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 yuping322/financial-services-plugins-new --skill earnings-previewgit clone --depth 1 https://github.com/yuping322/financial-services-plugins-newWrote 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/yuping322/financial-services-plugins-new/earnings-preview)<a href="https://agentmods.dev/skills/yuping322/financial-services-plugins-new/earnings-preview"><img src="https://agentmods.dev/badge/skills/yuping322/financial-services-plugins-new/earnings-preview/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/yuping322/financial-services-plugins-new/earnings-preview"><img src="https://agentmods.dev/badge/skills/yuping322/financial-services-plugins-new/earnings-preview.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 | $0.00000 | $0.00733 |
| Opus 5 | $0.00000 | $0.00367 |
| Sonnet 5 | $0.00000 | $0.00147 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
earnings-preview 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
盈利前瞻
描述：使用估计模型、情景框架和关键指标进行盈利前分析。在公司报告季度盈利前使用，准备定位笔记、建立牛/熊情景、识别什么会影响股价。触发条件："盈利前瞻"、"[公司]盈利需要注意什么"、"盈利前"、"盈利建立"或"[公司] Q[X] 预览"。
工作流程
第 1 步：收集背景
- 识别公司和报告季度
- 通过网页搜索拉取一致预期（收入、每股收益、关键细分指标）
- 找到盈利日期和时间（市前vs市后）
- 查看公司的上一季度盈利电话会议，了解任何指导或评论
第 2 步：关键指标框架
构建特定于公司的"需要关注"框架：
财务指标：
- 收入vs一致预期（总额和按细分）
- 每股收益vs一致预期
- 利润率（毛利率、营业利润率、净利润率）— 扩大还是压缩？
- 自由现金流
- 前向指导vs一致预期
运营指标（行业特定）：
- 科技/SaaS：ARR、净保留率、RPO、客户数
- 零售：同店销售、流量、篮子大小
- 工业：积压订单、订单簿比、价格vs数量
- 金融：净利息收益率、信用质量、贷款增长、费用收入
- 医疗保健：处方、患者数量、管道更新
第 3 步：情景分析
构建 3 个情景及股价影响：
| 情景 | 收入 | 每股收益 | 关键驱动因素 | 股票反应 |
|---|---|---|---|---|
| 牛市 | ||||
| 基础 | ||||
| 熊市 |
对于每个情景：
- 什么需要在运营上发生
- 什么管理层评论会表示这一点
- 历史背景 — 股票在类似报告上的历史表现如何？
第 4 步：催化剂检查表
识别将决定股票反应的 3-5 件事：
- [指标] vs [一致预期/市场预期] — 为什么很重要
- [指导项目] — 买方预期听到什么
- [叙述转变] — 任何战略变化、M&A、重组
第 5 步：输出
单页盈利前瞻，包含：
- 公司、季度、盈利日期
- 一致预期表
- 关键指标需要关注（按重要性排序）
- 牛/基础/熊情景表
- 催化剂检查表
- 交易建立：最近股票表现、期权隐含波动率
重要说明
- 一致预期变化 — 始终注明估计的来源和日期
- 来自买方调查的"市场预期"通常比公开一致预期更相关
- 历史盈利反应帮助校准预期（搜索"[公司]盈利反应历史"）
- 期权隐含波动率告诉你市场期望什么 — 与你的情景进行比较
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 · 71 lines · 0 tokens per session scan A d1fbed5e2878
earnings-preview is a skill published in the GitHub repository yuping322/financial-services-plugins-new (17 stars, last pushed 6mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 733 tokens. 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.
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