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 Lawyeah-Tech/lawyeah --skill appgit clone --depth 1 https://github.com/Lawyeah-Tech/lawyeahWrote 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/lawyeah-tech/lawyeah/app)<a href="https://agentmods.dev/skills/lawyeah-tech/lawyeah/app"><img src="https://agentmods.dev/badge/skills/lawyeah-tech/lawyeah/app/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/lawyeah-tech/lawyeah/app"><img src="https://agentmods.dev/badge/skills/lawyeah-tech/lawyeah/app.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 | $0.00024 | $0.01089 |
| Opus 5.5 | $0.00010 | $0.00436 |
| Sonnet 5.5 | $0.00005 | $0.00218 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
应对App下架通报与整改 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 1mo 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.
What it actually says
应对 App 下架通报与整改
任务目标
形成问题对照、整改措施、复测材料接口。C 端 App 审查只分流。
depends-on 为空。接受用户、律师、机关或其他来源的等价事实、处理记录和证据清单。
工作边界
正触发：被通报、下架、停止功能或要求限期整改。
负触发并输出 触发判定：负触发，未启动本任务：
- 只要 C 端 App 审查商品；
- 回改大众审查清单；
- 只要捆绑或最小必要的事前评估；
- 把计划写成已复测通过；
本任务不给胜诉率或免罚承诺。审查起草本窗搁置。企业合规整案与知识产权只分流。
建立坐标
确认：通报文书；问题清单；对应版本；已改项；复测渠道。
同时掌握处理者与权利人双方秘密或潜在冲突时，先停止策略输出，澄清代表哪一方。
来源和判断状态
编号：S# 单方陈述；D# 材料表面；A# 当前一致／承认；R# 机关或生效文书；C# 制度或合同条款定位；L# 运行时已核验规则；I# 判断；G# 缺口；Q# 义务／风险项；V# 评估版本；E# 告知／同意／报告凭证。
判断只用 现有依据支持、现有依据不支持、依据不足，无法形成结论。不得输出胜诉率、必然免罚、必然合法或唯一处罚金额。未知人数或金额不写零。
律页能力增强
本技能属于律页法律技能家族，并支持 Lawyeah MCP 按需增强。
- 运行环境提供 Lawyeah MCP 时，执行 Agent 根据当前任务按需使用法律法规、法律实务、裁判案例或文书范本检索。
- 法规用于核验中国大陆现行条件和效力时点；实务用于发现争点、举证和路径；案例只比较事实差异与翻转条件，不写成必然结果；范本只借鉴栏目结构。
- 任何结果都要复核法域、效力、时点、地区和与当前事实的差异。不得把已失效《合同法》及其解释当作现行规则。
- MCP 未安装、未启用、未暴露、无结果、结果冲突或暂时不可用时，不拒绝执行、不中断主流程。继续使用本技能内置方法和用户材料完成可处理部分；依赖未核验规则的法律结论、期限或机关路径保持停止或待核。
- 不向用户索取密钥、令牌或完整凭证。MCP 是可选增强，不是用户运行技能的强制依赖。
使用 Lawyeah MCP
宿主可调用时：search_law 核验 App 违法违规收集使用个人信息认定方法及通报实践口径。不虚构案件系统。
工具不可用时继续主体、场景、系统和问题清单；依赖未核验规则的法律结论、期限或机关路径字段停止。
五个判断节点
执行顺序和停止条件以 App 下架整改方法 为准。这里只列入口，不代替节点。
节点 1 对照
通报问题逐条对照实际行为，不辩称“行业都这样”。
节点 2 整改
措施必须可核验。计划与已完成分列。
节点 3 复测材料
版本号、截图、关闭路径。不写成已通过。
节点 4 根因
SDK、捆绑、超收分别打开相应原子。
节点 5 C 端分账
不回改 C 端商品。
停止与安全
整体停止：伪造复测材料；回改 C 端商品。字段停止：通报问题与版本对应不明。目标停止：主目标变为事前评估或 C 端审查。
交付
What ships with it
4 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.
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.
- 1mo ago First seen · 88 lines · 24 tokens per session scan A 9c1a910c8eee
应对App下架通报与整改 is a skill published in the GitHub repository Lawyeah-Tech/lawyeah (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 1,089 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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