本文へ移動
cccskills
無料GitHub で公開

modeler-decision-logger

Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md3.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Purpose

Make human judgment traceable without multiplying decision files.

Canonical Output

methods/Qx/qx_decisions.jsonl

One JSON object per line. Records are append-only.

Use planning/framing_decisions.jsonl for global/pre-Qx framing decisions.

Required Fields

{
  "schema_version": 1,
  "decision_id": "q1_method_choice",
  "decision_type": "method_choice",
  "status": "DECIDED",
  "decided_by": "human",
  "captured_in_mode": "learning",
  "choice": "M2",
  "rationale": "Human-authored reason tied to evidence.",
  "evidence_refs": ["methods/Q1/probes/risk_probe_summary.json"],
  "decided_at": "ISO-8601",
  "supersedes": null
}

Optional structured fields may include confidence, rejected alternatives, round action, claim scope, assumption labels, or fallback activation.

Workflow

  1. Receive the human's answer, the choice-card ID, and evidence paths.
  2. Preserve the user's meaning and wording. Normalize only structure, identifiers, and whitespace.
  3. Verify:
    • the choice is one of the presented options or explicitly records a user-supplied alternative;
    • evidence paths exist;
    • rationale is non-empty and contains no placeholder;
    • the record does not falsely label AI-authored prose as human-authored.
  4. Append one JSON line.
  5. If revising a decision, append a new record with supersedes; never overwrite history.
  6. Update the compact history in qx_method_card.md only when the decision changes method state.
  7. Update the manifest gate/status fields when present.

Decision Types

Typical values:

  • framing
  • method_choice
  • fallback_activation
  • result_verdict
  • stability_verdict
  • assumption_necessity
  • claim_scope
  • package_signoff
  • submission_authorization

Staleness

Mark a decision stale only when its cited evidence materially changed:

  • append a decision_stale record naming the old decision and changed evidence;
  • ask the human to reconfirm through one choice card;
  • do not mark decisions stale because unrelated files or formatting changed.

Legacy Migration

Read legacy:

  • methods/Qx/qx_decision_log.md
  • methods/Qx/decisions/*_modeler_decision.md

Migrate only completed human decisions. Preserve original timestamps and source paths when available. Do not convert PENDING placeholders into decisions.

Rules

  • Never choose, rationalize, strengthen, or complete the user's decision.
  • Never create a separate pending decision artifact.
  • Never copy ai_suggestion into the human rationale.
  • One evidence-linked sentence is enough; do not impose arbitrary prose length.
  • Preserve an honest AI-use provenance distinction.

Verification

  • JSONL is valid one-object-per-line.
  • Record is append-only and uniquely identified.
  • Human ownership and evidence are accurate.
  • Supersession and staleness preserve history.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.

日本語の概要は準備中です。原文の説明を表示しています。

zhnnky329/MathModeling-skills1,0572026年9月24日 更新

zhnnky329 のスキルをすべて見る

このスキルの問題を報告する