New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Bi Distribution Analysis

by agentscope-ai in agentscope-ai/QwenPaw-Data

分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。

Apache-2.0Auto-check passed

Install Bi Distribution Analysis

skills CLI
$ npx skills add agentscope-ai/QwenPaw-Data --skill bi-distribution-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agentscope-ai/QwenPaw-Data bi-distribution-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/atomic/bi-distribution-analysis .claude/skills/bi-distribution-analysis && rm -rf skills-src

Use ~/.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/

Facts

Skill name
bi-distribution-analysis
GitHub stars
127
Token cost
~580 tokens
SKILL.md length
146 words
Files
2 (incl. scripts)
Skills in repo
29
Repo updated
First seen
Licence
Apache-2.0

At a glance

分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。

  • Works in 2 steps: ：检查数据以及确定区间维度与计算指标 → ：计算数据分布特征
  • SKILL.md covers 执行步骤, fallback（指引模式） and 输出要求
  • Runs Python scripts from its folder; calls python

What it does

Bi Distribution Analysis is an agent skill from agentscope-ai/QwenPaw-Data. 分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。

Its SKILL.md is about 580 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/distribution_stats.py`).

The repository describes itself as: Agentic enterprise data analytics: governed facts (DataBridge), reusable methodology (Skill-Hub), and controllable execution (Host). The licence is Apache-2.0.

Example prompts

  • “/bi-distribution-analysis”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. ：检查数据以及确定区间维度与计算指标
  2. ：计算数据分布特征

What it can do on your machine

Read from SKILL.md and the folder at commit e0bae36. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bi Distribution Analysis loads about 580 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 146 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~580

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from agentscope-ai/QwenPaw-Data at commit e0bae36, republished under its Apache-2.0 licence (© agentscope-ai). 146 words, ~580 tokens.

Download SKILL.mdSave it as .claude/skills/bi-distribution-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
bi-distribution-analysis
description
分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。

bi-distribution-analysis

观察数据在不同区间的分布特征，计算均值、标准差、中位数等统计信息，用于刻画集中趋势与离散程度。

执行步骤

Step 0：检查数据以及确定区间维度与计算指标
  1. 明确分析对象：明确区间维度，以及相应具体数据列。例如，分析“不同国家用户数的分布情况”，区间维度为“国家”，具体数据列为“用户数”；
  2. 数据以 CSV 格式保存，且数据中已包含区间维度信息以及关键数据列。如
csv
date,访问用户数,国家
20250101,10000,英国
20250102,10500,法国
20250103,9800,德国
Step 1：计算数据分布特征

对用于分析的数值序列 (x_1,\ldots,x_n)，计算以下数据分布特征：

指标说明
均值(\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i)
标准差(\sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i-\bar{x})^2})
中位数数值序列中位数
top 5 的维度数据值最大的 5 个维度 ｜
top 5 维度各数值占比数值最大的 5 个维度，每个维度对应数值占所有维度数值和的比例
频率分布（按累计占比分桶）将各维度按数值从大到小排序，逐项累计求和并除以总和得到累计占比 (r)，按 (r) 落入以下 7 个桶：<0.5、[0.5, 0.6)、[0.6, 0.7)、[0.7, 0.8)、[0.8, 0.9)、[0.9, 0.95)、>=0.95。每个桶的值为该桶包含的维度名称列表

使用 <skill-dir>/scripts/distribution_stats.py 脚本，计算上述 6 个数值分布特征（数值类指标计算结果保留小数点后 5 位）。

bash
python <skill-dir>/scripts/distribution_stats.py --input_file "<输入数据文件路径 (CSV)>" --value_col "<数值列名>" --dimension_col "<区间维度取值列>"

