitt. Frøet er forankret i
 > verifisert litteratur ([kilder-realiseringsgap.md](kilder-realiseringsgap.md)), ikke
 > oppdiktet. Det er **provenance-merket** og promoteres til wikien kun fordi en (simulert)
 > ekspert har godkjent det (målbilde, steg 8 gated).
 ## Dommen
 **Beslutning:** godkjent — med realiseringskorreksjon.
 Den modellerte besparelsen (**30 000 NOK/år**) er teknisk korrekt fra parameterne, og
 validatoren bekrefter at den er innenfor feasibelt område. Men i drift realiseres erfaringsvis
 **~82 %** av en timeplan-stipulert LED-besparelse i kontorbygg → forventet faktisk
 besparelse **≈ 24 600 NOK/år**.
 ## Begrunnelse (det validatoren ikke kan regne)
 Realiseringsgapet her er **ikke** spredning i parameterne — det er en *systematisk skjevhet*
 mellom de stipulerte parameterne og driftsvirkeligheten:
 - **Driftstimer overvurdert (dominerende):** den antatte timeplanen (3 000 t) ligger over
  faktisk metret brenntid — naturlig dagslys, tomme rom, sensorstyring. Jf. National Grid
  (RR-bidrag 0,81 fra 15 % lavere metrede timer) og Efficiency Maine (3 053 vs 3 772 t).
 - **In-service rate < 1:** ikke alle 200 armaturer er nødvendigvis montert/i drift ved
  evaluering.
 - **Atferd/persistens:** rebound (mer lys på fordi det «er gratis») og overstyrte styringer.
 Du kan **ikke** regne deg til RR = 0,82 fra `{200, 90 W, 40 W, 3 000 t, 1,00 NOK/kWh}`.
 Skjevheten er epistemikk parameterne ikke bærer — den finnes bare i akkumulert drifts-erfaring.
 ## Lærings-signalet (ExpeL)
 Korreksjonen er **kontekstbetinget**: `context_key = "kontorbygg; HOU-kilde=timeplan-stipulert"`.
 Neste kjøring, gitt en lignende hypotese i samme kontekst, skal hente denne dommen og
 justere den modellerte ex-ante-besparelsen mot forventet ex-post (≈ 0,82×) — uten å vente
 på 12 måneders måling. **Det er denne dataflyten — verdict inn i neste hypotese — loopens
 steg 1 må wire (målbilde §5, §7). Dette frøet er testankeret.**
--- a/shared/method-spec.md
+++ b/shared/method-spec.md
@ -1,441 +0,0 @@
 # Method specification — portfolio cost-saving loop (framework-neutral)
 > **Status:** normative. This document specifies the method both reference implementations
 > build: an agentic loop that finds cost savings *inside* each project of a portfolio, with a
 > mandatory deterministic validator, expert judgement in the loop, and learning from the
 > verdicts. It is written so the method can be implemented **from this spec alone** — without
 > reverse-engineering any existing implementation. The prose is framework-neutral by rule: it
 > never names a concrete agent toolkit or vendor stack, and a guard test keeps it that way.
 >
 > The key words MUST, MUST NOT, SHOULD, and MAY are to be interpreted as in RFC 2119.
 > Requirements are labelled normative; anything marked *(reference)* documents the reference
 > implementation's concrete choice and is informative, not binding — except where the golden
 > suite (§7) freezes it.
 ## 1. Scope and conformance
 The method is the product: a swarm of agents generates candidate cost-saving measures for one
 project at a time; a **deterministic validator decides the numbers** (mandatory, blocking);
 domain experts judge the outcomes (human-in-the-loop); the system **learns from the verdicts**
 across runs. A conforming implementation:
 1. implements the 8-step loop of §3 with the contracts of §4–§10;
 2. reproduces the shared golden suite's decided outcomes (§7) on the shared example bundle;
 3. proves every load-bearing seam with a test that FAILS when that seam is detached (§11).
 **Honesty rule (unwaivable):** no artifact — code, docstring, README, or report — may claim
 more than the implementation does. Scripted stand-ins (synthetic clients, seeded verdicts)
 MUST be labelled as such wherever their output is presented.
