Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ssmurfgg04-gif/context-m --skill gaokao-fetch-volunteersgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ssmurfgg04-gif/context-m/gaokao-fetch-volunteers)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/gaokao-fetch-volunteers"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/gaokao-fetch-volunteers/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/gaokao-fetch-volunteers"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/gaokao-fetch-volunteers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.01826 |
| Opus 5 | $0.00036 | $0.00913 |
| Sonnet 5 | $0.00015 | $0.00365 |
| Haiku 4.5 | $0.00007 | $0.00183 |
Grade A, and why
gaokao-fetch-volunteers scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
获取推荐志愿表
本 Skill 是流水线的第二步：读取 student.json，提取并映射考生倾向到 API 选填参数，调用志愿接口，输出 parsed.json。
上下游
- 上游：gaokao-collect-student-info →
student.json - 下游：gaokao-recommend-majors、gaokao-recommend-schools、gaokao-generate-report
环境准备
cd gaokao-fetch-volunteers
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
执行步骤
1. 从辅助信息提取倾向（Agent 必做）
读取 student.json，根据 preference_mapping.md 将考生倾向补全/写入以下字段：
| 字段 | 映射到 API |
|---|---|
preferred_universities |
universitys |
preferred_provinces / preferred_cities |
provinces |
preferred_tags |
tags |
preferred_major_classes |
majorClass |
提取来源：interests、career_direction、preferred_cities、notes 及对话中的院校/专业/层次偏好。
若 Step1 已结构化写入，本步核对并补全；调用 API 前倾向字段不得为空数组（考生无偏好时除外）。
2. 构建 API 请求
python3 scripts/build_api_request.py \
-i output/student.json \
-o output/api_request.json \
--summary output/preference_summary.json
脚本将 preferred_* 转为 API 选填参数；preferred_cities 会自动推导 provinces（见 preference_mapping.md）。
build_api_request.py 会调用 province_config.validate_classify 校验 classify 是否与省份模式匹配。
2.5 调用前参数核对（必做）
在调用 API 前，对照 reference.md 核对 student.json / api_request.json：
| 检查项 | 规则 |
|---|---|
classify |
新疆→文科/理科；3+1+2 省→物理/历史；3+3 省→综合。传错会导致批次列表为空 |
subjects |
3+1+2：完整三科（首选+两门再选，如 物理,化学,生物）；3+3：完整三科；新疆不传；京沪津专科由脚本处理 |
gradeType |
仅北京/上海/天津填本科或专科；其他省必须为 null 或不传 |
score |
新疆必传（无一分一段表，仅 rank 无效） |
batch |
可填泛称「本科批」/「专科批」，由 batch/list 解析为各省具体批次名 |
| 西藏 | 测试环境不支持，应退回采集环节说明 |
批次 batch：用户侧可填泛称 本科批 / 专科批，脚本通过 batch/list 解析为各省具体批次名（如山东 → 普通类一段）。接口失败时使用 reference.md 中的静态兜底表。
3. 调用志愿 API（两阶段：批次 → 志愿列表）
python3 scripts/fetch_volunteers.py \
--config output/api_request.json \
-o output/parsed.json
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/api_request_shandong.json 543 B
- examples/api_request_template.json 398 B
- preference_mapping.md 3.6 KB
- reference.md 6.5 KB
- requirements.txt 31 B
- scripts/build_api_request.py 5.8 KB runs code
- scripts/fetch_volunteers.py 20 KB runs code
- scripts/province_config.py 8.0 KB runs code
- scripts/test_batch_api.py 6.0 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 145 lines · 73 tokens per session scan A 534629d648ad
gaokao-fetch-volunteers is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 73 tokens to every session and 1,826 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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