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 Shy2593666979/AgentChat --skill data-analysisgit clone --depth 1 https://github.com/Shy2593666979/AgentChatWrote 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/shy2593666979/agentchat/data-analysis)<a href="https://agentmods.dev/skills/shy2593666979/agentchat/data-analysis"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/data-analysis/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/shy2593666979/agentchat/data-analysis"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/data-analysis.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 | $0.00034 | $0.00351 |
| Opus 5.5 | $0.00014 | $0.00140 |
| Sonnet 5.5 | $0.00007 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
data-analysis 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 yesterday.
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
数据分析
适用于表格概览、指标比较与数据质量检查。先确认实际可读取的内容，再确定能完成的分析范围。
工作方法
- 识别一行代表什么，检查字段、单位、时间范围、样本规模，以及是否只有截取的部分数据。
- 检查缺失值、重复记录、字段类型、异常范围及分组覆盖。清洗规则必须说明；异常值不等同于错误值。
- 定义指标口径，尤其是分母、去重对象、时间窗口与币种。对均值、中位数、增长率和转化率，说明计算依据。
- 从用户问题选择必要的对比，避免穷举无关统计。相关性不能直接解释为因果。
- 交付可复核的结果，并把发现、解释和建议分开。样本不足时减少结论的确定性。
读取 /data-analysis/reference/output-template.md 组织结果；使用 /data-analysis/reference/review-checklist.md 复核口径。
能力边界
当前没有代码执行与绘图工具。可以解释数据、复核小规模明确计算、编写供用户运行的分析代码；不能声称已运行 Python、处理完整附件或生成图表。材料过大或无法读取时，说明需要的字段与汇总结果，不凭样本推断全量统计。
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.
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.
- yesterday First seen · 23 lines · 34 tokens per session scan A b3ff66be55a7
data-analysis is a skill published in the GitHub repository Shy2593666979/AgentChat (921 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 351 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-18.
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reward-design
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agent-evolution
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bad-case-to-dpo
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knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.