 **Boundary:** this is a purely technical framework. The deploying organisation owns all
 processing purposes and impact assessments; implementations provide only the technical
 prerequisites (local-only operation, provenance, no silent egress) and a disclaimer.
 ## 2. Terms and architecture
 Three layers, strictly separated (the separation is load-bearing — see §3 Step 1 and §6):
 - **Context layer** — one curated, version-controlled knowledge bundle per project (an "LLM
  wiki") in the open OKF format: a directory of markdown files with YAML frontmatter, one
  required field `type`, a reserved `index.md` entry point, and intra-bundle cross-links.
  This layer holds project documents, methodology, verified literature, constraints, **and
  approved verdicts** (`type: verdict` files). It is what runtime reading and experience
  retrieval draw from.
 - **Output layer** — a run-scoped folder structure of raw results: proposals pending verdict,
  rejections with reasons, and raw verdict files (plain JSON, one per file). Append-heavy,
  **never part of the wiki**.
 - **Promotion gate** — the only path from the output layer into the context layer (§6). Only
  expert-approved knowledge crosses it.
 Other terms: the **IR** is the typed intermediate representation of a candidate measure
 (§7.1); the **store** is the in-memory collection of historical verdicts retrieval ranks over
 (§4.2); the **fold** is the injection of retrieved prior verdicts into the hypothesis prompt
 (§3 Step 1); the **two falsifiers** are the deterministic validator (numbers) and the debate
 checker (reasoning) — they judge the same candidate and are never conflated (§3 Step 4, §9).
 ## 3. The loop (normative)
 Eight steps. Steps 1–6 happen within one run; steps 7–8 close the learning loop across runs
 separated in time.
 ### Step 1 — Understand the context (navigate, never stuff)
 The agent read-context for a project MUST be built by **navigating** its OKF bundle with
 progressive disclosure — never by stuffing the whole bundle (or keyword-retrieved chunks of
 it) into the prompt:
 - Navigation starts at `index.md` and follows its intra-bundle markdown cross-links
  (`](target.md)`). Targets containing a path separator are out-of-bundle and MUST be
  skipped. Repeated links are de-duplicated; order is deterministic (index first, then links
  in first-seen order). *(reference: the link pattern is `\]\(([^)]+\.md)\)`)*
 - A missing `index.md` is an error (a bundle has no entry point without it). A broken or
  bundle-escaping cross-link MUST be tolerated — skipped, never raised (OKF robustness rule);
  path resolution MUST be boundary-checked against the bundle directory, fail-closed.
 - Frontmatter is the leading `---`-delimited block, parsed line-oriented as `key: value`
  strings; the single required field is `type`; unknown fields MUST be preserved.
 - The rendered read-context is the index body (the summary) followed by each non-index
  concept file as a `## {type}: {title}` section; empty sections are dropped.
 - **`type: verdict` files MUST be excluded from the read-context.** Prior verdicts reach the
  hypothesis prompt ONLY via the gated experience fold below — never via context rendering,
  and never via a query-time retrieval tool pointed at the bundle (which would re-leak the
  verdict layer).
 **Experience fold (ExpeL-style, the learning seam):** before generation, the candidate's
 prior verdicts are retrieved from the store and folded into the hypothesis prompt:
 - The retrieval query key is the bundle's candidate features, read from the IR projection
  (§7.1) — available *before* any proposal exists.
 - Seeding: every `type: verdict` file in the bundle becomes a store entry keyed on those
  candidate features, with `decision` from frontmatter (default `approved`) and a rationale
  built from the `description` frontmatter plus, when present, the structured learning
  fields rendered as `[realiseringsgrad={realization_rate}; forventet_faktisk_NOK=
  {expected_actual_saving_nok}]`.
 - Ranking is **structural, never textual** — surface text MUST NOT contribute to similarity.
  Similarity is the weighted sum `0.60 × Jaccard(affected-code sets) + 0.25 ×
  [measure-type equality] + 0.15 × [same magnitude bucket]`, with magnitude buckets
  `[0, 1e5), [1e5, 5e5), [5e5, 1e6), [1e6, ∞)` over the claimed saving and Jaccard of two
  empty sets defined as 1. Retrieval returns the top-k by similarity, ties broken by verdict
  id ascending (deterministic); k MUST be positive.
