ASu-skills is a plugin package containing eight separate AI-assisted workflows for job seeking and software development. Users can apply them to make resumes, prepare for interviews, track recruitment, learn projects, document evidence, and contribute to open source. The catalogue add-ons are the plugin’s skills, instructions, and setup files for supported coding agents.
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 Hisn00w/ASu-skills --skill interviewgit clone --depth 1 https://github.com/Hisn00w/ASu-skillsWrote 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/hisn00w/asu-skills/interview)<a href="https://agentmods.dev/skills/hisn00w/asu-skills/interview"><img src="https://agentmods.dev/badge/skills/hisn00w/asu-skills/interview.svg" alt="Measured on agentmods" 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 | $0.00101 | $0.01951 |
| Opus 5 | $0.00051 | $0.00975 |
| Sonnet 5 | $0.00020 | $0.00390 |
| Haiku 4.5 | $0.00010 | $0.00195 |
Grade A, and why
interview 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 4d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/interview：把简历问穿
/interview 不替用户编造可以背诵的经历，而是把简历和 JD 中的重要 Claim 变成可验证的问题。它通过面试契约、逐轮追问、证据评估和弱项复练，检查用户能否讲清真实职责、技术实现、指标口径、决策取舍和失败案例。
核心链路：
简历 / JD
↓
Claim 与岗位能力矩阵
↓
面试契约
↓
问题与评分契约
↓
一次一问、动态追问
↓
会话账本
↓
证据复盘与弱项复练
输入与事实边界
优先使用当前对话已有材料，不让用户重复粘贴：
- 简历或项目经历；
- 目标岗位和 JD；
- 项目文档、代码、论文或公开链接；
- 已确认的真实职责、指标和结果；
- 面试轮次、可用时间和反馈偏好。
没有 JD 时仍可根据简历预测；没有简历时，请用户提供简历或至少一段项目经历。只询问会实质改变训练结果的缺失信息，其余使用明确默认值。材料不足时标记 待确认，不补写用户没有提供的经历、数据或技术细节。
问题优先验证真实主张，不批量生成与岗位无关的通用八股。不要把回答、掌握度或面试表现写入公开文件；只有用户明确要求时才把复盘保存到本地文件。
四种模式
Predict：预测问题
当用户要求预测问题或输入 /interview predict 时：
- 提取 Ownership、Metric、Technical、Architecture 和 Result Claim；
- 从 JD 提取岗位能力，建立“能力—简历证据—缺口”矩阵；
- 按相关度、发生概率、表述风险和证据缺口排序；
- 输出高概率题、补充题和压力题；
- 为核心题标明来源、考察意图、应覆盖事实和下一层追问。
不要为每道题直接生成长篇答案。推荐结构：
Q：你在项目中为什么选择这套方案？
来源：简历中的“设计 Agent Runtime”
考察：技术选型、个人决策权、trade-off
回答应覆盖：原问题、候选方案、选择依据、个人负责部分、结果证据
追问：如果流量或上下文规模扩大，最先出现什么瓶颈？
Grill：模拟面试
当用户要求模拟面试、压力面或输入 /interview grill 时：
- 根据已知材料建立面试契约；需要完整会话规则时读取 references/interview-contract-and-session.md；
- 选择最高相关或最高风险 Claim，为当前问题建立锁定的评分契约；
- 每轮只问一个问题，等待回答后再评估和决定下一步；
- 按回答证据选择深挖、澄清、降阶、切换 Claim 或结束；
- 按反馈策略决定立即反馈还是面试结束后统一反馈；
- 更新会话账本，避免重复提问和结论漂移。
提出问题和评估回答前读取 references/question-and-scoring-contract.md。用户要求系统设计、Case、两周 Demo 或陌生业务场景时，再读取 references/scenario-interviews.md。
真实模拟默认不在用户回答前给标准答案。用户卡住时，可以按约定提供一个小提示或回答结构；不要补造可冒充的项目事实。
Review：证据复盘
当用户要求总结、输入 /interview review 或结束模拟面试时，依据会话账本输出复盘：
- 已验证、部分验证、未验证和存在矛盾的 Claim；
- 触发判断的具体回答证据；
- 需要补事实、补知识或降低强度的内容；
- 再追两层最容易暴露的问题；
- 面试前按优先级排列的行动清单。
不使用缺乏校准依据的精确总分。样本不足时明确复盘范围和未覆盖能力。完整规则与模板见 references/review-and-retry.md。
Retry：弱项复练
当用户说“复练”“只练没掌握的题”或输入 /interview retry 时：
- 从最近一次会话账本选择部分验证、未验证或存在矛盾的 Claim；
- 优先问变体题、反事实题或相邻场景题，不机械重复原题；
- 比较本次与上次证据，判断是否真正补齐；
- 已通过的 Claim 退出复练队列，仍薄弱的 Claim 保留具体下一步。
读取 references/review-and-retry.md 执行复练，不凭空假设存在上一轮记录。
What ships with it
5 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.
- 4d ago Changed 5f7554bfbdb8
- 8d ago First seen · 156 lines · 101 tokens per session scan A 01c7e30804bd
interview is a skill published in the GitHub repository Hisn00w/ASu-skills (3,774 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 1,951 once invoked, about $0.0005 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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