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 breakout_candidate_finder_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/breakout_candidate_finder_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/breakout_candidate_finder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/breakout_candidate_finder_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/breakout_candidate_finder_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/breakout_candidate_finder_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 | $0.00076 | $0.02215 |
| Opus 5.5 | $0.00030 | $0.00886 |
| Sonnet 5.5 | $0.00015 | $0.00443 |
| Haiku 4.5 | $0.00008 | $0.00221 |
Grade A, and why
breakout_candidate_finder_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.
Copies of this mod
8 near-identical copies found in the catalogue:
- take_profit_ladder_skill — 86% identical, 197 lines differ
- shareholder_letter_digest_skill — 86% identical, 197 lines differ
- pullback_opportunity_finder_skill — 84% identical, 191 lines differ
- founder-requirements-clarification — 84% identical, 433 lines differ
- market_breadth_health_skill — 84% identical, 202 lines differ
- theme_leader_identification_skill — 84% identical, 197 lines differ
- guidance_change_impact_skill — 84% identical, 199 lines differ
- major_announcement_impact_skill — 83% identical, 200 lines differ
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Breakout Candidate Finder 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
本技能用于从一组股票中筛选“最接近有效突破”的候选标的，并把模糊的图形观察转化为可执行的候选池。输出重点不是简单罗列创新高个股，而是同时评估价格结构、量能条件、催化背景、交易拥挤度与失效边界，帮助使用者把注意力集中在最值得提前准备的突破机会。
功能简介
本技能的核心功能是：筛选形态成熟、放量待发的突破候选股，并给出触发条件。
它强调把候选发现、结构判断、优先级排序和执行前验证连接成一条完整链路，避免把“看起来快突破”误写成“已经可以介入”。
使用场景
- 短中线交易者需要从股票池中找出最接近突破的标的。
- 研究或交易团队要在盘前形成当天的重点观察名单。
- 某个板块进入强化阶段后，需要识别谁更可能率先走成龙头。
- 用户不想盯全部股票，希望把时间优先分配给结构最完整的候选股。
工作原则
- 突破筛选必须同时覆盖位置、结构、量能、催化与风险，不能只看价格接近前高。
- 任何候选结论都必须附带触发条件与失效边界，不能只有方向没有纪律。
- 必须区分“准备突破”“确认突破”“假突破”三个阶段，避免阶段混淆。
- 若市场环境不支持扩散，应下调单只股票的突破评分，不能脱离环境单独判断。
- 正文中只呈现分析逻辑和结论，不讨论内部调用过程。
执行流程
Step 1：确认筛选范围与交易时间框架
先确认本次筛选面向的是哪类股票池，以及使用者的交易周期：
- 是全市场扫描、行业内筛选，还是只看自选股
- 是偏 1-5 日的短线突破，还是 2-8 周的波段突破
- 用户更重视即时触发，还是愿意等待二次确认
- 当前任务是盘前准备、盘中缩圈，还是周度候选池更新
若用户未明确说明，默认按“最近一到三周内可能形成有效突破的短中线候选股”展开。
Step 2：先剔除不具备突破讨论价值的标的
先做第一轮粗筛，把明显不适合进入候选池的股票去掉，重点排除：
- 长期趋势向下且尚未形成底部重建结构的标的
- 价格虽有异动但波动混乱、缺少清晰整理平台的标的
- 流动性不足、成交过于稀薄、难以形成有效交易承接的标的
- 临近重大不确定事件、停牌风险或单一消息驱动过强的标的
这一步的目标不是找出最好机会，而是先确保剩余候选具备比较的基础。
Step 3：识别整理平台与关键阻力位置
对保留下来的股票，重点判断其是否形成了具备交易意义的突破准备结构：
- 是否存在清晰的平台、箱体、杯柄、旗形或高位整理区
- 平台时间是否足够，是否经历了必要的筹码交换
- 阻力位是否明确，用户能否清楚知道“突破发生在什么价格区域”
- 当前价格距关键位是过远、刚好接近，还是已经提前透支
只有先把位置与结构定义清楚，后续的“突破概率”讨论才有意义。
Step 4：检查量价关系与筹码承接质量
突破机会的质量，核心不在“涨了多少”，而在“涨得是否健康”。重点观察：
- 整理期间是否呈现波动收敛、抛压减弱、换手改善
- 逼近关键位时是否出现温和放量而非无序巨震
- 上涨日与回调日的量能结构是否偏强，是否体现主动承接
- 历史上靠近同一阻力位时，是否曾多次冲高失败
若量价结构显示分歧过大，即使图形好看，也应降低优先级。
Step 5：补充催化与基本面背景
优质突破往往不只是图形成立，还需要有叙事、事件或经营变化提供支撑。需要确认：
- 近期是否存在业绩、订单、产品、政策、主题强化等新增催化
- 公司所处板块是否处于资金偏好上行期
- 若股价接近突破，当前估值与财务质量是否足以支撑继续关注
- 是否存在即将到来的重大事件，可能决定突破成败
这一步的目的不是把技术选股改写成基本面深研，而是避免挑出“图形漂亮但逻辑空心”的标的。
Step 6：判断突破所处的市场环境
再好的个股，也需要合适的环境配合。要同时判断：
- 市场是否愿意接纳高位突破或强势追涨
- 板块内是否已有龙头打出高度，带来正反馈
- 当前环境更适合主升浪延续，还是更像存量博弈下的轮动冲高
- 若指数、情绪或板块进入退潮，候选股成功率是否会被明显压制
突破筛选必须把“个股结构”和“环境允许度”合并评估。
Step 7：给出候选优先级与触发条件
将剩余标的按突破准备度分层，至少区分：
- 第一优先级：结构成熟、阻力明确、量价健康、催化配合较好
- 第二优先级：结构尚可，但仍需等待量能、环境或事件确认
- 观察级：具备潜力，但时机过早或不确定性较高
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 · 186 lines · 76 tokens per session scan A bada614c8850
breakout_candidate_finder_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 76 tokens to every session and 2,215 once invoked, about $0.0003 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 2026-08-30.
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