 - The fold prepends the retrieved verdicts (id, decision, rationale per line) to the
  generation context. The rationale is the carrier of the learning signal — the fold is what
  lets an expert's realization-rate correction reach the next hypothesis.
 ### Step 2 — Hypothesise (structured candidate generation)
 The proposer model is asked for exactly one candidate measure as a JSON object for the IR
 (§7.1): `project_id`, `measure`, `affected_items` (list of `{code, quantity, unit_cost}`),
 `claimed_saving_nok`, optional `assumptions`. A reply that fails to parse into the typed IR
 MUST be retried (a blind parse-retry), never silently accepted or repaired downstream —
 bounded by the budget meter (§8). `project_id` MAY be defaulted from the project when the
 model omits it. IR schema invariants (§7.1) are enforced at construction, so a malformed
 proposal can never exist as a value.
 ### Step 3 — Debate (maker-checker)
 Candidate reasoning is debated by a two-role maker-checker pair — a `proposer` and a
 `checker` — alternating turns, with the debate's converged proposer output feeding generation
 (Step 2's context). Requirements:
 - The debate MUST be round-capped (a hard maximum-rounds bound) with an additional
  turn-count termination safety net above it; an unbounded debate is forbidden (§8).
 - Debate state MUST be fresh per run — no conversation state may survive from one project
  run into the next.
 - The checker MUST be instructed to end its reply with exactly one verdict line:
  `VERDICT: APPROVE` if the reasoning holds, or `VERDICT: REJECT - <short reason>` if not.
 - An optional synchronous in-run review gate on the checker MAY be enabled (the short
  feedback timescale, §3 Step 7); durable in-run checkpointing is NOT required.
 ### Step 4 — Validate / falsify (two falsifiers, same candidate)
 **The deterministic validator gates the numbers — mandatory, blocking, never an optional
 plugin.** It is the one endpoint-free judge that anchors the loop against swarm
 self-confirmation. Semantics (frozen by the golden suite, §7.2):
 1. Schema invariants already hold (IR construction, §7.1).
 2. **Feasibility bound:** the maximum feasible saving is capped at a policy fraction
   (0.30) of the affected items' total cost. *(reference: computed with an LP solve whose
   closed form here is `0.30 × Σ quantity·unit_cost`; a missing solver MUST escalate, never
   silently fall back.)*
 3. **Risk simulation:** seeded Monte Carlo over uncertain unit costs yields `p10`/`p50`/`p90`
   percentiles of the feasible saving (§7.2 fixes the procedure).
 4. **Structural block:** a claim above the optimistic feasible bound (`p90`) yields a
   **rejection that is a distinct type from a validated proposal** — carrying the claimed and
   feasible figures in its reason, and no percentiles — so it can never be consumed as
   validated.
 **The checker gates the reasoning** (the second falsifier), parsed from the checker's LAST
 surfaced debate output, case-insensitively; the reject marker takes precedence and its
 trailing text is the reason. The gate is **opt-in-reject (fail-open)**: `VERDICT: APPROVE` or
 a missing/unparseable marker never blocks — the validator remains the sole gate on such runs.
 An explicit checker REJECT MUST override an otherwise-validated outcome into a rejection
 (reason prefixed with the checker's reason). A validator rejection stands regardless of the
 checker. The two falsifiers MUST be recorded separately (§9): the provenance field mirrors
 ONLY the validator; the checker's decision (`approve` / `reject` / `absent`) is reported as
 its own result field. Either the checker actually gates, or the debate must not be called
 maker-checker.
 ### Step 5 — Refine, informed and bounded
 When the validator rejects, the next attempt MUST be informed by the falsification: the
 previous attempt's rejection **reason** is fed verbatim into the next attempt's prompt as a
 revision instruction. Constraints (all normative):
 - Only the *most recent* rejection reason is carried — never an accumulated history
  (bounded prompt growth).
 - Only the *reason* is carried — never the prior proposal JSON (the model must address the
  falsification, not parrot the rejected candidate).
 - The refinement loop runs under the EXISTING caps — the attempt bound (`max_attempts`,
  reference default 3) and the token/round meter (§8). "Refine until good enough" without a
  cap is forbidden; no new loop may be introduced.
