bi-report-generation

bi-report-generation is a skill for Claude Code, Codex from agentscope-ai/QwenPaw-Data. It costs 28 tokens per session (2,423 once invoked), scanned A, original, Apache-2.0.

A process for turning completed business-data analysis into a readable HTML report with charts, tables, summaries, and source data.

In plain words
What is it for?
Use it to create reports for one or several analysis topics, including overview cards, trend details, explanations, and data-backed conclusions.
Why use it?
It gives readers one structured place to understand the numbers and conclusions, without manually assembling a report from separate analysis files.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create reports for one or several analysis topics, including overview cards, trend details, explanations, and data-backed conclusions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentscope-ai/qwenpaw-data/bi-report-generation
Install

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.

Any agent
npx skills add agentscope-ai/QwenPaw-Data --skill bi-report-generation
Clone the repo
git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bi-report-generation

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-report-generation/github.svg)](https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-report-generation)
Your own site
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-report-generation"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-report-generation/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.

agentmods 80×15 button for bi-report-generation

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-report-generation"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-report-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,423 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.02423
Opus 5 $0.00014 $0.01211
Sonnet 5 $0.00006 $0.00485
Haiku 4.5 $0.00003 $0.00242

Measured today against content hash 4d66d8d4f234, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

bi-report-generation 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 today.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/report_builder.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

packages/qwenpaw-data-skills/skills/atomic/bi-report-generation/SKILL.md · 164 lines

How it starts

The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.

bi-report-generation

将分析结论和数据文件转化为读者友好的 HTML 报告。支持单主题和多主题场景。

报告生成遵循三个核心原则:

  • 准确:报告中的所有数字和结论必须来自分析产出的数据文件,不得编造或推测数据
  • 紧凑:充分利用页面空间,优先将数据块并排布局,减少纵向滚动
  • 高密度:每张卡片尽量承载多个相关结论,避免一个指标独占一张卡片

前置检查

开始前确认以下事项已就绪,任一缺失应先完成前置分析流程:

  • 分析主题明确(可能包含多个子主题)
  • 各主题的分析步骤已执行完毕,结论已产出
  • 支撑结论的数据文件已生成且可访问

执行步骤

1:展示布局规划

基于已完成的分析过程,梳理各主题涉及的关键指标、分析维度和数据特征,为每个主题规划展示形式:

  1. 根据分析路径选择展示方式:每个主题的第一张卡片必须是数据概况卡,展示核心指标整体情况作为背景,再按分析路径展开后续内容:

    分析路径 展示方式
    基础数据观测 KPI 卡展示核心数字,表格/图表展示数据细节
    下钻/拆解/归因 按照「现象 → 归因 → 数据佐证」展示完整分析路径
    归因类分析(含外部事件) 数据概况卡 → 按关键数据点或异常区间分块,每块附触发原因 + 具体动作描述的归因列表
  2. 选择图表类型:按 references/layout-spec.md §1 中的图表类型表匹配,命中即停;标注"✦ 支持切换"的场景需同时生成图表和表格视图(详见步骤 3)。

  3. 列出每个主题需要读取的数据文件。

  4. 多主题场景下,额外确定报告的总标题(概括报告整体范围)和总摘要(提炼各主题核心结论,形成跨主题的综合性洞察,将放在报告顶部的摘要区块)。

对每个主题,依次执行步骤 2 和步骤 3:

2:数据准备

根据步骤 1 确定的文件列表,读取该主题所需数据:

  1. 读取数据文件
  2. 列筛选:数据表可能包含与当前主题不直接相关的列,只保留相关列用于展示,剔除无关列

3:生成 HTML 卡片

根据步骤 1 的布局规划,为该主题生成独立的 HTML 卡片:

  • 卡片需包含标题、摘要、数据展示(表/图/KPI)、现象描述、分析解读和数据来源。

  • 生成卡片时需严格遵循 references/layout-spec.md 中的排版规范。

  • 图片一律用 ECharts 在 HTML 中动态绘制:从数据文件读取数值,在卡片内初始化 ECharts 实例渲染图片。禁止引用分析过程中已生成的 PNG/JPG 等静态图片(不得使用 <img> 标签嵌入图片路径、base64 或外链图片来展示图片)。

  • 标注"✦ 支持切换"的场景必须实现图表/表格双视图:图表容器顶部右侧放"图表"/"表格"切换按钮,默认显示图表,点击切换到表格;表格视图数值列颜色规则与图表保持一致。

  • 图表高亮重点:趋势图和折线图必须用 markPoint / markLine 标注关键数据点(异常值、大幅增长、增长停滞等),图表正下方紧跟一行小字列出具体数值和简要说明。

  • 分块归因分析:归因类卡片按数据重点分块展示,每块对应一个关键数据点或异常区间,块内归因列表中每条归因需包含:触发原因 + 具体动作描述,禁止只写原因不写动作。

  • 卡片写入 sections_{xxx}.json 文件(使用唯一标识区分不同报告)。每条 section 的字段说明:

    字段 类型 说明
    title string 卡片标题
    icon string(可选) Bootstrap Icon 类名,如 bi-graph-up-arrow
    type string(可选) "timeline" 表示时间线 section,渲染在 tab 区域外部且始终可见;不填则为普通可切换卡片
    html_fragment string 卡片内容 HTML
    [
      {
        "title": "访问趋势分析",
        "icon": "bi-graph-up-arrow",
        "html_fragment": "<div class='bg-white rounded-xl shadow-md p-6'>...</div>"
      },
      {
        "title": "业务事件时间线",
        "type": "timeline",
        "icon": "bi-calendar-event",
        "html_fragment": "<div class='bi-timeline'>...</div>"
      }
    ]
    

Read the full file on GitHub · 164 lines

Files

What ships with it

3 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.

Changes

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.

  1. today Changed · +5 lines 4d66d8d4f234
  2. 11d ago First seen · 159 lines · 28 tokens per session scan A 3e8ed4566417

Subscribe to this mod's changes

bi-report-generation is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 2,423 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

fastapi-patterns

FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.

affaan-m/ECC · 35 tokens

mem0-tour

Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.

mem0ai/mem0 · 47 tokens

status

Show whether Mem0 memory is working in this repository, covering configuration, capture state, pending flushes, and whether the Mem0 API key is valid. Use when the user asks whether memory is on, why a memory is missing, or anything looks broken.

mem0ai/mem0 · 54 tokens

pause

Pause Mem0 memory capture on this machine. Use when the user wants to stop memories being recorded, for example for private work or experiments.

mem0ai/mem0 · 30 tokens

open-source

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…

browser-use/browser-use · 137 tokens

mem0-test-integration

Verify a Mem0 integration produced by /mem0-integrate. Runs in the same workspace on the same branch (loose coupling) — installs dependencies, runs the repo's native test suite, then exercises a real end-to-end smoke flow against the user's API key. Produces a scorecard. TRIGGER when: user has just run /mem0-integrate…

mem0ai/mem0 · 207 tokens