baoyan-program-research

# Baoyan Program Research Use this skill to investigate 推免/保研 opportunities for any major. The output should be an evidence-backed, privacy-safe candidate list, usually a CSV, not a prose-only recommendation. ## Core Rules - Treat official notices as the source of truth. Aggregators, forums, WeChat reposts, and prior spreadsheets are leads only. - Do not invent dates, quotas, employment outcomes, salary, or eligibility gates. Use `待核验` / `官网未公开` when evidence is missing. - Search all common names: `夏令营`, `暑期学校`, `科学营`, `优秀大学生`, `优材生`, `预推免`, `预选拔`, `推免复试`, `推荐免试`, `九推`, `接收推免生`. - Build a broad pool first, then rank it. Include strong universities, relevant colleges, CAS or other research institutes, industry institutes with master's authority, and practical employment-oriented options. - Preserve privacy. Never include personal phone numbers, email addresses, ID numbers, student IDs, full names from prior-destination files, private local paths, or raw resumes in a public artifact. - Separate three labels in the CSV: official facts, inferred fit, and local historical-success evidence. ## Inputs To Collect Ask for or infer, when available: - Candidate profile: school tier, major, rank/GPA, English scores, research/project keywords, awards, desired degree type, city preference, employment vs PhD orientation. - Local files: resume, transcript summary, award list, prior-destination spreadsheets, old application lists. - Target policy: broad sea application vs curated list, master's only vs master/PhD, employment-oriented vs academic. - Output path and format. Default to CSV plus a short Markdown summary. ## Workflow 1. **Inventory and redact inputs** - List local files first; inspect size before reading. - Extract only decision-relevant facts from resumes. - Do not copy personal contact details into working notes or public repos. 2. **Profile fit model** - Derive discipline keywords in Chinese and English. - Identify adjacent departments that can admit the major, such as chemistry for polymers, EE for electronic materials, public health for biomedical engineering, or management science for data/business majors. - Record hard gates: rank cutoff, English cutoff, required recommendation letters, direct-PhD-only notices, master's-friendly notices. 3. **Coverage plan** - For broad tasks, explicitly include national coverage checks: 985, strong 211/double-first-class programs, top discipline-specific schools, CAS/institute systems when relevant, and local historical-success destinations. - For employment-oriented candidates, score city and industry resources separately from academic prestige. - For PhD-averse candidates, mark direct-PhD-heavy programs as risk even when the school is strong. 4. **Search and verification** - Use current-year official pages first. If current-year pages are not published, use the latest three years as timing references. - Search each target with multiple notice names and department names. - Preserve original URLs and exact dates where available. - If a page is image/PDF-heavy, extract enough text to verify dates and eligibility, then mark any uncertain fields. 5. **Prior-destination evidence** - Aggregate local prior-destination files by destination school/institute and college/department. - Export only counts and summaries, never student names. - Match universities by exact school name. Match institutes by specific institute tokens. Do not match on generic labels like `材料学院`, `计算机学院`, or `化学学院` alone. - Treat prior success as a priority booster, not proof of admission probability. 6. **CSV delivery** - Use the schema in `references/output_schema.md`. - Include official status, dates, links, source quality, local prior-success count, fit notes, risks, and action recommendation. - Add a coverage summary: row count, 985 coverage if applicable, institute count if applicable, current-year notices found, and prior-success hit count. 7. **Quality gate** - Run `scripts/validate_candidate_csv.py` on generated CSVs when possible. - Run `scripts/check_privacy.py` before publishing or sharing any repo/artifact. - Re-open the CSV summary with a limited preview to catch encoding, delimiter, and field-shift issues. ## When To Load References - Need exact CSV columns: read `references/output_schema.md`. - Need search query patterns and source ranking: read `references/search_strategy.md`. - Need privacy handling rules: read `references/privacy_and_safety.md`. - Need local prior-destination aggregation guidance: read `references/prior_destination_matching.md`. ## Helper Scripts - `scripts/aggregate_prior_destinations.py`: aggregate `.xlsx`, `.xls`, and `.csv` prior-destination files into privacy-safe counts. - `scripts/validate_candidate_csv.py`: check required CSV columns and obvious source/date issues. - `scripts/check_privacy.py`: scan files for common PII, local paths, tokens, and private source names before release. ## Final Response Pattern Report: - output file path, - coverage numbers, - current-year urgent notices, - strongest local prior-success destinations, - unresolved verification gaps, - privacy status. Keep direct recommendations short and grounded in the CSV fields.