math-modeling-skill-sion

name	math-modeling-skill-sion
description	面向 CUMCM、MCM/ICM、APMCM 等数学建模竞赛的证据与合规 Harness。用于新建或迁移比赛项目、题意与模型选型、数据/验证合同、真实运行、结果冻结、证据追溯、技术写作、确定性 QA、提交检查与不可变交付；普通数学题且不涉及竞赛交付链时不要使用。

Math Modeling Evidence Harness

Trigger and scope

Use this skill when a user asks to build, audit, migrate, run, validate, write, or submit a mathematical-modeling competition project. Start from the project root and use the thin harness CLI; keep the paper project separate from the Harness repository.

Do not use it for a one-off textbook solution, an informal plot, or a generic Python experiment with no evidence or competition-delivery obligations.

Core principles

1. Treat competition_profile.json as the only competition-rules contract.
2. Treat receipts as execution facts, the run index as a rebuildable projection, and the normalized manifest as the control plane.
3. Resolve exactly one preset: sprint, research, or submission. Overrides are allow-listed capability adjustments, not new presets.
4. Keep Contest Safety, independent validation, human checkpoints, result freeze, and submission immutability non-bypassable.
5. Never hand-write gate PASS values, receipts, hashes, freshness, claim inventories, or validation verdicts. A reviewer/human may create a new schema-valid review report; operators must not rewrite an existing report to manufacture PASS.
6. Preserve failed runs and failure evidence. A failed run is evidence, not a reason to delete a counterexample.
7. Hashes prove byte identity only. They do not prove leakage safety, objective semantics, mathematical correctness, units, statistics, or figure claims.
8. Use the order semantic validation -> accept -> freeze -> hash binding.
9. Stop on missing inputs, FAIL, ERROR, unresolved official rules, or a pending human decision. Do not promote a plan, demo, or regression as a benchmark result.
10. Treat MODELING_PLAN.md, PAPER_OUTLINE.md, PROJECT_BRIEF.md, and SUBMISSION_CHECKLIST.md as deterministic projections only. They never replace contracts, receipts, Gate reports, or frozen artifacts.
11. AI usage starts as unknown. Before S1, record each use or obtain an explicit human none declaration; an empty registry is not proof of no use. Harness-observable AI backends must log automatically as pending; a human must run harness ai verify before strict promotion.

Normal path

harness init -> S0 -> M1 -> P1 -> P2 -> W1 -> W2 -> S1 -> F1

Only advance after the current Gate has a factual report and its required human checkpoint. A later status value in a stale manifest cannot override an earlier factual failure.

S0 — scope and safety

Resolve a standalone competition-profile seed and inspect the current rule snapshot, endpoints, AI policy, page scope, and human checks. A maintainer seed is status: seed or unresolved; it is not an official verified rule. Keep official rules separate from a stricter local policy.

Minimum I/O: project root, competition_profile.json, run_manifest.json, artifact_dag.json, and run_index.json.

M1 — model and validation contract

Record the problem mechanism, data boundary, at least two candidate models (or a documented reason for an exception), assumptions, typed parameter provenance, formulas, implementation map, and falsifiable validation duties. Use assumption_forks for ambiguous question semantics and declare objective and risk semantics before coding. Run harness prepare M1 before the M1 human checkpoint; review its modeling plan, but never infer M1 PASS from that Markdown projection.

Minimum I/O: ready model_contract.json, evidence registry, current profile, M1 human checkpoint, and a current DAG. In research/submission, formal literature linkage and scope checks are required.

P1 — smoke execution

Run the smallest real process through run_and_record.py. The receipt must be process-captured, tied to this run, and successful; copied command text or an exit code in a projection is not a receipt.

Minimum I/O: one successful smoke receipt and the v2 run-index projection.

P2 — full run, independent validation, and result freeze

Run the selected full computation and an independent evaluator. Recompute each declared obligation, including sensitivity/OOS/failure evidence when triggered. Freeze the result only after semantic validation. Only status: frozen, validation_verdict: PASS, and claimable: true results may enter evidence.

Minimum I/O: exactly one selected full receipt, one successful freeze receipt, one frozen-results artifact, required input/output bindings, and P2 human checkpoint.

W1 — evidence and paper plan

Build one incremental evidence registry and a paper plan mapping each claim to verified evidence. Keep result definitions, population, units, and rounding consistent. Do not fill missing evidence with prose. Run harness prepare W1 to project the machine IR into a paper outline and deliverables/appendix plan. Missing evidence must remain visibly missing.

Minimum I/O: ready paper_plan.json, verified evidence references, current frozen result, and W1 human checkpoint.

W2 — deterministic content QA

Generate the writer package and draft, then run deterministic QA, mathematical semantic checks, consistency checks, template/build checks, PDF checks, and visual review required by the resolved capabilities. Semantic Critic and human review are separate from mechanical QA.