 - The only *per-attempt* falsifier in this loop is the deterministic validator. Seeding
  generation with the checker's critique is a run-level concern outside this loop's scope.
 - Attempt 1 MUST use the unchanged base prompt (the informed block appears only after a
  rejection).
 ### Step 6 — Discard or propose
 The run's outcome is either the validated proposal (with its percentiles) or a typed
 rejection with its reason — never a bare failure. Raw results (proposals pending verdict,
 rejections, captured verdict files) belong to the output layer (§2) as plain JSON — the raw
 layer deliberately does NOT use the wiki format (that is reserved for promoted knowledge).
 ### Step 7 — Respond to feedback (two timescales)
 - **Short loop:** an expert may review synchronously in-run (the optional Step-3 gate).
 - **Long loop (the async file inbox):** days or weeks later, an expert (or, in simulation, a
  persona) drops a verdict file into an inbox **folder**; a separate, later run picks it up
  whenever it lands. The system MUST be fully resumable across runs separated in time — no
  live-session assumption. Contract in §5. **Role split (unwaivable): the system READS the
  inbox; the expert writes it.** A run MUST NOT persist its own captured verdict back into
  the inbox (writing is the authoring primitive's and the promotion gate's job).
 In simulation a dedicated expert persona plays the human; in production a human uses the
 SAME folder interface. The persona is defined once, as a shared skill artifact (§4.3).
 ### Step 8 — Promote approved knowledge (optional + gated)
 When an expert APPROVES an outcome, it may be promoted from the raw output layer into the
 context layer as a `type: verdict` concept file, navigable by the next run's seeding
 (closing the learning loop through the file system). Promotion is an **opt-in public
 primitive** — it MUST NOT be wired into the run itself (the system reads context; the
 gate/persona promotes). Full gate semantics in §6.
 ## 4. The verdict contract
 Three machine-readable shapes carry expert judgement. All three share the decision
 vocabulary rule: **the run-path decision is binary** — `approved` or `rejected`. The
 adjusted-approval case (the signature case in practice: the measure is worth doing but the
 modelled saving overstates the expected actual) is an `approved` decision whose `rationale`
 records the correction. `approved_with_adjustment` exists ONLY in bundle-seed frontmatter
 and in the promotion gate's accepted set (§6) — a run-path feedback contract MUST reject it.
 ### 4.1 Run-path feedback
 The expert decision consumed by a run: `decision` ∈ {`approved`, `rejected`} (exactly two
 values) and a non-empty `rationale` string. Validated fail-fast at startup (§10).
 ### 4.2 The verdict file (raw output layer / inbox)
 One verdict per JSON file, named `{id}.json`. Top-level fields (all required):
 | Field | Meaning |
 |---|---|
 | `id` | The learning-loop key (see minting, below). Read VERBATIM on load — never re-minted. |
 | `decision` | The expert decision (§4 vocabulary). |
 | `rationale` | Prose carrying the knowledge the validator cannot compute. |
 | `proposal_features` | The structural features of the judged candidate (below). |
 `proposal_features` fields: `affected_codes` (emitted as a SORTED list), `measure_type`
 (string), `claimed_saving_nok` (number), `description` (string; surface text — deliberately
 excluded from both similarity ranking and id minting).
 **Id minting (normative):** `id` is the first 16 hex characters of the SHA-256 of the
 canonical JSON `{"affected_codes": <sorted list>, "claimed_saving_nok": <number>,
 "measure_type": <string>}` with keys sorted and separators `,`/`:` (no whitespace). The id
 therefore keys on the **candidate measure**, not the verdict event: a structurally identical
 proposal maps to the same id. Because raw JSON number formatting participates in the hash
 (`30000` vs `30000.0` differ), a loaded verdict's `id` MUST be kept verbatim — re-minting
 could diverge.
 **Conflict semantics (chosen, documented):** the in-memory store is FIRST-write-wins per
 `id` (repeated inbox merges are idempotent); the disk layers — inbox files and promoted wiki
 files — are LAST-write-wins per file. Two verdicts about the same candidate share an id and
 hence a filename. A full verdict-conflict taxonomy is deliberately deferred until real
 experts produce conflicting verdicts.
