Rival Search MCP
damionrashford/RivalSearchMCP
Deterministic deep research via RivalSearchMCP. An agent skill from damionrashford/RivalSearchMCP.
Runs a two-axis deep research method on a product, company, concept or person: its full history over time, compared with peers today, delivered as a typeset PDF report.
SKILL.md written in Chinese; this summary is our English description.
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install KKKKhazix/khazix-skills hv-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/hv-analysis .claude/skills/hv-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .claude/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install KKKKhazix/khazix-skills hv-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/hv-analysis .agents/skills/hv-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .agents/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install KKKKhazix/khazix-skills hv-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/hv-analysis .cursor/skills/hv-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .cursor/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/KKKKhazix/khazix-skills.git --path hv-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install KKKKhazix/khazix-skills hv-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/hv-analysis .gemini/skills/hv-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .gemini/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install KKKKhazix/khazix-skills hv-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/hv-analysis .github/skills/hv-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .github/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install KKKKhazix/khazix-skills hv-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/KKKKhazix/khazix-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/hv-analysis .opencode/skills/hv-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hv-analysis" agent skill from https://github.com/KKKKhazix/khazix-skills/tree/main/hv-analysis into .opencode/skills/hv-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hv-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hv-analysisRuns a two-axis deep research method on a product, company, concept or person: its full history over time, compared with peers today, delivered as a typeset PDF report.
Written in Chinese, this skill runs a deep research method called horizontal-vertical analysis, proposed by Khazix, on a product, company, concept, technology or person. The vertical axis follows the subject's whole history from origin to now, told as a story; the horizontal axis compares it with competitors or peers at the present moment; crossing the two yields the insights. The result is a typeset PDF research report, made from Markdown with the bundled `scripts/md_to_pdf.py`, which uses WeasyPrint.
The agent confirms the research subject, type, motive and any special focus, and skips questions when the request is already clear. Information gathering must use the web: sub-agents search in parallel (one on the vertical story, one on competitors and reception, a third for complex subjects), prefer primary sources over reposts, and check arXiv for academic topics. A sufficiency check asks whether the history has gaps, whether the competitor list is complete and whether key facts rest on more than one source. It is not for plain definitions, WeChat article writing or title generation.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 322346d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pippythoncurlFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
export.arxiv.orgarxiv.orgFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Horizontal-Vertical Deep Research loads about 2.1k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 133 tokens; SKILL.md has 271 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from KKKKhazix/khazix-skills at commit 322346d, republished under its MIT licence (© KKKKhazix). 271 words, ~2,102 tokens.
.claude/skills/hv-analysis/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.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。
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。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:
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。
使用子Agent并行搜索来提高效率。建议的分工:
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。
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 |
搜索完成后检查:
信息不够就再补搜。不要凑合。
沿时间轴,完整还原研究对象从诞生到现在的发展全貌。这是报告的主体部分,篇幅应该最重。
起源追溯:它诞生的背景是什么?基于什么技术/理念/需求而来?创始团队或核心推动者是谁?这些人之前做过什么,为什么是他们来做这件事?当时的行业环境是什么样的?有没有某个关键事件或灵感直接促成了它的诞生?
诞生节点:明确的首次发布/成立/提出时间,最初的形态和定位,跟现在有什么不同。
演进历程:从诞生到现在,按时间顺序梳理所有关键节点。包括但不限于:重大版本更新、融资事件、团队变动、战略转型、技术架构变化、用户规模里程碑、重大合作或收购、公关危机或争议事件。
决策逻辑:在每个关键节点上,尽可能还原决策背后的原因。为什么选了A而不是B?当时面对的约束条件是什么?哪些早期决策"锁定"了后来的发展方向、难以逆转?什么机制让它越走越深(网络效应、生态绑定、技术栈选择等)?
阶段划分:把整个历程自然分为几个阶段(萌芽期、快速增长期、转型期等),每个阶段有核心特征和核心矛盾。
6000-15000字。历史越长、节点越多的对象靠近上限,新生事物靠近下限。核心原则是把故事讲完整、讲透,每个关键节点都值得展开,不要为了压缩而跳过重要细节。宁可写长写细,也不要蜻蜓点水。
以当前时间点为切面,将研究对象与同赛道的竞品/同类进行全面对比。
分三种场景处理:
场景A:无直接竞品。 如果研究对象是全新品类或独占性极强的领域,跳过逐一对比,改为分析:它为什么没有竞品?是品类太新、壁垒太高、还是市场太小?未来最可能从哪个方向冒出竞争者?有没有间接替代方案或上一代解决方式可以参照?
