H
hypertopos/hypertopos-skills
Business data as geometry that AI agents navigate: investigation workflows for anomaly triage, root cause,…
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置（取自该仓库的安装命令）
{
"mcpServers": {
"hypertopos-hypertopos-skills": {
"command": "npx",
"args": ["skills"]
}
}
}解析自我们于 2026-11-10 抓取的该仓库 README；我们未执行过。
command/args 来自仓库写明的安装命令；不确定时请以该项目 README 为准。
VS Code（.vscode/mcp.json —— 键名是 servers）
{
"servers": {
"hypertopos-hypertopos-skills": {
"command": "npx",
"args": ["skills"]
}
}
}VS Code 顶层键是 <code>servers</code>，其余与上面那份相同。
Claude Code（命令行）
claude mcp add --transport stdio hypertopos-hypertopos-skills -- npx skills
Claude Code 不收 JSON 文件，用这条命令直接加。
配置项的键名以你的客户端为准：客户端与 CLI
- ★ 数取值
- 0 ★ · hypertopos/hypertopos-skills · 取自 GitHub 搜索 API · （2026-11-13 20:00:50 核对）
- 来源明细
- 从该仓库的 GitHub 页面直接读取 · 条目类型：开源项目
- 收录与活跃
- 收录于 2026-11-11 · 最近见到 2026-11-14 · 共见到 4 次 · 被 1 个来源列出
- 评分构成
- 综合分 103 · 1 个来源 · repo
- 安装
npx skills
常见问题（答案都来自我们的记录）
怎么安装？
该仓库给出的安装命令是
npx skills。我们的记录里它是仓库自己提供的；不保证在你的客户端上直接可用。它适合谁 / 用来做什么？
我们把它归在这些标签下: Agent 技能。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
hypertopos/hypertopos-skills；我们收录于 2026-11-11；最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。# hypertopos-skills
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
> Business data as geometry that AI agents navigate — the investigation workflows.
Install the skills into your agent, connect hypertopos-mcp to a sphere, and ask in plain language — the matching skill decides which tool to call, in which order, what the result means, and what to check next.
Judgement, not code. Nine Markdown skills — decision frameworks with worked examples — that an agent reads before it acts. No Python, nothing to run.
Every investigation ends in a report, not a tool dump. The skills carry a quality checklist, a report structure and anti-patterns, so a finding states its evidence, its confidence and its false-positive risk.
They know the tools' edges. Compact responses and the omitted map, sessions pinned to a snapshot, branches for what-if recalibration, and the parameter names a call must use.
Get started (one minute). Needs an agent that reads skills and a connected hypertopos MCP server:
bash
npx skills add hypertopos/hypertopos-skills # Claude Code, Cursor, Codex, Copilot, Amp, Cline, Windsurf, …
# or: copy the gds-/ folders into your agent's skills directory, e.g. .claude/skills/ for Claude Code
In Claude Code, the repository is also a plugin marketplace, so the skills install and update as a plugin:
/plugin marketplace add hypertopos/hypertopos-skills
/plugin install hypertopos-skills@hypertopos-skills
Then, in your AI assistant:
explore this sphere
why is account 85 anomalous?
check sphere health
run an AML scan
That's it. You get nine skills, each a folder with a SKILL.md (and references/ where the skill carries worked examples and detail the SKILL.md links to):
gds-analyst/ drives an investigation: goal, plan, delegation, report
gds-explorer/ orientation in a sphere you have never seen
gds-investigator/ root cause, entity deep-dive, hypothesis testing, false-positive assessment
gds-detective/ detection recipes: event rates, bursts, Simpson's paradox, collective drift
gds-scanner/ cross-pattern, neighbour-contamination, trajectory-shape and segment-shift scans
gds-monitor/ health checks, drift, regime changes, calibration epochs, safe recalibration
gds-fraud-investigator/ AML and fraud: screening, per-suspect investigation, typology recipes
gds-sphere-designer/ from a description of the data to a sphere.yaml, a build, and tuning
gds-spark/ anomaly detection on a Spark DataFrame: roles, find_anomalies, triage, explain, score
Works with Claude Code, Cursor, Codex, GitHub Copilot, Amp, Cline, Windsurf, and other supported agents.
