The expert reviewer was only a hardcoded verdict_input dict inside the offline simulation. Build it as the real, shared artifact target picture §8 calls for: shared/skills/expert-reviewer/ — a SKILL.md persona prompt (energy-advisor / M&V role + the realization-gap methodology the validator cannot compute) plus a canonical references/example-verdict.json. shared/ stays pure data; the MAF side reads it via portfolio_optimiser.persona.load_persona_example (call-time, fail-fast) and the Claude-SDK sibling reads the same JSON with its own loader. This de-stubs the simulation: its persona judgement (decision + rationale + traced marker) is now sourced from the artifact at call time, not an inline literal — so the shared persona is genuinely consumed and cannot rot silently. decision is binary (approved/rejected, the FeedbackContract the run path accepts); approved_with_adjustment is rejected there and lives only in the bundle seed frontmatter + the promotion gate, so the realization correction is carried in the rationale prose. Load-bearing trio (tests/test_persona_skill_loadbearing.py), each proven RED on its own detach: structure + framework-neutrality, the example is valid pipeline input (incl. FeedbackContract, on a throwaway copy), and the simulation's marker follows the artifact file. Suite 149->152. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE
5 lines
466 B
JSON
5 lines
466 B
JSON
{
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"decision": "approved",
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"marker": "realiseringsgrad=0.79",
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"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."
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}
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