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 agentscope-ai/QwenPaw-Data --skill skill-hub-guidegit clone --depth 1 https://github.com/agentscope-ai/QwenPaw-DataWrote 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/agentscope-ai/qwenpaw-data/skill-hub-guide)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/skill-hub-guide"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/skill-hub-guide/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/agentscope-ai/qwenpaw-data/skill-hub-guide"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/skill-hub-guide.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 | $0.00039 | $0.02865 |
| Opus 5 | $0.00019 | $0.01432 |
| Sonnet 5 | $0.00008 | $0.00573 |
| Haiku 4.5 | $0.00004 | $0.00286 |
Grade A, and why
skill-hub-guide 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 10d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
skill-hub-guide
Data Skill-Hub 是由资深数据分析专家团队设计、经过大量真实业务场景验证的通用技能仓库。每个 SKILL 都凝结了专家级分析方法论和工程最佳实践，覆盖了从意图识别、计划生成、取数、计算到报告产出的完整数据分析链路。
严格遵循本仓库中的 SKILL 定义，你将获得专家级的分析质量——逻辑严谨、结论有据、产物规范。偏离 SKILL 定义则会导致分析遗漏、结论失准或产物不可用。
你的所有数据分析行为必须由 SKILL 驱动，严格遵循各 SKILL.md 中定义的执行步骤和规则，不得自行发挥。
核心原则
- SKILL 即规范：每个 SKILL.md 由领域专家编写，定义了该能力唯一正确的执行流程。即使你认为可以简化，也必须完整遵循
- 脚本优先：有脚本（scripts/）必须调脚本，不可用时才降级为自行实现
- 参数优先级：用户显式指定 > 域知识包 > 语义层接口 > SKILL 默认值
- 按需调用：禁止一次性预读所有 SKILL，根据当前执行到的子任务和步骤逐个加载对应 SKILL
技能体系
分层架构
技能按所在目录划分为 6 层。下表中「目录」一栏即 skills/ 下的物理目录，新增 SKILL 时按其能力归属放入对应目录，并在本表登记。
| 层级 | 目录 | 职责 | SKILL |
|---|---|---|---|
| L0 路由 | skills/routing/ |
判定用户意图属于哪种任务类型（查询/分析/建模/报告/非数据），将请求分发至对应处理链路 | data-intent-router |
| L1 规划 | skills/planning/ |
补充上下文、匹配分析模块和指标，将用户需求转化为结构化的可执行分析计划 | analysis-plan-builder |
| Workflows | skills/workflows/ |
按分析计划编排原子技能与取数能力，完成从数据获取到结论汇总的完整分析流程 | fetch-data（取数）bi-metric-analysis（指标观测与异常归因）bi-retention-analysis（留存率分析）bi-conversion-analysis（转化率分析）bi-cohort-analysis（同期群分析） |
| Atomic | skills/atomic/ |
执行单一、独立的分析动作（计算、检测、下拆、归因、聚类、报告等） | 指标观测与计算：bi-metric-observation、bi-retention-rate、bi-conversion-rate异常检测与阈值：bi-anomaly-detection、bi-adaptive-threshold归因与下钻：bi-dimension-drilldown、bi-attribution-analysis、bi-time-impact-attribution、bi-new-dimension-analysis统计与画像：bi-event-analysis、bi-funnel-analysis、bi-comparison-analysis、bi-distribution-analysis、bi-clustering、bi-ltv-analysis报告输出：bi-report-generation |
| Runtime | skills/runtime/ |
横切面强制规范，约束所有层的执行行为（产物落盘、数据复用、异常处理、语义层查询） | runtime-guide、bi-semantic-layer-guide |
| Meta | skills/meta/ |
仓库使用与协作约定，加载其它任何 SKILL 之前必须先读 | skill-hub-guide（本文件） |
典型调用链路
下面给出几种常见任务类型的链路。Runtime 层（runtime-guide、bi-semantic-layer-guide）在所有链路里全程生效，不再在每条链路中重复展开；fetch-data 在任何需要新取数的链路前置生效。
1. BI 业务分析链路（任务类型 = 2a）
由 data-intent-router 判定为业务分析后，由 analysis-plan-builder 给出结构化计划，再根据计划落到具体的 workflow。
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.
- 10d ago First seen · 195 lines · 39 tokens per session scan A e0daa746e5f9
skill-hub-guide is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (71 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 2,865 once invoked, about $0.0002 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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