场景B:少量竞品(1-2个)。 逐一深入对比,每个竞品展开详细分析。
场景C:竞品充分(3个及以上)。 选取最具代表性的3-5个进行对比,其余简要提及。
根据研究对象的类型灵活调整,但至少覆盖以下方面:
核心差异对比:技术路线/核心方法论/底层逻辑、产品形态/商业模式/组织结构、目标用户/受众/适用场景、核心优势与明显短板、定价策略/资源投入/规模体量。
用户视角:每个竞品的真实用户口碑如何?社区评价、使用体验中被提及最多的优点和槽点分别是什么?用户实际的使用方式和官方定位有没有偏差?对比不要写成参数对照表的文字版,要讲清楚每个竞品「活成了什么样」,用户选它的真实理由是什么。
生态位分析:在整个赛道的版图中,研究对象占据什么位置?填补了什么空白,还是在跟谁正面竞争?当前格局是百花齐放、两强争霸、还是一家独大?
趋势判断:基于横向对比,研究对象在竞争格局中的走向是什么?机会和风险各是什么?
3000-10000字。场景A控制在3000字左右,场景C每个主要竞品至少展开1500字以上的独立分析,不要一笔带过。
这是整篇报告的精华段。把纵向发展脉络和横向竞争格局结合起来,给出综合性的、新的判断。不要写成前面内容的缩写版。
需要回答的核心问题:
1500-3000字。
这不是一份冷冰冰的咨询报告,而是一篇让人能从头读到尾的深度研究。写作风格需要在「研究报告的严谨」和「卡兹克的可读性」之间找到平衡点。
以下风格元素直接应用到报告写作中(详细定义请参考 khazix-writer skill):
节奏感:句子时长时短,段落之间跳跃自然。不要每段都一样长,一句话自成一段制造重量感的技巧可以用。好的节奏像波动,每次围绕主线偏出去一点,再用一句「扣主线句」拉回来。
叙事驱动,不是罗列驱动:纵向部分要有故事弧线,有起承转合。比如一个产品为什么在某个时间点突然爆发,背后的铺垫是什么,转折是什么。不要写成"2023年1月发布了A,2023年3月发布了B"这种流水账。
知识是「聊着聊着顺手掏出来」的:在讲述过程中自然地带出背景知识,不要「下面我来给大家科普一下」。
敢下判断:鼓励给出观点和洞察,但每个观点必须有事实支撑。先摆事实,再给判断。是推测的明确标注。表达判断时用「我觉得」「我的判断是」这种承认主观性的姿态,而不是居高临下的定论。
层层剥开的修辞:不直接讲结论,用"现象→表面解释→更深的追问→核心洞察"的方式展开。让读者参与到思考过程中。
文化升维:在交汇洞察部分,连接到更大的文化/哲学/历史参照物。不是硬凑的升华,是「聊着聊着自然想到了」的感觉。
回环呼应:开头或纵向部分埋的细节和钩子,在交汇洞察或结尾callback回来。前后因果的闭合感,是让报告从「信息流」变成「作品」的关键。
以下元素适合公众号文章但不适合研究报告,需要克制:
以下AI味标记无论什么文体都要避免:
避免咨询公司式的套话和空洞概括。用具体的细节和例子代替概括性陈述。比如不要写「该公司在这一阶段实现了快速增长」,而要写「从2024年中期的1000万美元ARR到2025年底的10亿美元,增长曲线几乎是垂直的」。
报告写完后,使用本Skill自带的 scripts/md_to_pdf.py 脚本将Markdown转为排版精美的PDF。
[研究对象]_横纵分析报告.mdpip install weasyprint markdown --break-system-packagespython [skill目录]/scripts/md_to_pdf.py input.md output.pdf --title "研究对象名称" --author "数字生命卡兹克"md_to_pdf.py 已内置完整的CSS排版方案,无需手动调整:
"Droid Sans Fallback", Helvetica, Arial, sans-serif,自动处理中英文混排为了让脚本正确解析并生成最佳PDF效果:
# 标题 作为报告标题(会自动用于封面)> 研究时间:... | 所属领域:... | 研究对象类型:... 格式写元信息行,会被提取到封面## 作为主要章节标题(纵向分析、横向分析、横纵交汇等)### 和 #### 作为子章节> 语法**文本**在Markdown稿件末尾加上:
封面页
目录
一、一句话定义
[用一句话说清楚这个东西是什么]
二、纵向分析:从诞生到当下
[完整的纵向叙事,6000-15000字]
三、横向分析:竞争图谱
[横向对比分析,3000-10000字]
四、横纵交汇洞察
[交叉分析和未来推演,1500-3000字]
五、信息来源
[所有引用的来源列表]PDF文件命名为 [研究对象名称]_横纵分析报告.pdf,保存到用户的工作目录中。
核心原则不变(纵向追时间深度,横向追同期广度),但侧重点不同:
研究产品时:纵轴重点关注版本迭代、技术路线演变、用户增长曲线、关键产品决策;横轴重点关注功能对比、性能对比、用户体验、定价。
研究公司时:纵轴重点关注创始团队、融资历程、战略转向、组织变革、关键人事变动;横轴重点关注商业模式差异、市场份额、营收对比、组织架构差异。
研究概念时(技术范式、商业模式、文化现象):纵轴重点关注概念的起源(谁提出的、基于什么理论/需求)、如何流行起来、经历了哪些争论和演变;横轴重点关注与相近概念的区别、各自适用场景、不同阵营的论证。
研究人物时:纵轴重点关注个人经历、职业轨迹、关键决策、成长曲线、公开言论变化;横轴重点关注与同领域其他人物的对比(做事方式、风格、成就、影响力、路线选择差异)。
| 部分 | 字数范围 | 说明 |
|---|---|---|
| 纵向分析 | 6,000 - 15,000字 | 报告主体,不要蜻蜓点水 |
| 横向分析 | 3,000 - 10,000字 | 视竞品数量调整 |
| 横纵交汇 | 1,500 - 3,000字 | 精华段,给出新判断 |
| 全文总计 | 10,000 - 30,000字 | 不要怕长,深度和完整度是价值所在 |
交付前自检:
© KKKKhazix, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (scripts, references) in hv-analysis of KKKKhazix/khazix-skills.