 ### 4.3 The persona example artifact
 The expert-reviewer persona is a shared skill: a persona prompt plus one canonical example
 verdict JSON with fields `decision`, `marker`, and `rationale`. Requirements:
 - `decision` MUST be a run-path value (§4.1); the canonical example is `approved`.
 - `marker` MUST be a substring of `rationale` — it is the traceable payload (the realization
  rate) a simulation follows from the persona's judgement into a later run's prompt.
 - Implementations MUST source the persona judgement from this artifact at call time (a
  loader, fail-fast on a missing/malformed file — it is required input), never from an
  inlined copy. The persona prompt's prose never names a concrete agent toolkit.
 ## 5. The inbox/outbox folder contract
 The long feedback loop's folder interface (§3 Step 7). Normative:
 - **One verdict per file**, `{id}.json`, shape per §4.2. The authoring primitive creates the
  directory if needed and writes deterministically *(reference: sorted keys, 2-space
  indent)*.
 - **Tolerant load** (the raw layer is written out of band; half-written or foreign files are
  realistic): a missing folder yields zero verdicts; files that are not `.json`, fail to
  parse, or lack a required top-level key (`id`, `decision`, `rationale`,
  `proposal_features`) are SKIPPED, never raised. Contrast: required inputs (the IR
  projection §7.1, the persona example §4.3) are fail-fast.
 - **Deterministic order:** files are processed sorted by filename.
 - **Merge, never replace:** a run ingests the inbox INTO its store (per-verdict add,
  first-write-wins per id) BEFORE the Step-1 fold, so a passed-in store's existing verdicts
  survive (cross-project threading) and repeated merges are idempotent.
 - **Role split:** the system reads; the expert/persona writes (§3 Step 7).
 ## 6. The promotion gate
 `promote` lifts one APPROVED verdict from the raw output layer into the OKF context layer.
 Normative semantics:
 - **Fail-closed:** a verdict whose `decision` is not in {`approved`,
  `approved_with_adjustment`} MUST be refused with an error, writing and linking NOTHING.
  Only human/persona-approved knowledge enters the wiki — never raw agent output
  (self-contamination).
 - **Provenance-stamped:** the promoted file records who approved, which experiment, and
  when. The timestamp MUST be an explicit required argument — no wall-clock default — so
  promotion is deterministic and reproducible.
 - **Minimal promoted file:** frontmatter `type: verdict`, the `decision`, the verdict's
  `rationale` as the `description` field (the learning signal as prose), the verbatim
  verdict id, the provenance stamp, and tags. The promoted file MUST NOT reproduce a
  hand-authored seed's structured learning fields (`realization_rate` etc.) — the raw
  verdict model carries the signal only as rationale prose, and seeding (§3 Step 1) folds
  the `description` in.
 - **Navigability:** the file is written into the bundle (path-safe, fail-closed against
  escaping names; filename `promoted-verdict-{token}.md` where the token is the id
  sanitised to `[A-Za-z0-9._-]`, a degenerate token falling back to a content hash) and
  linked from `index.md` — navigation follows only index cross-links, so an unlinked file
  is unreachable. Linking MUST be idempotent (re-promotion never double-links).
 - **Neutral label:** the index link label is FIXED and carries NO verdict signal. The index
  body flows verbatim into the rendered read-context, so a descriptive label (e.g. the
  rationale) would leak the learning signal around the gated fold.
 - **Per-candidate growth:** ids key on candidate features (§4.2), so two approvals of the
  same candidate share a filename — last-write-wins; the wiki grows one curated verdict
  file per distinct candidate, not one per verdict event.
 - *(reference limitation)* the index read-modify-write is not atomic — single-process use
  is assumed for the MVP.
 ## 7. Ground truth: IR projection and golden suite
 The shared example bundle ships two JSON files that are **the only ground truth** ("fasit")
 for cross-implementation equivalence. Implementations MUST consume them unchanged.
 ### 7.1 The IR projection (`validator-input.json`)
 The candidate measure projected into the typed cost-IR the validator consumes:
 - `project_id` (string), `measure` (string), `affected_items` — a non-empty list of
  `{code: string, quantity: number ≥ 0, unit_cost: number > 0}` — `claimed_saving_nok`
  (number > 0), and `assumptions`: a map `code → [low_unit_cost, high_unit_cost]` giving the
  uncertainty band per cost code for the risk simulation (empty = degenerate, no spread).
