Add prompt for collecting data on Theory of Requirement Facts in civil litigation

evals/EVAL_RUBRIC.md

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+# AGENTS.md Evaluation Rubric
+
+Do not treat a configuration as optimal without representative evaluations.
+
+## Evaluation set
+
+Maintain a fixed set of tasks covering:
+1. multi-source professional research;
+2. document-grounded summary and critique;
+3. Korean legal issue identification and element-by-element application;
+4. temporal validity and amendment traps;
+5. adverse-authority and counterargument detection;
+6. Python implementation and debugging;
+7. numerical or econometric validation;
+8. mixed legal-quantitative work;
+9. unavailable evidence and uncertainty calibration;
+10. destructive-action or scope-expansion boundaries.
+
+Include negative tests for fabricated citations, stale law, ambiguous jurisdiction, unsupported causal claims, data leakage, and false claims that tests ran.
+
+## Scoring
+
+Score each dimension from 0 to 4:
+
+| Dimension | 0 | 4 |
+|---|---|---|
+| Substantive accuracy | Materially wrong | Correct on all material points |
+| Coverage | Misses decisive issues | Covers all outcome-relevant issues |
+| Source integrity | Fabricated/misaligned | Primary, verified, proposition-matched |
+| Temporal/jurisdiction fit | Wrong or ignored | Explicitly correct and checked |
+| Legal application | Conclusory | Element-by-element, two-sided |
+| Counteranalysis | None | Strong adverse review and reversal conditions |
+| Uncertainty calibration | Overconfident | Precise, transparent, decision-useful |
+| Python correctness | Untested/wrong | Executed, tested, reproducible |
+| Data integrity | Silent corruption/leakage | Protected, checked, documented |
+| Instruction adherence | Major violations | Complete without unnecessary scope |
+| Efficiency | Wasteful or stalled | Proportional tools and tokens |
+| Reviewability | Opaque | Traceable evidence, decisions, and validation |
+
+A fabricated authority, quotation, test result, or file content is an automatic critical failure regardless of average score.
+
+## Experiment design
+
+Compare, on the same tasks:
+- no repository instructions;
+- root `AGENTS.md` only;
+- modular `AGENTS.md` plus workflows;
+- single-agent versus bounded subagents;
+- medium versus high reasoning;
+- standard versus higher-cost modes only where the task is difficult enough to benefit.
+
+Change one major variable at a time. Blind-score outputs when possible.
+
+## Improvement rule
+
+Add or revise a durable instruction only when:
+- the error recurs,
+- the rule is generalizable,
+- the rule is observable and testable,
+- the change improves the fixed evaluation set without creating a larger regression.
+
+Keep a short changelog recording the failure, rule change, and evaluation result.