参数说明：

参数说明默认值
--input_file输入数据文件路径 (.csv)(必填)
--value_col区间各维度对应数值列名(必填)
--dimension_col区间维度值列(必填)

fallback（指引模式）

无脚本环境时按以下计算方式手动计算，不可遗漏任何指标计算。

对用于分析的数值序列 (x_1,\ldots,x_n)，计算以下数据分布特征：

指标计算方式
均值(\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i)
标准差(\sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i-\bar{x})^2})
中位数1. 将数据进行从小到大排序；2. n 是奇数，中位数为第 (\frac{n+1}{2}) 个数；n 是偶数，中位数为第 (\frac{n}{2}) 位和第 (\frac{n+1}{2}) 为数的平均数
top 5 的维度数据最大的 5 个维度 ｜
top 5 维度各数值占比数值最大的 5 个维度，每个维度对应数值占所有维度数值和的比例
频率分布（按累计占比分桶）1. 将所有维度按数值 (x_i) 从大到小排序；2. 计算总和 (S=\sum_i x_i)；3. 依次计算累计和 (C_k=\sum_{i=1}^{k} x_i) 与累计占比 (r_k = C_k / S)；4. 按 (r_k) 将第 (k) 个维度名归入对应的桶：<0.5（(r_k<0.5)）、[0.5, 0.6)、[0.6, 0.7)、[0.7, 0.8)、[0.8, 0.9)、[0.9, 0.95)、>=0.95（(r_k \ge 0.95)）；5. 输出为 dict，key 为桶名，value 为该桶包含的维度名称列表

输出要求

输出上述全部数值分布特征的计算结果，不要遗失任何计算结果，包括 NaN 值。频率分布 中即使某个桶没有任何维度，也需保留为空列表 []。

© agentscope-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in packages/qwenpaw-data-skills/skills/atomic/bi-distribution-analysis of agentscope-ai/QwenPaw-Data.

  • SKILL.md
  • scripts/distribution_stats.py

Open the folder on GitHubat commit e0bae36

Compare with similar skills

Bi Distribution Analysis 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.

Bi Distribution Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bi Distribution Analysis this skillagentscope-ai/QwenPaw-Data127—~580Automated safety check: PassApache-2.0
Distributed Triagepytorch/pytorch104k—~2.8kAutomated safety check: PassCustom licence
Distributed Tracingwshobson/agents40k12 repos~527Automated safety check: PassMIT
Distribute Skill To All Agentssickn33/agentic-awesome-skills47k1 repos~1.2kAutomated safety check: PassMIT
Distributed Trainingaiming-lab/AutoResearchClaw15k—~216Automated safety check: PassMIT
Debug Distributed Hangsgl-project/sglang37k2 repos~2.4kAutomated safety check: PassApache-2.0

Similar skills

  • Distributed Triage

    pytorch/pytorch

    Sub-triages issues in the oncall:distributed queue by assigning distributed module labels, routing to sub-oncalls, and marking triaged.

    104k GitHub stars~2.8k tokensUpdated today
    DevOps & CloudAuto-check passed

  • Distributed Tracing

    wshobson/agents

    Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks.

    40k GitHub starsUsed in 12 repos~527 tokens
    DevOps & CloudAuto-check passed

  • Distribute Skill To All Agents

    sickn33/agentic-awesome-skills

    Distribute a skill across configured agent skill folders while respecting local symlink layouts.

    47k GitHub starsUsed in 1 repo~1.2k tokens
    Auto-check passed

  • Distributed Training

    aiming-lab/AutoResearchClaw

    Multi-GPU and distributed training patterns with PyTorch DDP.

    15k GitHub stars~216 tokensUpdated 1 mo ago
    AI & LLM EngineeringAuto-check passed

  • Debug Distributed Hang

    sgl-project/sglang

    Debug hanging issues in SGLang distributed inference (TP/PP/DP/EP).

    37k GitHub starsUsed in 2 repos~2.4k tokens
    DevelopmentAuto-check passed

  • Paddle Design Distributed

    PaddlePaddle/Paddle

    A skill your agent uses when working with Paddle's distributed training system: understanding parallelism strategies (DP, ZeRO, TP, PP, SP), semi-automatic parallel with ProcessMesh + shardtensor…

    24k GitHub stars~660 tokensUpdated today
    AI & LLM EngineeringAuto-check passed

More from agentscope-ai/QwenPaw-Data

All 29 skills in this repo

  • Bi Report Generation

    agentscope-ai/QwenPaw-Data

    将 BI 数据分析结果组织成可视化 HTML 报告。当分析完成、需要生成报告时调用. An agent skill from agentscope-ai/QwenPaw-Data.

