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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Act as the gate-driven scheduler and state reader. Do not solve models, write model code, or draft paper sections.
../../AGENTS.md is the packaged policy source. Prefer a project-root AGENTS.md when one exists; otherwise read the packaged copy relative to this SKILL.md. Apply that policy without reproducing large reports or dashboards.
Before orchestration in a new workspace:
git status --short;planning/session_config.json, accepting legacy mode.Report warnings concisely. Do not create the full project skeleton unless the user is initializing a project.
Prefer, in order:
planning/manifests/Qx.jsonNever trust a dashboard over newer canonical artifacts.
Maintain one compact JSON manifest per subquestion:
{
"schema_version": 1,
"question_id": "Q1",
"rigor_profile": "lean",
"current_gate": "G2",
"status": "method_screened_waiting_human",
"artifacts": {
"method_card": "methods/Q1/q1_method_card.md",
"decision_ledger": "methods/Q1/q1_decisions.jsonl",
"risk_probe": "methods/Q1/probes/risk_probe_summary.json",
"latest_run": null
},
"allowed": {
"code_generation": false,
"freeze": false,
"paper_writing": false,
"final_assembly": false
},
"blockers": [],
"next_action": {
"owner": "human",
"skill": "decision-prompt-builder",
"reason": "method choice not recorded"
},
"updated_at": "ISO-8601"
}
Update only fields affected by the current state change. Generate a human dashboard on request or at a milestone; otherwise derive status directly from manifests.
Evaluate each Qx independently.
Pass when parse, classification, data inventory, success criteria, and human framing exist. A placeholder in a human-owned field blocks the gate.
Pass when:
qx_method_card.md defines a main candidate and usable baseline;risk_probe_summary.json covers applicable checks, including output degeneracy;PASS or justified CONDITIONAL;Do not require a fixed number of candidates, universal PoCs, or a source-line limit.
Pass when qx_decisions.jsonl contains a human DECIDED method choice citing probe evidence. While blocked, allow data preparation but not model code generation.
Pass when:
run_summary.json is complete;Accept legacy Markdown review artifacts during migration, but prefer JSON for new work.
In lean, pass the result-judgment subgate when final-result and stability decisions cite computed evidence. Continue iterating without freezing when the human selects adjust or fallback.
In submission, additionally require:
frozen_numbers.json.Require the three writer rules, frozen-number sourcing, human-confirmed interpretation/claim scope, and verified figures.
Evaluate only in submission. Require passing consistency, completeness, and QA artifacts. Never infer that one auditor covers another.
Choose one primary next action:
data-auditor-cleaner;method-selector;decision-prompt-builder;model-code-analyzer;robustness-checker;Do not invoke several judgment-bearing skills speculatively.
Classify changes before scheduling checks:
NONE: scratch, formatting, comments, non-semantic docs.LOCAL: exploratory code or method-card updates before freeze.CANONICAL: schema/units, symbols, equations, parameters, official values, figure paths.FROZEN: changes affecting frozen values or paper claims.Route checks:
NONE: none.LOCAL: local tests/review.CANONICAL: scoped consistency for affected Qx.FROZEN: thaw log, rerun affected work, re-freeze, scoped consistency.Never schedule a full-workspace consistency audit solely because more than one file changed.
In lean:
In submission:
Read legacy artifacts when new ones are absent:
planning/progress_dashboard.mdqx_method_candidates.mdqx_method_iteration_log.mdqx_decision_log.mddecisions/*_modeler_decision.mdMark them legacy_source in the manifest and recommend migration at the next material edit. Do not regenerate legacy files for new work.
Return a compact state report:
Do not paste a full dashboard or large JSON structure unless the user asks.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.