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 canslim_growth_scan_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/canslim_growth_scan_skill)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/canslim_growth_scan_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/canslim_growth_scan_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/canslim_growth_scan_skill"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/canslim_growth_scan_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 | $0.00070 | $0.01713 |
| Opus 5.5 | $0.00028 | $0.00685 |
| Sonnet 5.5 | $0.00014 | $0.00343 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
canslim_growth_scan_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
78% identical to breakout_candidate_finder_skill — 192 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.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CANSLIM Growth Scan 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
本技能用于依据成长股选股框架，从大量股票中筛出“基本面成长、市场预期强化、价格表现领先、供需结构有利”的强势候选股。输出重点不是把经典方法论机械打标签，而是把成长质量、业绩加速、相对强弱和市场资金偏好整合成一套实用筛选框架。
功能简介
本技能的核心功能是：用成长股方法论批量筛选基本面与走势共振的强势标的。
它强调把成长、预期、强势、供需和市场主线结合判断，帮助使用者形成一组更接近“强者恒强”逻辑的成长股候选池。
使用场景
- 成长股投资者需要在一批股票中快速找出最符合强势成长画像的对象。
- 财报季后或景气强化阶段，需要批量更新成长候选池。
- 用户希望同时兼顾基本面成长与市场强度，而非只看单一维度。
- 团队需要在行业内部进行“谁最强、为什么强”的结构化筛选。
工作原则
- 成长股筛选必须同时考虑经营加速与市场确认，不能只看业绩增速。
- 价格相对强弱是结果，也是筛选条件，但不能脱离基本面单独使用。
- 要关注供需结构和流通盘特征，避免只看静态财务指标。
- 方法论应服务实际筛选，不应写成名词解释。
- 正文中不显式提及内部调用过程。
执行流程
Step 1：确认筛选范围与成长定义
先明确本次扫描更偏向：
- 高增长行业中的龙头筛选
- 多行业横向扫描成长强股
- 近几季出现明显加速的财报成长股
- 还是主题强化下的景气成长股
同时确认用户更看重盈利成长、收入成长，还是“成长 + 相对强势”的综合画像。
Step 2：先筛经营成长与变化方向
成长股的第一层，是经营变量是否向好。重点判断：
- 收入、利润、利润率或核心业务指标是否呈现加速
- 增长是来自真实需求扩大，还是一次性波动
- 当前成长是否仍在改善，而非已经见顶
- 公司是否具备支持未来几个季度继续维持高景气的基础
Step 3：结合市场预期与财报验证
成长股是否值得跟踪，不只看报表，还要看市场是否愿意上修预期。要判断：
- 最近几次财报或经营更新是否持续强化市场预期
- 管理层指引和行业反馈是否支持后续继续上修
- 市场此前是否低估了成长强度
- 若预期已经很高，继续上修空间是否仍足够
Step 4：检查相对强度与领涨属性
成长股选股必须兼顾市场确认。需要重点看：
- 价格表现是否显著强于大盘、行业和同类股票
- 强势是稳定推进，还是剧烈波动后的一次性脉冲
- 是否具备在板块中引领资金聚焦的属性
- 过去一段时间内，回调是否短而强、上涨是否更有斜率
Step 5：评估供需结构与交易特征
很多成长股强不起来，不是因为逻辑不行，而是供需结构不配合。应观察：
- 流通盘、换手、筹码结构是否支持趋势延续
- 是否存在过高拥挤或明显兑现压力
- 市场对这类成长风格的偏好是否正在上升
- 当前是否更像“机构型慢牛”，还是“题材型快涨快跌”
Step 6：识别真正的优先级差异
将候选股按“成长加速度 + 预期上修能力 + 相对强势 + 供需结构”综合排序，区分：
- 核心成长强股
- 逻辑不错但仍需等待确认的次优对象
- 财务成长看似漂亮但市场尚未确认的观察对象
Step 7：补充风险与失败模式
成长股筛选必须主动考虑失败方式，例如：
- 高增长不可持续
- 估值透支过快
- 市场风格转向防御
- 供需结构恶化导致强势中断
Step 8：整合成成长强股成稿
最终输出应让使用者知道“谁最符合成长强股画像、为什么、该继续跟谁、该回避谁”。正文只呈现结论、依据和后续观察框架。
输出结构
成长强股筛选（{YYYY-MM-DD}）
一、30 秒结论
- 最强成长候选：{名称列表}
- 最核心的优势维度：{成长加速 / 预期上修 / 相对强势 / 供需优势}
- 整体判断：{积极跟踪 / 选择性聚焦 / 谨慎分层}
- 主要风险：{一句话}
二、候选池总览
| 股票 | 成长质量 | 预期上修空间 | 相对强势 | 供需结构 | 优先级 |
|---|---|---|---|---|---|
| {标的1} | {强/中/弱} | {高/中/低} | {强/中/弱} | {优/中/弱} | {A/B/C} |
| {标的2} | {等级} | {等级} | {等级} | {等级} | {等级} |
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 · 162 lines · 70 tokens per session scan A 0790b8733cc1
canslim_growth_scan_skill is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 1,713 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 78% identical to breakout_candidate_finder_skill, differing in 192 lines, and is treated as a copy.
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