Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/yushui2022/MathModel-Skillnpx agentmods add skills/yushui2022/mathmodel-skill/authoritative-data-harvesterWrote 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/yushui2022/mathmodel-skill/authoritative-data-harvester)<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/authoritative-data-harvester"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/authoritative-data-harvester/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/yushui2022/mathmodel-skill/authoritative-data-harvester"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/authoritative-data-harvester.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.02165 |
| Opus 5 | $0.00028 | $0.01082 |
| Sonnet 5 | $0.00011 | $0.00433 |
| Haiku 4.5 | $0.00006 | $0.00216 |
Grade A, and why
authoritative-data-harvester 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
权威数据自动获取（Authoritative Data Harvester）
全局流程协作约束（长对话防漂移）
- 本 skill 不得作为孤立入口。用户要求完整论文、生成 Word、继续流程或不确定阶段时，先回到
paper-workflow-orchestrator判断当前 S0-S8 阶段。 - 启动或继续本 skill 的正式任务前，必须运行：
python .claude/skills/paper-workflow-orchestrator/scripts/workflow_guard.py --skill authoritative-data-harvester - 如果输出
[WORKFLOW FAIL]或报告status != "PASS"，停止本 skill，按paper_output/qa/workflow_guard_report.json的失败项回补前置阶段，不得凭记忆继续。 - 本 skill 只写入自己契约范围内的
paper_output/产物；完成后必须回到paper-workflow-orchestrator判断下一步，并用context-memory-keeper记录已完成产物、阻塞项和下一步。 - 长对话中如果上下文变长、阶段不确定或用户分开调用 skill，先运行：
再读取python .claude/skills/paper-workflow-orchestrator/scripts/workflow_guard.py --statuspaper_output/qa/workflow_guard_report.json、paper_output/preflight_report.json、paper_output/input_manifest.json、paper_output/results/run_manifest.json和本 skill 的上游 JSON 契约，按报告里的recommended_skill与next_action继续。 - 继续流程前，必须把
paper_output/context/workflow_memory.json视为长期断点记录；若其中的current_step、next_step、recommended_skill与workflow_guard.py --status不一致，以 guard 报告为准。 - 每次完成本 skill 的产物后，先回到
paper-workflow-orchestrator或运行workflow_guard.py --status，再更新 workflow memory：
更新后读取python .claude/skills/context-memory-keeper/scripts/update_workflow_memory.pypaper_output/context/workflow_memory.json/.md，确认下一步和推荐 skill 已记录。
执行契约
- 上游输入：用户给出的变量需求，或
paper_output/step1/problem_analysis.json、paper_output/plan/model_route.json中识别出的外部数据需求。 - 必须输出：可复现的数据源说明、抓取或下载方案，并将原始/处理后数据与来源信息保存到
crawled_data/，优先包含crawled_data/sources.json。 - 下游交接：
data-cleaning-and-visualization读取crawled_data/做统一清洗、图表计划和论文级配图。 - 推荐下一步：数据落盘后进入
data-cleaning-and-visualization；完整论文目标应回到paper-workflow-orchestrator判断后续阶段。 - 失败回退：若无法自动获取，应给出同级权威替代源、口径差异和人工下载路径；不得使用无来源或不可引用的数据冒充权威数据。
目标
在数学建模任务中，快速找到“权威、可引用、可复现”的公开数据源，并以尽量不爬网页、优先 API/批量下载的方式获取数据，最终输出：
- 数据获取脚本/方案（含链接、参数、时间范围、字段解释）
- 原始数据与清洗后的数据（CSV/Parquet）
- 数据字典与引用信息（来源、更新时间、许可证/条款、访问日期）
What ships with it
1 file 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.
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 · 130 lines · 56 tokens per session scan A 423d5c4aeedc
authoritative-data-harvester is a skill published in the GitHub repository yushui2022/MathModel-Skill (384 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 2,165 once invoked, about $0.0003 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 2026-08-30.
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