Gaokao Volunteer

高考志愿填报AI助手。基于位次法和线差法，提供分数匹配、 院校推荐、冲稳保方案生成、志愿梯度检查。覆盖全国31省新老高考模式。 Triggers: 填志愿, 高考志愿, 能上什么大学, 志愿填报, 冲稳保, 一分一段, 位次换算, gaokao, gaokao volunteer, 志愿推荐, 查分数线, 院校推荐, 专业推荐, 志愿方案

高考志愿填报技能 (Gaokao Volunteer Filling)

AI-powered college application assistant for Chinese Gaokao. Combines ranking-based matching (位次法), score-difference analysis (线差法), and reach/match/safety classification (冲稳保) to generate personalized college application plans.

When to Use

Activate this skill when the user mentions any of:

• "帮我填志愿" / "高考志愿填报" → full guided workflow
• "XX分能上什么大学" / "能报哪些学校" → quick score matching
• "冲稳保怎么填" / "帮我排志愿梯度" → strategy guidance
• "XX大学XX专业多少分" → single-point lookup
• "检查这份志愿方案" → plan review and gradient analysis
• "XX省高考志愿规则" → province-specific rules
• 查询历年分数线 / 一分一段表 / 位次换算

Core Workflow

Phase 1: Information Collection (multi-turn dialogue)

Collect the following from the user in a structured, conversational way:

1. Province (省份) — REQUIRED. Determines filling rules template.
2. Score (分数) — REQUIRED. Total Gaokao score.
3. Subject Type (科类) — REQUIRED. Physical (物理类) / History (历史类) / Comprehensive (综合). For new Gaokao provinces, also collect selected subjects (选科).
4. Rank (位次) — HIGHLY RECOMMENDED. Provincial ranking from 一分一段表. If not provided, estimate from score using batch line difference.
5. Interests (兴趣方向) — Optional. Preferred major categories (e.g., 计算机, 医学, 金融).
6. Location Preference (城市偏好) — Optional. Preferred cities or regions.
7. School Level (院校层次) — Optional. 985 / 211 / 双一流 / 不限.
8. Batch (批次) — Default to 本科批 unless specified.

If the user provides incomplete info, ask for missing REQUIRED fields. Do NOT proceed to Phase 2 until province + score + subject_type are available.

Phase 2: Data Collection

After gathering user profile, search for relevant data:

2.1 Batch Lines (批次线)

Search for the current year's batch lines for the user's province:

WebSearch: "2026年{省份}高考{科类}批次线 本科线"

Also search for the previous 2 years for comparison:

WebSearch: "2025年{省份}高考{科类}批次线"
WebSearch: "2024年{省份}高考{科类}批次线"

2.2 Ranking Data (一分一段表)

If the user has a score but no rank:

WebSearch: "2026年{省份}高考一分一段表 {科类} {分数}"

Extract the corresponding cumulative rank. Also find equivalent scores for previous years.

2.3 Admission Scores (院校投档线)

Search for universities matching the user's score range:

WebSearch: "2025年{省份}{科类}本科批投档线 {分数范围}"
WebSearch: "2024年{省份}{科类}本科批投档线 {分数范围}"

If the user has specific universities in mind, search those specifically.

Phase 3: Algorithm Processing

Execute the scripts in order:

3.1 Score Delta Calculation

python scripts/score_delta.py --score {score} --batch-line {line} \
  --prev-lines "{2025_line},{2024_line}"

This computes line differences and equivalent scores for previous years.

3.2 Risk Classification

python scripts/risk_classifier.py --rank {rank} \
  --admissions-data references/admission_sample.json \
  --target-count {max_volunteers}

Classifies universities into 冲(Reach) / 稳(Match) / 保(Safety) tiers.

3.3 Ranking Matcher

python scripts/ranking_matcher.py --rank {rank} --province {province} \
  --subject {subject_type} --interests "{interests}"

Matches the user's rank against historical admission data.

3.4 Plan Generation

python scripts/plan_generator.py --profile references/user_profile.json \
  --matches references/matches.json --template assets/report_template.html \
  --output gaokao_plan_2026.html

Generates the final HTML report.

Phase 4: Report Delivery

1. Render the HTML report using report_template.html and the computed data.
2. Open with open_result_view or preview_url for HTML.
3. Offer to deliver_attachments for export/sharing.
4. Provide summary in text: tier counts, top recommendations, risks.

Key reminders in the report:

• Data source date — remind user to verify against official sources
• 冲/稳/保 explanation in plain language
• Disclaimer: AI-generated recommendation, final decision belongs to user
• Common risks: 退档, 滑档, 调剂

Province Rules Quick Reference

Load references/province_rules.json for the full rules. Key differences:

Province	Model	Max Volunteers	Parallel?	Notes
湖北, 湖南, 广东, 江苏	院校专业组	45	Yes	组内调剂
山东	专业+院校	96	Yes	无调剂
浙江	专业+院校	80	Yes	无调剂
河北, 辽宁, 重庆	专业+院校	96/112	Yes	无调剂
四川	院校+专业	9	Yes	传统模式
河南	院校+专业	12	Yes	传统模式

Always check references/province_rules.json before generating plans for a specific province.

Important Notes

• Data freshness: Gaokao data changes yearly. Always WebSearch for current-year data first. Use the scripts only after collecting current data.
• User privacy: Do NOT store user scores or ranks permanently. Process in-memory only.
• Disclaimer: Always include a disclaimer that this is AI-assisted reference only. The user bears full responsibility for final decisions.
• Fallback: If WebSearch fails or data is unavailable, guide the user to manually input data from official sources (各省教育考试院官网).
• File paths: All scripts use absolute paths. Construct paths dynamically using the skill directory: skill_dir = C:\Users\PC\.workbuddy\skills\gaokao-volunteer\