code-reviewer
FreeDetect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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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.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
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.
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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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
The English summary is being prepared. Showing the original description.