 - Construction-time invariant: the claimed saving MUST NOT exceed the affected items' own
  total (`Σ quantity·unit_cost`); violation is a schema error, not a validator rejection.
 - Loading the IR projection from a bundle is FAIL-FAST: a missing file raises (required
  input — contrast the tolerant inbox, §5).
 ### 7.2 The golden suite (`golden.json`)
 Two parts, both normative on their decided fields:
 - **`validator`** — the frozen deterministic outcome of validating the IR projection:
  `outcome` (the validated type's name), `validates` (true — the claim sits within the
  feasible range), `claimed_saving_nok`, `nominal_feasible`, and the percentiles `p10`,
  `p50`, `p90`. A conforming implementation MUST reproduce these values (approx-equality on
  floats) — either with the reference procedure below or an equivalent deterministic method
  that reproduces the golden outcomes. The meaningful assertion is `validates` = true
  (claimed ≤ `p90`); the frozen numbers are the regression net.
  Reference procedure (what generated the golden): `nominal_feasible = 0.30 ×
  Σ quantity·unit_cost`; Monte Carlo with a Mersenne-Twister PRNG seeded `20260624`, 512
  samples; per sample, iterate `affected_items` in order and draw the unit cost uniformly
  from the item's `assumptions` band (fixed cost when no band), the sample's feasible saving
  being `0.30 ×` the sampled total; percentiles are the 1st, 5th and 9th cut points of the
  10-quantiles (inclusive method) over the 512 feasible values.
 - **`learning_surface`** — what the validator CANNOT compute, encoded by the seed verdict:
  `modelled_saving_nok`, `realization_rate` (strictly between 0 and 1 — a realization gap),
  `expected_actual_saving_nok` (= `realization_rate` × `modelled_saving_nok`, internal
  consistency required), `gap_source`, and `context_key` (the context the correction holds
  for). This is the ExpeL seed's anchor: the signal Step 1's fold must carry into the next
  hypothesis, and the reason the learning loop exists at all.
 ## 8. Budget and stop criteria
 Never an unbounded loop, anywhere. Normative:
 - **Required at startup:** positive `max_rounds` and `max_tokens` caps (the termination
  contract, §10). Cap objects MUST refuse construction with non-positive values.
 - **Real usage, never a proxy:** token accounting MUST come from the provider-reported
  usage (total token count) after each model call — never a word-count or character proxy.
  On counting paths, a response missing usage MUST fail closed (an error), not silently
  stop counting.
 - **Structured stop:** crossing a cap raises a structured stop event carrying the breached
  kind (tokens or rounds), the limit, and the observed value — never a silent hang.
 - Every retry loop is attempt-bounded (Steps 2 and 5); the debate is round-capped (Step 3);
  round ticks are charged between attempts so the meter also bounds parse-retries.
 ## 9. Provenance
 Every proposal carries a first-class provenance stamp — authoritative data, not display
 metadata:
 - **At least one citation** into the source documents (file + exact text span + snippet); a
  run whose context yields no citable content MUST fail fast.
 - The `model` and `role` that produced the proposal. An injected test client's real model id
  is stamped when available; a neutral `unknown` is the fallback — never a fabricated name.
 - **`validator_decision`** ∈ {`validated`, `rejected`} — mirrors the DETERMINISTIC VALIDATOR
  only, stamped from the validator's outcome BEFORE any checker override, so a
  checker-gated proposal whose numbers passed is never mislabelled as validator-rejected.
  The checker's decision is a separate result field (§3 Step 4); the two falsifiers are
  never conflated.
 - The run's token usage (from the meter, §8).
 - Promotion provenance is §6 (who/experiment/when, explicit timestamp).
 ## 10. Startup contracts
 ALL configuration MUST be schema-validated fail-fast at startup, BEFORE any model client is
 constructed: the data source (a docs directory + a positive top-k), the model map (role →
 model id per backend profile, each profile REQUIRING a `default` entry), the termination
 contract (§8), and the feedback shape (§4.1). The first malformed contract raises; a run
 never starts on a bad config.
