| name | mm-problem-intake |
| description | 数学建模竞赛 V2 题面与数据建档阶段。用于读取赛题、附件和资源，拆解子问题，审计数据风险，建立可恢复上下文，并为后续建模与代码阶段提供结构化输入。 |
| allowed-tools | Bash(*), Read, Write, Edit, Grep, Glob, Agent, WebSearch, WebFetch |
Problem Intake
文件关系全貌请见 [[FILE_RELATIONSHIP_MAP]] · 上游: [[skills/mm-start-contest-v2/SKILL|V2 总控]] · 下游: [[skills/mm-model-strategy/SKILL|Phase 2 Strategy]] · 共享规范: [[skills/_references/SKILL|_references]]
Load First
Read:
../_references/v2_pipeline_contract.md../_references/codex_subagent_protocol.md../_references/rag_usage_contract.md../_references/source_quality_policy.md../_references/problem_type_router.md
Required Outputs
Create or update:
PROBLEM_BRIEF.mdDATA_AUDIT.mdWORKFLOW_STATE.mdreports/AGENT_RUNS.mdreports/INTAKE_GATE.md
Procedure
- Inventory all provided problem statements, PDFs, DOCX files, spreadsheets, CSV files, images, and downloaded resources.
- Extract the exact contest title, background, top-level questions, attachment descriptions, submission requirements, and known constraints.
- Treat only explicitly numbered top-level questions as
ques1, ques2, etc. Do not inflate small details into fake subproblems.
- If local RAG is available, query
cumcm_problems or mcm_icm_problems for similar task wording, attachment patterns, and hidden scoring cues. Record only sourced hits using ../_references/rag_usage_contract.md and ../_references/source_quality_policy.md; core problem facts should come from the current files or S official sources, not unsourced memory.
- Audit each data file for rows, columns, field meaning, units, missing values, abnormal values, duplicate records, encodings, and whether it can support each subproblem.
- Record ambiguities and risks in
WORKFLOW_STATE.md; mark any unreadable or semantically unclear data as HIGH risk.
- If using Codex subagents, run
problem-analyst and data-auditor independently, then summarize their outputs into reports/AGENT_RUNS.md.
PROBLEM_BRIEF.md Structure
# Problem Brief
## Contest And Task
## Source Files
## Top-Level Questions
## Inputs And Outputs
## Constraints And Evaluation
## Dependencies Between Questions
## Ambiguities And Risks
## Next Modeling Needs
## Local RAG Evidence Used
DATA_AUDIT.md Structure
# Data Audit
## File Inventory
## Table And Field Summary
## Missing / Abnormal / Duplicate Data
## Unit And Encoding Issues
## Usable Variables By Question
## Derived Variables Needed
## Blocking Risks
Gate
Write reports/INTAKE_GATE.md with PASS, CONDITIONAL_PASS, or FAIL.
Do not proceed to modeling on FAIL. On CONDITIONAL_PASS, list exactly what the model strategy stage must handle.