Review is executable, not declarative: run harness review after the draft. It runs deterministic QA, materializes the allow-listed review bundle, and routes the required perspectives (sprint: semantic critic; research: semantic + judge; submission: research set plus one review with independence_level >= L1). Reports land under reports/review/ as generated evidence; W2 recomputes verdict, artifact binding, and freshness from those reports — never from manifest self-reports. Open blocker/high/medium findings block W2 unless a medium finding is explicitly accepted with justification; a paper change makes the old review stale. --fresh binds the bundle, child working directory, receipt, and report, but is not an OS sandbox: submission still needs a genuinely separate context/model. Load the review references via the router before acting as a reviewer.

Minimum I/O: paper source/draft, abstract, conclusion, deterministic QA report, current review reports, current evidence chain, and W2 human checkpoint. research requires the full evidence chain; submission additionally requires strict math/editorial and current template/bibliography evidence. Run harness prepare W2 to refresh the project brief and targeted revision view; it does not run or replace deterministic QA/review.

S1 — current submission checks

Check the current verified profile, page scope, support package, AI disclosure, required manual checks, and final PDF. A seed profile cannot pass submission. The Harness never uploads on a user's behalf. Run harness prepare S1 to generate the AI ledger/disclosure draft, submission checklist, and mutable staging directories. It must not populate immutable submission/final/ or claim readiness.

Minimum I/O: verified profile, current PDF/support/disclosure artifacts, S1 report, and S1 human checkpoint.

F1 — immutable delivery

Freeze the submission package and bind the final source/PDF/package identities in the submission manifest. Any changed immutable input requires a new version, new digest binding, downstream invalidation, and rerun of affected Gates.

Minimum I/O: immutable submission manifest and receipt. F1 is not a mutable run_manifest.gates flag.

Presets

Preset	Use	Integrity posture
sprint	fast exploratory/model smoke work	identity boundaries; explicit result freeze
research	default formal research run	selected-run lineage, independent validation, selective critical hashes
submission	current contest delivery	strict math/editorial checks and final-chain immutability

Never disable Contest Safety, human checkpoints, independent validation, result freeze, or submission immutability with an override.

CLI quick start

python scripts/harness.py init --project C:\work\q1 --competition cumcm --preset research
python scripts/harness.py status --project C:\work\q1
python scripts/harness.py prepare M1 --project C:\work\q1 --json
python scripts/harness.py ai status --project C:\work\q1 --json
python scripts/harness.py check M1 --project C:\work\q1 --profile research --json
python scripts/harness.py run --project C:\work\q1 --stage smoke -- python model.py
python scripts/harness.py review --project C:\work\q1 --json
python scripts/harness.py review --project C:\work\q1 --recheck --json
python scripts/harness.py ai verify --project C:\work\q1 --usage-id AI-REVIEW-... --checked-by-role team-lead --verification-method "checked report against evidence" --human-changes "record accepted edits or state that none were adopted"
python scripts/harness.py validate --project C:\work\q1 --strict
python scripts/harness.py prepare S1 --project C:\work\q1 --json
python scripts/harness.py freeze --project C:\work\q1 --kind results --source results.json --output frozen_results.json --run-id run-1 --model-contract model_contract.json --code model.py --validation validation.json
python scripts/harness.py profile --project C:\work\q1 --json
python scripts/harness.py doctor --project C:\work\q1 --json
python scripts/harness.py migrate --project C:\work\legacy --json

For conceptual process/framework figures, choose an archetype independently of style_profile: research_framework, computational_pipeline, parallel_integration, method_architecture, or iterative_optimization. The planner may change geometry and primitives only; it must preserve nodes, edges, source refs, semantic roles, and the existing palette system. Data plots and result figures remain deterministic rather than Draw.io archetypes.

Use --json for agent consumption. Underlying checkers retain their stdout, stderr, and exit code. Use their script-level flags only for debugging; the normal path should require one project root plus stage/preset.

Progressive disclosure

Read only the relevant router entry before a specialized task:

• reference router for contracts, Gate policy, validation, writing, figures, literature, and submission details;
• artifact contracts for ownership, result semantics, and generated artifact boundaries;
• Gate policy for Gate-specific checks.

Detailed statistics, optimization, prediction, mechanism, writing, figure, citation, template, and contest-submission rules stay in the existing one-level routed references. Do not load all cards into the active context.

Migration and safety reminders

Run harness migrate --project <legacy-root> to a separate migration_v2/ directory. Read migrated, inferred, unresolved, deprecated, and manual_review_required before promotion. Migration never rewrites historical frozen evidence and never upgrades a legacy command declaration to a receipt.

Status is read-only and a v1 status is explicitly deprecated. A DAG freshness projection is evidence for the next action, not permission to rewrite the DAG. Regression tests prove stated code paths; they are not capability benchmarks.

For release work, report commands actually run, exit codes, artifact paths, uncovered risks, and whether any real benchmark was run. Quarantine previous national-contest papers under references/precedents/cumcm/ and MCM/ICM papers under references/precedents/mcm-icm/, register their provenance in the local indexes, and extract reusable mechanisms into pattern-cards/ before Writer use. Raw precedent papers are not current evidence and must not silently enter a new project.