 ## 11. Load-bearing conformance tests
 A conforming implementation MUST prove each seam with a test that FAILS when the seam is
 detached ("green-but-dead" tests are the failure mode this rule exists to prevent). The
 required red-conditions, mirroring the reference suite (test names cited for
 cross-reference):
 | Seam | The test MUST fail when… | Reference test |
 |---|---|---|
 | Step-1 fold | a prior verdict no longer reaches the next hypothesis prompt; control: an empty store changes the outcome signal | `test_step1_expel_loadbearing.py` |
 | Verdict-layer exclusion | the realization signal appears in the rendered read-context | `test_okf.py` (bundle-context exclusion) |
 | Checker gate | the checker's surfaced output is detached OR its REJECT no longer overrides a validated outcome | `test_checker_gate_loadbearing.py` |
 | Informed refinement | the prior rejection reason no longer appears verbatim in the next prompt / the outcome never flips | `test_step5_refine_loadbearing.py` |
 | Async file loop | a verdict dropped after Run A fails to reach Run B's prompt via a FRESH store; control: an empty inbox | `test_step7_async_loop_loadbearing.py` |
 | Promotion gate | a non-approved verdict reaches the wiki; an approved one is not navigable; the index label leaks the signal | `test_step8_promotion_loadbearing.py` |
 | Persona artifact | the example drifts from the pipeline schema, or the judgement is re-inlined instead of artifact-sourced | `test_persona_skill_loadbearing.py` |
 | Closed loop | the two-run simulation's marker crosses runs without the promotion (or fails to cross with it) | `test_simulation_loadbearing.py` |
 | Golden regression | the validator's decided fields diverge from `golden.json` | `test_bygg_energi_mikro.py` |
 | Context-seam purity | the navigation/context module imports an agent toolkit | `test_okf.py` (import guard) |
 | Spec integrity | this spec goes missing, names a framework, or stops documenting a consumed contract field | `test_method_spec_loadbearing.py` |
 ## 12. Cross-check table
 Every field of the machine-readable contracts, mapped to its normative section (completeness
 is enforced by the spec-integrity test):
 | Field | Contract | Section |
 |---|---|---|
 | `decision` | persona example / verdict file / run-path feedback | §4, §4.1–§4.3 |
 | `marker` | persona example | §4.3 |
 | `rationale` | persona example / verdict file / run-path feedback | §4.1–§4.3 |
 | `id` | verdict file | §4.2 |
 | `proposal_features` | verdict file | §4.2 |
 | `affected_codes` | verdict file (features) | §4.2 |
 | `measure_type` | verdict file (features) | §4.2 |
 | `claimed_saving_nok` | verdict file (features) / IR projection / golden | §4.2, §7.1, §7.2 |
 | `description` | verdict file (features) / promoted frontmatter | §4.2, §6 |
 | `project_id` | IR projection | §7.1 |
 | `measure` | IR projection | §7.1 |
 | `affected_items` | IR projection | §7.1 |
 | `code`, `quantity`, `unit_cost` | IR projection (affected item) | §7.1 |
 | `assumptions` | IR projection | §7.1 |
 | `outcome`, `validates` | golden (validator) | §7.2 |
 | `nominal_feasible`, `p10`, `p50`, `p90` | golden (validator) | §7.2 |
 | `modelled_saving_nok`, `realization_rate`, `expected_actual_saving_nok` | golden (learning surface) | §7.2 |
 | `gap_source`, `context_key` | golden (learning surface) | §7.2 |
 | `approved`, `rejected` | decision vocabulary (run path, binary) | §4, §4.1 |
 | `approved_with_adjustment` | decision vocabulary (seed frontmatter + gate only) | §4, §6 |
 | `type` | OKF frontmatter (required field) | §2, §3 Step 1 |
 | `realization_rate` (frontmatter) | bundle seed (structured learning fields) | §3 Step 1, §6 |
--- a/shared/skills/expert-reviewer/SKILL.md
+++ b/shared/skills/expert-reviewer/SKILL.md
@ -1,68 +0,0 @@
 ---
 name: expert-reviewer
 description: Adopt the expert energy-advisor persona to judge a deterministically-validated cost-saving proposal — render a verdict (approve / approve-with-adjustment / reject) that encodes the realization gap the validator cannot compute. Use after the deterministic validator has accepted a proposal's numbers and a human-grade domain judgement is needed.
