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 Travisun/Opptrix --skill wechat-articlegit clone --depth 1 https://github.com/Travisun/OpptrixWrote 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/travisun/opptrix/wechat-article)<a href="https://agentmods.dev/skills/travisun/opptrix/wechat-article"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/wechat-article/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/travisun/opptrix/wechat-article"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/wechat-article.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.00953 |
| Opus 5 | $0.00034 | $0.00477 |
| Sonnet 5 | $0.00013 | $0.00191 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
wechat-article 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 6d 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
公众号三 Agent 成稿
署名：Opptrix · AI Berkshire 分析（内容向；非买卖建议）
何时使用 / 边界
| 使用 | 不要用本技能 |
|---|---|
| 主题明确，要一篇可发布中文公众号长文 | 多篇《看懂 XX》系列 → @skill:deep-company-series |
| 需要作者/编辑/读者三角色强制外部视角 | 财报团队成稿 → @skill:earnings-team |
投研决策备忘版式 → @skill:investment-memo-craft |
定位确认（ask_user）
| 维度 | 默认 |
|---|---|
| 目标读者 | 有点背景但非该领域专家 |
| 深度 | 中深度 |
| 长度 | 3000–4000 字 |
| 风格 | 对话式（写给聪明的朋友） |
流程
阶段 1：研究素材
并行 run_subagent（2–3 个）：核心内容 / 行业应用 /（可选）对比脉络。工具：list_news_articles、http_fetch、browser_navigate；涉财务数字走 @skill:financial-data 验算。整理：核心论点一句、3–5 数据点、大纲 6–8 节 → workspace_write。
阶段 2：作者初稿
run_subagent 作者角色：纯中文、强钩子开头、公式须大白话、不用 emoji、段不过长。初稿写入 workspace。
阶段 3：编辑 + 读者并行
同轮两个 run_subagent：
- 编辑：标题/开头/结构/节奏/结尾传播力；给「原文→建议」对照。
- 读者：按目标画像答「前 3 段是否继续」「何处看不懂」「会否转发」。
双方都指出的问题必须改；矛盾时偏向读者体验。
阶段 4：定稿交付
综合修改 → create_web（长文可读排版）。文末可附资料链接。配图：若环境无法可靠提取论文高清图，诚实说明并用文字/表格替代，禁止假称已插入高清原图。
写作红线
- 不虚构数据；搜不到标估计并降级。
- 禁止套话腔（「让我们一起来看看」等）。
- 不过度承诺「颠覆/革命」。
- 涉财务关键数字须可追溯来源。
- 结尾须有一句可传播的判断（非荐股口号）。
网页目录
- 标题与读者定位
- 正文（定稿）
- 编辑/读者关键改动摘要（可折叠短节）
- 来源与缺口
- 免责声明
禁止
- 把本技能当深度买卖决策主路径
- 用训练知识冒充已检索原文
- 无交付结束
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.
- 6d ago First seen · 82 lines · 67 tokens per session scan A 635211d407a2
wechat-article is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 2d ago), licensed Apache-2.0. It adds 67 tokens to every session and 953 once invoked, about $0.0003 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 2026-09-03.
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