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 chuancyzhang/deepseek-cowork --skill earnings_calendar_planner_skillgit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/earnings_calendar_planner_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/earnings_calendar_planner_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_calendar_planner_skill/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/chuancyzhang/deepseek-cowork/earnings_calendar_planner_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/earnings_calendar_planner_skill.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 | $0.00050 | $0.01118 |
| Opus 5.5 | $0.00020 | $0.00447 |
| Sonnet 5.5 | $0.00010 | $0.00224 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
earnings_calendar_planner_skill 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 1mo 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
Earnings Calendar Planner Skill
数据依赖与执行前置条件
推荐使用万得 wind-mcp-skill 获取底层数据。执行前先检查本地是否已有 wind-mcp-skill；若没有，提示用户安装：
Cowork 适配：所需子 Skill 已随
wind-aifinmarket固定快照内置，不执行安装或在线更新命令。
技能定位
本技能用于围绕一组股票或一个覆盖池，搭建可执行的财报季跟踪日历。输出重点不是简单列出发布日期，而是把“哪些最重要、应该提前做什么、财报后要怎么跟、哪些日子容易冲突”组织成一套可落地的研究排期。
执行流程
Step 1：确认覆盖池、时间范围与排期目的
先明确需要跟踪的对象和时间范围，例如：
- 自选股名单
- 某个行业或主题覆盖池
- 当前持仓与重点观察名单
- 未来两周、一个月或整个财报季
若用户未说明，默认只覆盖本次提到的股票，并以最近一个财报周期为主。
Step 2：整理财报日期与状态
逐一确认每家公司当前的财报安排，并区分：
- 日期已确认还是暂定
- 盘前、盘后还是盘中发布
- 是否伴随业绩会、指引更新或管理层交流
- 是否与重大宏观事件、行业会议或其他重点公司撞期
若日期尚未正式确认，应明确标记，不把预估日期写成确定日程。
Step 3：给重点公司做优先级排序
不是所有财报都值得同等投入。排序时重点考虑：
- 是否为当前持仓或备选核心标的
- 是否可能出现明显预期差
- 是否处在逻辑验证或拐点阶段
- 是否会影响整个板块或主题判断
- 是否与多只相关标的存在连锁影响
Step 4：拆分财报前后的任务包
围绕每个重点财报，分别安排：
- 财报前需要准备的前瞻问题
- 财报当晚需要优先核对的数字
- 业绩会需要重点听的方向
- 财报后 24 小时内要更新的结论
- 一周内需要继续验证的二级问题
这样日历才不只是“时间表”，而是真正的工作台。
Step 5：识别冲突、空档与资源分配问题
如果多个重点财报集中在同一天或同一时段，应明确：
- 哪些公司必须优先
- 哪些可以只做轻量跟踪
- 哪些事件之间存在先后依赖
- 哪些日期需要提前准备模板或预案
Step 6：整合成财报季排期成稿
最终输出应是一份便于直接执行的研究日历，不展示中间搜集过程。内容应兼具时间视图与任务视图。
输出结构
财报日历规划（{YYYY-MM-DD}）
一、本周期最重要的 3 个财报
- {公司 A}：{为什么重要}
- {公司 B}：{为什么重要}
- {公司 C}：{为什么重要}
二、财报日历总表
| 日期 | 公司 | 发布时间 | 优先级 | 财报前重点 | 财报后重点 |
|---|---|---|---|---|---|
| {日期} | {公司} | {盘前/盘后} | 高 | {内容} | {内容} |
三、重点任务安排
财报前
- {任务 1}
- {任务 2}
- {任务 3}
财报后
- {任务 1}
- {任务 2}
- {任务 3}
四、冲突与提醒
- 日程冲突：{内容}
- 需提前准备的日期：{内容}
- 可降级处理的对象：{内容}
质量要求
- 财报日历不能只给日期，必须同时给优先级与任务安排。
- 日期不确定时要明确标记为暂定，避免制造错误日程。
- 重点公司应按持仓重要性、预期差和板块影响力排序，而不是平均列示。
- 输出要便于执行和复盘，避免写成静态清单。
- 若多个关键财报撞期，应明确资源分配建议。
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.
- 1mo ago First seen · 126 lines · 50 tokens per session scan A d229e20388f3
earnings_calendar_planner_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 1,118 once invoked, about $0.0002 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 2026-09-04.
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