Khazix Skills is a collection of structured instruction sets that coding agents can load to perform tasks such as defining goals, analyzing products, cleaning project documentation, researching topics, and writing articles. It is intended for users of Claude Code, Codex, and other agents that support the Agent Skills standard.
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 KKKKhazix/khazix-skills --skill hv-analysisgit clone --depth 1 https://github.com/KKKKhazix/khazix-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/kkkkhazix/khazix-skills/hv-analysis)<a href="https://agentmods.dev/skills/kkkkhazix/khazix-skills/hv-analysis"><img src="https://agentmods.dev/badge/skills/kkkkhazix/khazix-skills/hv-analysis/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/kkkkhazix/khazix-skills/hv-analysis"><img src="https://agentmods.dev/badge/skills/kkkkhazix/khazix-skills/hv-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 55 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00353 | $0.05407 |
| Opus 5 | $0.00177 | $0.02704 |
| Sonnet 5 | $0.00071 | $0.01081 |
| Haiku 4.5 | $0.00035 | $0.00541 |
Grade A, and why
hv-analysis scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> - **学术类研究对象必查arxiv**：如果研究对象涉及学术概念、算法、AI模型、技术范式等，必须通过arxiv API获取相关论文。调用方式：`curl -s "https://export.arxiv.org/api/query?search_query=all:关键词1+AND+all:关键词2&max_results=10"`，或用WebFetch访问同一URL。返回XML格式，包含标题、作者、摘要、发布日期、PDF链接。 How it starts
The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
横纵分析法深度研究
方法论溯源 横纵分析法由数字生命卡兹克（Khazix）提出，融合了语言学中的历时-共时分析（Saussure）、社会科学中的纵向-横截面研究设计、商学院案例研究法、以及竞争战略分析的核心思想，形成了一套适用于产品/公司/概念/人物的通用研究框架。核心原则不变：纵向追时间深度，横向追同期广度，最终交汇出判断。
你正在执行一次横纵分析法深度研究。最终产出一份排版精美的PDF研究报告。
前置准备
环境准备
- 确认PDF转换脚本可用：本Skill自带
scripts/md_to_pdf.py（基于WeasyPrint），用于将最终Markdown报告转为排版精美的PDF。确保依赖已安装：pip install weasyprint markdown --break-system-packages。 - 写作风格：本Skill已内置完整的写作风格指南（见下文"写作风格"部分），无需额外加载其他skill。
明确研究对象
拿到用户输入后，确认以下信息。如果用户已经给得足够明确（比如"帮我用横纵分析法研究Hermes Agent"），不需要追问，直接开始：
- 研究对象：具体的产品名/公司名/概念名/人名
- 类型：产品、公司、概念、人物、还是其他？
- 研究动机（可选）：为什么要研究它？最近发生了什么？
- 特别关注点（可选）：有没有特别想深入的方向？
第一步：联网信息收集
这个方法论的质量完全取决于信息的丰富度和准确性。必须联网搜索，不能仅靠已有知识。研究报告的价值在于深度和完整度，所以信息收集阶段宁可多搜，不要因为信息不够导致后面的分析浮于表面。
并行搜索策略
使用子Agent并行搜索来提高效率。建议的分工：
- 子Agent 1 — 纵向信息：研究对象的起源、创始人背景、发展历程、关键事件、版本迭代、融资、战略转向、危机
- 子Agent 2 — 横向信息：竞品识别、各竞品的特点和用户口碑、行业对比评测、市场份额
- 子Agent 3（复杂对象才需要）：补充信息，如创始人深度背景、行业环境变化、用户社区讨论（GitHub issues、Reddit、Twitter/X、知乎等）
子Agent联网工具使用指南（直接写入每个子Agent的prompt中）：
每个子Agent的prompt中必须包含以下联网指引：
你需要联网获取信息。使用以下工具：
- WebSearch：用于搜索发现信息来源，获取摘要和关键词结果
- WebFetch：当已知具体URL时，用于从页面定向提取内容
- 如果用户环境中安装了 web-access skill（检查路径
/mnt/.claude/skills/web-access/SKILL.md是否存在），优先加载它并遵循其指引，它提供更强的浏览器CDP能力- 搜索策略：先用WebSearch发现信息来源和线索，找到具体URL后用WebFetch深入提取
- 多次搜索、多个关键词组合，不要只搜一次就放弃
- 一手来源优于二手来源：官方博客 > 权威媒体原创报道 > 转载/聚合
- 学术类研究对象必查arxiv：如果研究对象涉及学术概念、算法、AI模型、技术范式等，必须通过arxiv API获取相关论文。调用方式：
curl -s "https://export.arxiv.org/api/query?search_query=all:关键词1+AND+all:关键词2&max_results=10"，或用WebFetch访问同一URL。返回XML格式，包含标题、作者、摘要、发布日期、PDF链接。可按需调整关键词组合和结果数量。找到关键论文后，用WebFetch读取论文页面（https://arxiv.org/abs/论文ID）获取更多细节。
prompt要描述目标（"获取""调研""了解"），不要用暗示具体手段的动词（"搜索""爬取"），让子Agent自主判断最佳获取方式。
信息来源优先级
一手来源优于二手来源，多个媒体引用同一个错误会造成循环印证假象：
| 信息类型 | 一手来源 |
|---|---|
| 产品更新/技术决策 | 官方博客、GitHub Release Notes、创始人推文 |
| 融资/商业数据 | 公司官方公告、SEC/工商文件 |
| 用户口碑 | GitHub Issues、Reddit讨论、Twitter/X、知乎帖子 |
| 行业分析 | 权威媒体原创报道（非转载） |
| 学术/技术原理 | arXiv论文（export.arxiv.org/api/query）、Google Scholar、学术会议论文集 |
What ships with it
2 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.
- 11d ago First seen · 324 lines · 353 tokens per session scan A 898f21b1f7b4
hv-analysis is a skill published in the GitHub repository KKKKhazix/khazix-skills (20,571 stars, last pushed 3d ago), licensed MIT. It adds 353 tokens to every session and 5,407 once invoked, about $0.0018 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Commits, pushes, and remotely verifies authorized repository changes. Not for releases, package publication, or announcements.