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 email-writinggit 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/email-writing)<a href="https://agentmods.dev/skills/shy2593666979/agentchat/email-writing"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/email-writing/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/email-writing"><img src="https://agentmods.dev/badge/skills/shy2593666979/agentchat/email-writing.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 | $0.00037 | $0.00321 |
| Opus 5.5 | $0.00015 | $0.00128 |
| Sonnet 5.5 | $0.00007 | $0.00064 |
| Haiku 4.5 | $0.00004 | $0.00032 |
Grade A, and why
email-writing 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
邮件写作
产出可直接审阅的邮件草稿。已有草稿时优先保留原意，按用户要求调整语气、结构或语言。
工作方法
- 从请求中提取收件人关系、沟通目的、语言与语气。事实已足够时直接起草；缺少会改变邮件含义的内容时再询问。
- 用主题说明事项，用开头说明目的，正文只保留收件人需要的信息。
- 将希望对方执行的动作、需要回复的问题和已提供的期限写清楚。用户没有提供期限或承诺时，不自行编造。
- 对延期、拒绝或投诉回复，说明已知事实与可行下一步，避免替用户承认未经确认的责任或作出未经授权的承诺。
- 润色后核对姓名、日期、金额、附件描述和行动要求是否与原材料一致。
场景选择与结构见 /email-writing/reference/output-template.md；发送前可用 /email-writing/reference/review-checklist.md 复核。
当前只生成草稿，不发送邮件、不添加真实附件。缺少署名或姓名时省略，或明确标为待补充信息，不声称已经发送。
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 · 21 lines · 37 tokens per session scan A ab34b0b45d40
email-writing is a skill published in the GitHub repository Shy2593666979/AgentChat (921 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 321 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.
Other skills, from other repositories
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
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.