 ---
 # Expert reviewer — energy advisor (M&V)
 You are an experienced energy advisor and measurement-and-verification (M&V) professional. Your
 role in the loop is the **human-grade judgement** that comes *after* the deterministic validator
 has already confirmed a proposal's numbers are arithmetically sound and within a feasible range.
 You are not a calculator and you are not a second validator — you supply the experiential knowledge
 the math cannot reach.
 This persona is **framework-neutral**: it is consumed unchanged by every implementation of the
 method. It depends on no specific agent toolkit, transport, or vendor.
 ## What you receive
 1. A **validated savings proposal** for one project measure: the measure, the affected cost items,
   the claimed saving, and the validator's confirmation that the claim sits within the feasible
   (e.g. P90) range.
 2. The project's **curated knowledge bundle** — project documents, the assessment methodology, the
   verified literature on realization gaps, and the hard constraints (budget, what cannot change).
 ## What you produce
 A single verdict, two fields:
 - `decision` — `approved` or `rejected`. The feedback the loop consumes is **binary**. The
  *approve-with-correction* case — the signature case in energy work, where the measure is worth
  doing but the modelled saving overstates the expected actual — is an `approved` decision whose
  rationale records the correction. Reserve `rejected` for measures that should not proceed
  (infeasible in practice, unsafe, mandated spec, or a realization gap that erases the benefit).
 - `rationale` — prose that carries the knowledge the validator cannot compute. For an approval that
  corrects, the rationale MUST state the **realization rate** you expect and the **expected actual**
  saving, and *why* — the specific operational mechanism, not a generic hedge. This is where the
  learning signal lives; it is folded back into the next run's hypothesis.
 The canonical machine-readable shape is in [references/example-verdict.json](references/example-verdict.json).
 ## The judgement — the realization gap
 The deterministic validator proves the *modelled* saving is correct from the parameters. Your job
 is to judge the **realization gap**: the systematic bias between that modelled saving and what the
 building will *actually* realize in operation. This gap is **not** parameter spread (the validator's
 risk simulation already covers that) — it is a directional skew the parameters do not carry, visible
 only in accumulated operating experience:
 - **Hours-of-use overestimation (usually dominant):** the assumed schedule typically exceeds metered
  burn time — daylight, empty rooms, occupancy controls. A timetable-stipulated 3000 h often meters
  materially lower.
 - **In-service rate < 1:** not every installed unit is necessarily mounted and operating at the time
  of evaluation.
 - **Behaviour and persistence:** rebound (more light because it is "now free") and overridden controls
  erode the saving over time.
 You cannot derive the realization rate from the proposal's parameters — that is exactly why a human
 judgement is required here and a deterministic rule is not. Ground every correction in the bundle's
 verified literature; never invent a number.
 ## Discipline
 - **Provenance:** your verdict is stamped with who judged it, on which experiment, and when. Only an
  approved (or approved-with-adjustment) verdict is eligible to be promoted back into the project's
  knowledge base; a rejection never contaminates it.
 - **Context-bound learning:** state the context your correction holds for (building type, the source
  of the hours-of-use assumption). The next similar proposal in the same context should inherit it.
 - **Honesty:** if you lack the experience to judge a measure, say so and do not fabricate a rate.
--- a/shared/skills/expert-reviewer/references/example-verdict.json
+++ b/shared/skills/expert-reviewer/references/example-verdict.json
@ -1,5 +0,0 @@
 {
  "decision": "approved",
  "marker": "realiseringsgrad=0.79",
  "rationale": "Godkjent med realiseringskorreksjon. Den modellerte besparelsen er teknisk korrekt fra parameterne og validatoren bekrefter at den er innenfor feasibelt omraade. Men i drift realiseres erfaringsvis ~79% av en timeplan-stipulert LED-besparelse i kontorbygg (realiseringsgrad=0.79) pga. overestimerte driftstimer og in-service rate < 1; forventet faktisk besparelse ca 23700 NOK/aar."
 }