> [!NOTE]
> The skills need hypertopos-mcp connected to a built sphere; they call its tools and read its responses. hypertopos builds the sphere. gds-sphere-designer works before a sphere exists, and gds-spark works in a Spark or Databricks notebook with hypertopos[spark], needing the MCP server only to hand a result to an agent.
## See it in action
A skill is text the agent follows. This is what gds-monitor says about recalibrating when the entities under review may be distorting the baseline (verbatim from its "Safe recalibration via branch" section):
text
# 1. Recalibrate on a branch (optionally excluding known influencers)
recalibrate(pattern_id, branch="my_branch", exclude_keys=["KEY1", "KEY2"])
Install the skills into your agent, connect hypertopos-mcp to a sphere, and ask in plain language — the matching skill decides which tool to call, in which order, what the result means, and what to check next.
Judgement, not code. Nine Markdown skills — decision frameworks with worked examples — that an agent reads before it acts. No Python, nothing to run.
Every investigation ends in a report, not a tool dump. The skills carry a quality checklist, a report structure and anti-patterns, so a finding states its evidence, its confidence and its false-positive risk.
They know the tools' edges. Compact responses and the omitted map, sessions pinned to a snapshot, branches for what-if recalibration, and the parameter names a call must use.
Get started (one minute). Needs an agent that reads skills and a connected hypertopos MCP server:
bash
npx skills add hypertopos/hypertopos-skills # Claude Code, Cursor, Codex, Copilot, Amp, Cline, Windsurf, …
# or: copy the gds-/ folders into your agent's skills directory, e.g. .claude/skills/ for Claude Code
In Claude Code, the repository is also a plugin marketplace, so the skills install and update as a plugin:
/plugin marketplace add hypertopos/hypertopos-skills
/plugin install hypertopos-skills@hypertopos-skills
Then, in your AI assistant:
explore this sphere
why is account 85 anomalous?
check sphere health
run an AML scan
That's it. You get nine skills, each a folder with a SKILL.md (and references/ where the skill carries worked examples and detail the SKILL.md links to):
gds-analyst/ drives an investigation: goal, plan, delegation, report
gds-explorer/ orientation in a sphere you have never seen
gds-investigator/ root cause, entity deep-dive, hypothesis testing, false-positive assessment
gds-detective/ detection recipes: event rates, bursts, Simpson's paradox, collective drift
gds-scanner/ cross-pattern, neighbour-contamination, trajectory-shape and segment-shift scans
gds-monitor/ health checks, drift, regime changes, calibration epochs, safe recalibration
gds-fraud-investigator/ AML and fraud: screening, per-suspect investigation, typology recipes
gds-sphere-designer/ from a description of the data to a sphere.yaml, a build, and tuning
gds-spark/ anomaly detection on a Spark DataFrame: roles, find_anomalies, triage, explain, score
Works with Claude Code, Cursor, Codex, GitHub Copilot, Amp, Cline, Windsurf, and other supported agents.
> [!NOTE]
> The skills need hypertopos-mcp connected to a built sphere; they call its tools and read its responses. hypertopos builds the sphere. gds-sphere-designer works before a sphere exists, and gds-spark works in a Spark or Databricks notebook with hypertopos[spark], needing the MCP server only to hand a result to an agent.
## See it in action
A skill is text the agent follows. This is what gds-monitor says about recalibrating when the entities under review may be distorting the baseline (verbatim from its "Safe recalibration via branch" section):
text
# 1. Recalibrate on a branch (optionally excluding known influencers)
recalibrate(pattern_id, branch="my_branch", exclude_keys=["KEY1", "KEY2"])
收录徽章（可选）
想把收录标记放进 README 就复制这行，可选，不影响收录。
[](https://anclum.com/zh/s/hypertopos-hypertopos-skills/)
- 来源
- github · https://github.com/hypertopos/hypertopos-skills
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