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feat(persona): build the shared expert-reviewer persona as a framework-neutral Agent Skill

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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
This commit is contained in:
Kjell Tore Guttormsen 2026-06-30 13:59:42 +02:00
commit 6f861a0078
8 changed files with 281 additions and 13 deletions
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29
src/portfolio_optimiser/simulation.py
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@ -36,6 +36,7 @@ from agent_framework import (
)
from agent_framework_openai import OpenAIChatCompletionClient
from portfolio_optimiser.persona import load_persona_example
from portfolio_optimiser.run import RunResult, run_project
from portfolio_optimiser.validator import ValidatedProposal
from portfolio_optimiser.verdicts import VerdictStore, promote_verdict, seed_store_from_bundle
@ -52,14 +53,10 @@ _VALID_PROPOSAL = (
# The checker's debate turn ends with the gate marker the run parses (run._checker_verdict).
_CHECKER_APPROVE = "Tallene er innenfor feasibelt område og resonnementet holder. VERDICT: APPROVE"
# The persona's verdict: an APPROVE that carries NEW realization knowledge the validator cannot
# compute. The marker (a realization rate ABSENT from the bundle — the seed is 0.82) is the payload
# we trace from Run A's persona judgement, through promotion, into Run B's hypothesis prompt.
_DEFAULT_MARKER = "realiseringsgrad=0.79"
_DEFAULT_PERSONA_RATIONALE = (
"Godkjent. I drift realiseres erfaringsvis ~79% av en timeplan-stipulert LED-besparelse i "
f"kontorbygg ({_DEFAULT_MARKER}) pga overestimerte driftstimer; forventet faktisk ca 23700 NOK/aar."
)
# The persona's verdict is sourced from the shared expert-reviewer skill (``load_persona_example``),
# NOT inlined here — that de-stubs the persona and makes the shared artifact genuinely consumed. Its
# marker (a realization rate ABSENT from the bundle — the seed is 0.82) is the payload we trace from
# Run A's persona judgement, through promotion, into Run B's hypothesis prompt.
class ScriptedChatClient(OpenAIChatCompletionClient):
@ -148,19 +145,29 @@ async def simulate_learning_loop(
bundle_dir: str,
work_dir: str,
*,
persona_rationale: str = _DEFAULT_PERSONA_RATIONALE,
marker: str = _DEFAULT_MARKER,
persona_rationale: str | None = None,
marker: str | None = None,
timestamp: str = "2026-06-30",
max_rounds: int = 3,
) -> LearningSimulationResult:
"""Run the loop twice on a throwaway COPY of the bundle (the shared fixture is never mutated),
with a promotion in between, and trace whether the persona's approved knowledge crosses runs.
The persona's verdict (decision + rationale + traced ``marker``) defaults to the shared
expert-reviewer skill's canonical example (``load_persona_example``), read at CALL time — so the
simulation is genuinely artifact-driven, not inlined. Callers may override ``marker`` /
``persona_rationale`` for a control.
Run A: a fresh (empty) wiki -> an uninformed hypothesis; the persona approves with NEW realization
knowledge (``marker`` in ``persona_rationale``). ``promote_verdict`` lifts that verdict into the
wiki; ``seed_store_from_bundle`` re-reads the wiki; Run B's Step-1 ExpeL fold then carries the
marker into its hypothesis prompt. The two runs use SEPARATE sinks so each prompt set is
inspected independently."""
example = load_persona_example()
if marker is None:
marker = example.marker
if persona_rationale is None:
persona_rationale = example.rationale
if marker not in persona_rationale:
raise ValueError("marker must be a substring of persona_rationale (the carried payload)")
@ -168,7 +175,7 @@ async def simulate_learning_loop(
shutil.copytree(bundle_dir, copy)
copy_s = str(copy)
replies = {"proposer": _VALID_PROPOSAL, "checker": _CHECKER_APPROVE}
verdict_input = {"decision": "approved", "rationale": persona_rationale}
verdict_input = {"decision": example.decision, "rationale": persona_rationale}
# Run A — empty wiki isolates the persona's NEW knowledge.
sink_a: list[str] = []