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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
@ -0,0 +1,62 @@
+# 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, realized 2026-07-03):** the shared core lives in its own
+  repository, [`portfolio-optimiser-commons`](https://git.fromaitochitta.com/ktg/portfolio-optimiser-commons)
+  — the **source of truth**. Each implementation repo consumes it as a **git subtree**
+  at this unchanged `shared/` path (so tests and the `PORTFOLIO_SHARED_ROOT` default
+  resolver are unaffected). Do not edit commons content anywhere else without syncing.
+
+## Subtree sync (pull-only — run from the consuming repo's root)
+
+The remote is registered as `commons`
+(`ssh://git@git.fromaitochitta.com/ktg/portfolio-optimiser-commons.git`).
+
+**All edits land in commons first** (clone it, commit, push there), then each
+consuming repo pulls them in:
+
+```sh
+git subtree pull --prefix=shared commons main --squash
+```
+
+**Never run `git subtree push` from a consuming repo.** Observed 2026-07-03: because
+this repo's history contains commits that create/delete the `shared/` prefix, the
+push re-split leaked the consumer's *entire* history into commons (cleaned up by
+force-push the same day). Pull-only keeps commons the clean source of truth.
+
+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
@ -0,0 +1,40 @@
+---
+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
@ -0,0 +1,21 @@
+{
+  "_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
@ -0,0 +1,42 @@
+---
+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
@ -0,0 +1,46 @@
+---
+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
@ -0,0 +1,40 @@
+---
+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
@ -0,0 +1,66 @@
+---
+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
@ -0,0 +1,16 @@
+{
+  "_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
@ -0,0 +1,56 @@
+---
+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
@ -0,0 +1,441 @@
+# 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
@ -0,0 +1,68 @@
+---
+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
@ -0,0 +1,5 @@
+{
+  "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."
+}