    127 GitHub stars~1.2k tokensUpdated 5 days ago
    Auto-check passed

  • Fetch Data

    agentscope-ai/QwenPaw-Data

    取数 / 查数据 / 拉数据 / 跑 SQL。把自然语言取数需求转为 SQL，经数据湖仓执行后返回查询结果供下游分析。任何需要业务数据的任务在工作区缺少对应文件时都必须先调用此技能——覆盖 BI 业务分析、留存 / 转化 / 同期群分析、数据探索 EDA、统计建模、定量计算、元数据查询、数据查询。命中任一即触发：(1) 直接索要指标或记录，如「DAU 多少」「上月销售额」「3…

    127 GitHub stars~2.5k tokensUpdated 5 days ago
    Auto-check passed

  • Bi Adaptive Threshold

    agentscope-ai/QwenPaw-Data

    通过量化历史数据的自然波动幅度，自适应计算判定阈值。当需要从数据本身确定阈值（如波动阈值、影响度阈值等）、而非使用固定值时调用。仅适用于日/周粒度阈值确定。

    127 GitHub stars~726 tokensUpdated 5 days ago
    Auto-check passed

  • Bi Anomaly Detection

    agentscope-ai/QwenPaw-Data

    基于阈值检测时间序列中的显著异常波动点。当需要找出指标异常波动日期、识别数据异动时调用. An agent skill from agentscope-ai/QwenPaw-Data.

    127 GitHub stars~637 tokensUpdated 5 days ago
    Auto-check passed

  • Bi Attribution Analysis

    agentscope-ai/QwenPaw-Data

    计算各维度（组）值对指标变动的贡献度，支持可加型量值指标和加权平均型/率值指标。当需要计算贡献度、解释指标"为什么涨/跌"时调用。

    127 GitHub stars~1.2k tokensUpdated 5 days ago
    Auto-check passed

  • Bi Causal Attribution

    agentscope-ai/QwenPaw-Data

    从运营周报、活动文档、对话输入或文档工具 API 中提取业务事件，与指标异常时间窗口对齐，生成有证据支撑的因果归因假设并排序。当已知指标存在异常波动、需要从外部文档证据中解释"为什么"时调用。

    127 GitHub stars~1.3k tokensUpdated 5 days ago
    Auto-check passed

Questions about Bi Distribution Analysis

What does Bi Distribution Analysis do?

分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。. Bi Distribution Analysis is an agent skill from agentscope-ai/QwenPaw-Data.

How do I install Bi Distribution Analysis in Claude Code?

Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-distribution-analysis -a claude-code`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-distribution-analysis in agentscope-ai/QwenPaw-Data) into .claude/skills/bi-distribution-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Bi Distribution Analysis in Codex?

Run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-distribution-analysis -a codex`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/atomic/bi-distribution-analysis in agentscope-ai/QwenPaw-Data) into .agents/skills/bi-distribution-analysis in your project. Codex loads it when a task matches its description.

Can I use Bi Distribution Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentscope-ai/QwenPaw-Data --skill bi-distribution-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/bi-distribution-analysis, .gemini/skills/bi-distribution-analysis, .github/skills/bi-distribution-analysis and .opencode/skills/bi-distribution-analysis in your project.

What does Bi Distribution Analysis need to run?

Going by SKILL.md and its folder, Bi Distribution Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Bi Distribution Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bi Distribution Analysis safe to install?

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.

What licence does Bi Distribution Analysis use?

Bi Distribution Analysis is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bi Distribution Analysis use?

About 580 tokens (SKILL.md is roughly 2.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bi Distribution Analysis?

Skills that share tags, products or a category with Bi Distribution Analysis: Distributed Triage (pytorch/pytorch, 104k stars), Distributed Tracing (wshobson/agents, 40k stars), Distribute Skill To All Agents (sickn33/agentic-awesome-skills, 47k stars) and Distributed Training (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bi Distribution Analysis?

agentscope-ai (a GitHub organization) maintains it in agentscope-ai/QwenPaw-Data, which has 127 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 5, 2026.

Source: agentscope-ai/QwenPaw-Data on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.