# Baoyan Program Research

> Use when researching Chinese graduate recommendation admission options across majors, including 推免/保研 summer camps, pre-admission, September rounds, 科学营/优秀大学生活动, universities, CAS institutes, research institutes, local prior-destination evidence, official-link verification, privacy-safe CSV delivery, and broad-coverage school/program scouting.

- Skill: `zouchenzhen/baoyan-program-research` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add zouchenzhen/baoyan-program-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zouchenzhen/baoyan-program-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: zouchenzhen (https://skillmd.com/u/zouchenzhen)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zouchenzhen/baoyan-program-research

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# 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.