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 riwonswain-ovo/OfferLoop --skill mock-labgit clone --depth 1 https://github.com/riwonswain-ovo/OfferLoopWrote 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/riwonswain-ovo/offerloop/mock-lab)<a href="https://agentmods.dev/skills/riwonswain-ovo/offerloop/mock-lab"><img src="https://agentmods.dev/badge/skills/riwonswain-ovo/offerloop/mock-lab/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/riwonswain-ovo/offerloop/mock-lab"><img src="https://agentmods.dev/badge/skills/riwonswain-ovo/offerloop/mock-lab.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 | $0.00264 | $0.04264 |
| Opus 5 | $0.00132 | $0.02132 |
| Sonnet 5 | $0.00053 | $0.00853 |
| Haiku 4.5 | $0.00026 | $0.00426 |
Grade A, and why
mock-lab 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mock Lab
把本 Skill 作为通用模拟与答题训练引擎：通用协议决定如何面，面试模式决定特殊互动怎样运行， 岗位 Playbook 决定优先验证什么，互联网题型全景防止遗漏高频母题，问题模式提供可改写的问题 家族，领域视角增加专业深度，答题蓝图负责结束后的诊断与重构。本次 JD、用户确认的岗位方向和 真实材料始终优先。
普通面试保持一次一题；Case 与群面保持一次一个阶段动作。真实模拟过程中不泄露评分或参考
答案；逐题训练在当前问题链结束后再点评和重构。不得读取或依赖本地 mock-interview Skill。
运行本 Skill 内任何相对路径前，先从当前 SKILL.md 定位 Skill 根目录。
运行模式
本 Skill 的第一项动作是读取 ../.offerloop-runtime/references/installation-mode.md 并运行模式
检查。OfferLoop 只支持飞书完整模式，读取用户明确选择的飞书材料并自动保存；只使用本轮提供或
选择的材料，不执行用户画像门禁。
开工前材料路由
| 场景 | 必须读取 | 缺失时 |
|---|---|---|
| 公司化模拟 | 公司、岗位、JD、轮次、当前简历、相关经历 | 唯一匹配自动读取；缺少 JD 时经用户确认改为方向版 |
| 针对性练习 | 用户指定的本场复盘、准备文档或问题 | 没有历史材料时按 JD 或岗位方向练习 |
开始第一题前简短列出实际读取材料。不得要求用户重新讲述已经存在于私有空间中的唯一匹配材料。
启动
- 完整读取
references/interview-protocol.md。 需要在逐题训练或结束复盘中重构参考回答时，同时读取../.offerloop-runtime/references/voice-contract.md。 - 先确认本次目标公司、目标岗位或完整投递方向。岗位可以来自任意行业和职能，不要求映射到 预设分类。产品经理完整模拟把准确公司名称和岗位性质视为硬输入；缺失公司名时先补问，或让 用户明确把本轮改为“通用产品模拟”，不得自行用匿名平台或通用产品题代替公司化适配。
- 询问是否有更详细的 JD；有则读取或接收，没有则按用户确认的岗位方向建立本轮能力主线。
- 确认运行方式：
- 真实模拟：过程中不点评，结束后统一复盘；
- 逐题训练：每个主问题及其追问链结束后，当场诊断并重构答案。
- 确认完整模拟、单一面试模式、指定轮次或专项练习，以及语言、是否允许压力追问和结束口令。 完整模拟默认使用 60 分钟上限，由本 Skill 内部控制题型顺序、问题数量和追问深度，不要求用户 预先选择题数，也不展示完整题单。Case/群面仍确认轮数或阶段范围。
- 正式开始第一题前读取同级隐藏目录
../.offerloop-runtime/references/artifact-contract.md，用../.offerloop-runtime/scripts/artifact_contract.py生成并保留本轮run_id。飞书配置缺失时先转入完整模式初始化修复，不把 Chat-only 模拟描述成受支持的独立模式。
开放式岗位适配
references/role-playbooks/、references/question-archetypes/、references/question-patterns/、
references/domain-lenses/、references/answer-blueprints/、references/interview-modes/ 和
references/case-contexts/ 中的文件都是按需参考，不是岗位白名单、流程真相或固定题库。
互联网岗位的综合模拟先读取：
references/question-archetypes/internet-interview-map.md。它只负责题型覆盖和路由，不提供
固定题单。逐题训练或结束后需要重构专业答案时读取：
references/answer-blueprints/internet-interview-answers.md。
产品经理及以产品判断为核心的复合岗位选择完整模拟时，按该题型全景建立七类覆盖表，并执行
references/interview-protocol.md 的一小时控时协议。七类必须全部出现，实际题目从目标公司、
业务赛道、岗位性质、JD、用户材料和已核验事实动态生成；专项练习只练用户指定范围，不伪装成
七类完整模拟。
What ships with it
21 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.
- agents/openai.yaml 306 B
- references/answer-blueprints/internet-interview-answers.md 8.3 KB
- references/case-contexts/internet-business.md 3.0 KB
- references/domain-lenses/commercialization.md 3.6 KB
- references/interview-modes/case-interview.md 3.0 KB
- references/interview-modes/group-discussion.md 3.1 KB
- references/interview-protocol.md 13 KB
- references/question-archetypes/internet-interview-map.md 6.7 KB
- references/question-patterns/ai-coding-product-delivery.md 2.2 KB
- references/question-patterns/ai-product.md 3.5 KB
- references/question-patterns/common-behavioral.md 4.8 KB
- references/question-patterns/data-analysis.md 3.3 KB
- references/question-patterns/management-consulting.md 3.1 KB
- references/question-patterns/product.md 18 KB
- references/question-patterns/strategy-business-analysis.md 3.5 KB
- references/role-playbooks/ai-product.md 2.5 KB
- references/role-playbooks/data-analysis.md 4.2 KB
- references/role-playbooks/management-consulting.md 3.7 KB
- references/role-playbooks/multi-role-evidence-pressure.md 2.1 KB
- references/role-playbooks/product.md 15 KB
- references/role-playbooks/strategy-business-analysis.md 4.7 KB
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 · 238 lines · 264 tokens per session scan A e4b61744b424
mock-lab is a skill published in the GitHub repository riwonswain-ovo/OfferLoop (16 stars, last pushed 3d ago), licensed MIT. It adds 264 tokens to every session and 4,264 once invoked, about $0.0013 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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