Open the folder on GitHubat commit 322346d
Horizontal-Vertical Deep Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Horizontal-Vertical Deep Research this skillKKKKhazix/khazix-skills | 21k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Rival Search MCPdamionrashford/RivalSearchMCP | 132 | — | ~796 | Automated safety check: Pass | MIT | |
| Report Writingvstorm-co/pydantic-deepagents | 1.1k | — | ~486 | Automated safety check: Pass | MIT | |
| Verify Citationssickn33/agentic-awesome-skills | 47k | 1 repos | ~1k | Automated safety check: Pass | Apache-2.0 | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Scientific Writingneflibata-feng/MyArxiv-Agent | 126 | 18 repos | ~8.4k | Automated safety check: Notes | MIT |
damionrashford/RivalSearchMCP
Deterministic deep research via RivalSearchMCP. An agent skill from damionrashford/RivalSearchMCP.
vstorm-co/pydantic-deepagents
Guidelines for writing well-structured, cited research reports
sickn33/agentic-awesome-skills
Verify citations and references in a document, report, or article against real sources.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
neflibata-feng/MyArxiv-Agent
Core skill for the deep research and writing tool. An agent skill from neflibata-feng/MyArxiv-Agent.
guhaohao0991/PaperClaw
Generate a specialized domain-expert research agent modeled on PaperClaw architecture.
KKKKhazix/khazix-skills
Scans a Mac or Windows PC read-only to find what fills the disk, grades each item by how safe it is to clean and builds an interactive HTML report.
KKKKhazix/khazix-skills
Brings project docs, agent rule files, authorized memory and leftover workspace files back in line with what the code and runtime actually do at the end of a work session.
KKKKhazix/khazix-skills
Writes long-form WeChat official account articles in the personal style of the Khazix account, from briefs, links, PDFs or rough notes, with a topic quality check.
KKKKhazix/khazix-skills
Answers questions about recent AI news by querying AIHOT's Chinese-language agent API live, covering daily highlights, search, hot events and the daily digest.
KKKKhazix/khazix-skills
Turns a one-line idea into a task brief of up to 4000 characters that an autonomous agent can run through /goal, with measured baselines and cheat-resistant acceptance checks.
Works with
Categories
Runs a two-axis deep research method on a product, company, concept or person: its full history over time, compared with peers today, delivered as a typeset PDF report. Written in Chinese, this skill runs a deep research method called horizontal-vertical analysis, proposed by Khazix, on a product, company, concept, technology or person. The vertical axis follows the subject's whole history from origin to now, told as a story; the horizontal axis compares it with competitors or peers at the present moment; crossing the two yields the insights.
Horizontal-Vertical Deep Research fits situations like: researching a product, company or technology end to end, with competitors; producing a PDF research report with history and comparison sections; understanding who a person or concept is and how it got here.
Run `npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a claude-code`. Or copy the skill folder (hv-analysis in KKKKhazix/khazix-skills) into .claude/skills/hv-analysis in your project. Claude Code loads it when a task matches its description.
Run `npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a codex`. Or copy the skill folder (hv-analysis in KKKKhazix/khazix-skills) into .agents/skills/hv-analysis in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add KKKKhazix/khazix-skills --skill hv-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hv-analysis, .gemini/skills/hv-analysis, .github/skills/hv-analysis and .opencode/skills/hv-analysis in your project.
Going by SKILL.md and its folder, Horizontal-Vertical Deep Research needs Python for the scripts in its folder and the command-line tools its instructions call (pip, python and curl). Our summary lists: Web search and fetch tools; Python with WeasyPrint and markdown, for PDF export.
SKILL.md names 2 domains. In commands or code: export.arxiv.org and arxiv.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Horizontal-Vertical Deep Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Horizontal-Vertical Deep Research: Rival Search MCP (damionrashford/RivalSearchMCP, 132 stars), Report Writing (vstorm-co/pydantic-deepagents, 1.1k stars), Verify Citations (sickn33/agentic-awesome-skills, 47k stars) and GitHub Deep Research (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
KKKKhazix (a GitHub user) maintains it in KKKKhazix/khazix-skills, which has 21,265 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on October 1, 2026.
Source: KKKKhazix/khazix-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.