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result-report-generator

Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.

Star
1,057
许可证
MIT
最近提交
2026年9月24日

plugins/mathmodeling-skills/skills/result-report-generator/SKILL.md

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Purpose

Turn saved experiment artifacts into compact evidence. Do not treat ordinary successful runs as requiring a long report, and do not choose the winning method.

Inputs

  • run_summary.json
  • method card and probe summary
  • decision ledger
  • saved tables, metrics, and figures
  • session rigor_profile

Stop if the run summary claims outputs that do not exist or if main and baseline are not comparable.

Modes

Ordinary lean round

  • Validate the run summary and referenced artifacts.
  • Return a compact evidence digest in the conversation.
  • Do not save a Markdown report unless:
    • a fallback trigger fired;
    • a material anomaly or contradiction exists;
    • the human must make a proceed/adjust/fallback decision.

Decision-point round

Save:

results/Qx/experiments/roundN/qx_decision_report.md

Include only:

  • main vs baseline metrics;
  • output-degeneracy/concentration evidence;
  • assumption or feasibility warnings;
  • robustness evidence already available;
  • fallback trigger state;
  • unresolved trade-offs.

Then invoke decision-prompt-builder. After the human answers, route the answer to modeler-decision-logger.

Final/submission mode

Save:

results/Qx/reports/qx_final_result_analysis.md

Include:

  • final main/baseline comparison;
  • uncertainty and error;
  • concentration/degeneracy interpretation;
  • robustness links;
  • limitations and applicable scope;
  • exact source paths for numerical claims.

Rejection and Fallback

  • Archive a method only after a human result_verdict or fallback_activation decision.
  • Move rejected code and outputs to workspace/archived/<Qx>/<method>_REJECTED_roundN/.
  • Add one compact history line to qx_method_card.md; do not create a separate iteration log.
  • Do not archive from an AI suggestion alone.

Rules

  • Do not fabricate metrics, comparisons, or interpretations.
  • Separate facts from human verdicts.
  • Do not create result-report-generator_modeler_decision.md.
  • Do not repeat the full run summary; cite it and extract only decision-relevant evidence.
  • Do not call a diagnostic reference a usable baseline.
  • Do not generate paper prose.

Verification

  • Every reported number resolves to a saved artifact.
  • Main/baseline comparison uses the same split, unit, and metric definition.
  • Output concentration and fallback trigger are addressed.
  • Reports are generated only at decision points or final mode.
  • Human verdicts are read from or appended to the canonical JSONL ledger.

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