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 volume_spike_reasoning_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/volume_spike_reasoning_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/volume_spike_reasoning_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/volume_spike_reasoning_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/volume_spike_reasoning_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/volume_spike_reasoning_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 | $0.00066 | $0.01138 |
| Opus 5.5 | $0.00026 | $0.00455 |
| Sonnet 5.5 | $0.00013 | $0.00228 |
| Haiku 4.5 | $0.00007 | $0.00114 |
Grade A, and why
volume_spike_reasoning_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.
This is a copy
77% identical to breakout_candidate_finder_skill — 196 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Volume Spike Reasoning 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
本技能用于解释“为什么突然放量”，并把放量背后的性质区分为事件驱动、板块扩散、主力博弈、技术位释放或情绪交易。目标是避免把所有放量都当成利好，也避免错过真正有信息含量的放量。
执行流程
Step 1：确认放量发生的上下文
先定义放量发生的时间粒度与市场位置：
- 盘中瞬时放量、半小时放量、全天放量
- 放量发生在上涨、下跌、横盘还是突破/跌破附近
- 当日是否叠加高换手、跳空、封板、炸板或尾盘异动
Step 2：判断放量是否显著偏离常态
比较当前成交表现与近阶段常态水平，重点看：
- 成交额或成交量是否显著抬升
- 换手率变化是否同步明显
- 放量是持续型还是脉冲型
- 放量是否主要集中在单一时段
Step 3：排查增量驱动来源
重点从四类原因进行排查：
- 事件驱动：公告、新闻、政策、财报、产品、订单、监管
- 板块驱动：同行集体异动、题材发酵、龙头带动
- 技术驱动：关键位突破、跌破、止损踩踏、趋势加速
- 资金驱动：情绪抢筹、游资接力、博弈换手、尾盘资金试盘
Step 4：识别放量质量
放量本身不代表有效，必须回答：
- 价格是否顺着放量方向推进
- 是否出现“只放量不走价”或“放量冲高回落”
- 放量后是否带来结构重估，而不是一天脉冲
- 当前市场是否支持这种放量延续
Step 5：输出跟踪重点
给出后续最重要的验证问题，例如：
- 次日是否承接
- 关键位置是否站稳
- 是否继续获得题材热度支持
- 成交是否回落到常态后仍能维持价格强势
Step 6：整合为归因结果
最终输出以“放量性质 + 可信度 + 后续观察点”为主，不展示内部排查路径。
输出结构
{股票名称}放量异动归因
一、放量概况
- 放量时段：{盘中/全天/尾盘}
- 价格表现：{上涨/下跌/冲高回落/震荡}
- 放量特征：{显著程度与持续性描述}
二、主要归因
| 归因方向 | 判断 | 说明 |
|---|---|---|
| 事件驱动 | 高/中/低 | {是否存在明确增量信息} |
| 板块驱动 | 高/中/低 | {是否有集体共振} |
| 技术驱动 | 高/中/低 | {是否与关键位置有关} |
| 资金博弈 | 高/中/低 | {是否更像情绪或资金行为} |
三、放量质量判断
- 可信度：{高/中/低}
- 最支持当前判断的证据：{1-2 条}
- 最需要警惕的风险：{1-2 条}
四、后续跟踪
- 下一步重点看：{信号}
- 若继续强化：{意味着什么}
- 若迅速衰减：{意味着什么}
质量要求
- 归因必须覆盖多种可能，不允许一上来就把放量等同于利好。
- 若证据不足，允许给出“归因暂不充分”的判断。
- 解释重点是“放量为何发生”以及“是否值得继续跟踪”。
- 同时存在多重原因时，要明确主因与次因，不要并列堆砌。
- 输出应服务盘中快速判断，结论要短而稳。
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 · 112 lines · 66 tokens per session scan A 7e0b87fbd086
volume_spike_reasoning_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 1,138 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 77% identical to breakout_candidate_finder_skill, differing in 196 lines, and is treated as a copy.
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