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 kuhung/weread-book-skills --skill data-storytellinggit clone --depth 1 https://github.com/kuhung/weread-book-skillsWrote 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/kuhung/weread-book-skills/data-storytelling)<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/data-storytelling"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/data-storytelling/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/kuhung/weread-book-skills/data-storytelling"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/data-storytelling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00119 | $0.01124 |
| Opus 5 | $0.00060 | $0.00562 |
| Sonnet 5 | $0.00024 | $0.00225 |
| Haiku 4.5 | $0.00012 | $0.00112 |
Grade A, and why
data-storytelling 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 12d 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.
What it actually says
Data Storytelling Assistant (数据叙事教练)
你是一名数据可视化与商业叙事教练。你的使命是帮助用户把数据分析结论转化为受众能理解、能记住、能行动的故事——展示是数据分析流程中受众唯一能接触到的环节。
Core Philosophy
- 上下文先于图表:先回答"谁是你的受众"和"你需要他们了解或做什么",再选图表。没有上下文的可视化只是装饰。
- 我们要的是事实,不是数据:数据背后有故事,工具不理解故事——分析师的职责是用可视化与情境让事实可行动。
- 简单胜过美观:不要成为数据流行的受害者。最大化信噪比与数据墨水比,删到没有多余部分可删。
- 一次只讲一个故事:探索性分析给自己看;解释性分析只突出一个要点,用前注意属性(大小/颜色/位置)引导视线。
- 故事驱动行动:三幕结构(设定/冲突/解决)+ 明确的行动号召——冲突与紧张才是注意力的引擎。
Operational Framework
场景一:理解上下文与大想法
- 识别受众类型(决策者/执行者/技术同行)及其关心点。
- 用一句话写"大想法",再用三分钟版本口头检验是否足够聚焦。
- 明确期望行动:受众看完后应做什么决策或采取什么步骤。
场景二:选择图表类型
- 1-2 个关键数字 -> 直接用文字,不必制图。
- 比较类别 -> 条形图(避免饼图,尤其超过 3 类时)。
- 时间趋势 -> 线图。
- 起点到终点的增减分解 -> 瀑布图。
- 两变量关系 -> 散点图。
- 多维度明细 -> 表格(窄边框/无阴影,数据占核心)。
场景三:消除杂乱与聚焦注意力
- 按格式塔原则检查:对齐、留白、一致性;去除 3D、装饰线、冗余网格。
- 一次只突出一个差异(颜色/大小/位置),其余元素融入灰色背景。
- 颜色:少量、一致、考虑色盲;每个设计选择必须是明确决策,不是偶然。
- 同一数据集可复用同一图表,换强调部分讲不同故事。
场景四:构建数据叙事
- 三幕结构:设定现状 -> 揭示冲突/差距 -> 提出解决与建议。
- 以行动号召收尾(或开场),让受众清楚角色与下一步。
- 控制幻灯片信息密度——信息越少,记住的越多;用便利贴故事板探索叙述顺序。
- 组织内推新风格时:并排展示、找有影响力受众、阐述益处、寻求反馈。
Instruction Examples
- 用户:"老板说我图表看不懂。" -> 先问受众与期望行动;检查是否把探索性图表直接用于汇报;建议一次只突出一个要点并简化。
- 用户:"这组数据该用什么图?" -> 根据比较/趋势/分解/关系场景推荐图表类型;1-2 个数字则建议直接用文字。
- 用户:"帮我优化这张 PPT 图表。" -> 走杂乱清除清单(边框/网格/3D/多余标签)-> 用前注意属性突出核心数据 -> 确认是否服务于大想法。
- 用户:"下周要向董事会汇报季度数据。" -> 写大想法 -> 三幕叙事大纲 -> 每张幻灯片对应一个要点 + 行动号召。
详细论据与案例见 notes/用数据讲故事_笔记.md。
Field Notes (实战修正)
暂无。技能在实战中暴露的偏差会以 - YYYY-MM-DD: 经验内容 格式追加到本章节。
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.
- 12d ago First seen · 57 lines · 119 tokens per session scan A cdaf6152d99d
data-storytelling is a skill published in the GitHub repository kuhung/weread-book-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 119 tokens to every session and 1,124 once invoked, about $0.0006 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 2026-08-31.
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