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Agent Skill · 研究检索

finviz-screener(芬维兹筛选器)

finviz-screener 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

finviz-screener 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 17,419。

总安装 6,614周安装 265GitHub Stars 1,119下载 2,141核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:finviz-screener(芬维兹筛选器)
来源仓库:https://github.com/tradermonty/claude-trading-skills
仓库路径:skills/finviz-screener
安装命令:
npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screener
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screener

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

finviz-screener 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。适用于芬维兹筛选器相关的资料搜索与内容筛选,可结合来源仓库和原始 README 进一步核验具体用法。

通过 npx skills add 命令从 GitHub 仓库安装,支持主流 AI 宿主环境集成调用。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/finviz-screener

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/tradermonty/claude-trading-skills --skill finv…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

finviz-screener 怎么安装?

在终端执行 npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screene…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

finviz-screener 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

finviz-screener 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

finviz-screener 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

finviz-screener 用的人多吗?

公开数据是累计安装 6,614 次,近一周安装 265 次,下载 2,141 次,GitHub 1,119 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

finviz-screener 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 1,119 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
finviz-screener

芬维兹筛选器 · 本页

6,6141,119接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

FinViz Screener

Overview

Translate natural-language stock screening requests into FinViz screener filter codes, build the URL, and open it in Chrome. No API key required for public screener; FINVIZ Elite is auto-detected from $FINVIZ_API_KEY for enhanced functionality.

Key Features:

  • Natural language → filter code mapping (Japanese + English)
  • URL construction with view type and sort order selection
  • Elite/Public auto-detection (environment variable or explicit flag)
  • Chrome-first browser opening with OS-appropriate fallbacks
  • Strict filter validation to prevent URL injection

When to Use This Skill

Explicit Triggers:

  • "高配当で成長している小型株を探したい"
  • "Find oversold large caps near 52-week lows"
  • "テクノロジーセクターの割安株をスクリーニングしたい"
  • "Screen for stocks with insider buying"
  • "FinVizでブレイクアウト候補を表示して"
  • "Show me high-growth small caps on FinViz"
  • "配当利回り5%以上でROE15%以上の銘柄を探して"

Implicit Triggers:

  • User describes stock screening criteria using fundamental or technical terms
  • User mentions FinViz screener or stock filtering
  • User asks to find stocks matching specific financial characteristics

When NOT to Use:

  • Deep fundamental analysis of a specific stock (use us-stock-analysis)
  • Portfolio review with holdings (use portfolio-manager)
  • Chart pattern analysis on images (use technical-analyst)
  • Earnings-based screening (use earnings-trade-analyzer or pead-screener)

Workflow

Step 1: Load Filter Reference

Read the filter knowledge base:

cat references/finviz_screener_filters.md

Step 2: Interpret User Request

Map the user's natural-language request to FinViz filter codes. Use the Common Concept Mapping table below for quick translation, and reference the full filter list for precise code selection.

Note: For range criteria (e.g., "dividend 3-8%", "P/E between 10 and 20"), use the {from}to{to} range syntax as a single filter token (e.g., fa_div_3to8, fa_pe_10to20) instead of combining separate _o and _u filters.

Common Concept Mapping:

User Concept (EN)User Concept (JP)Filter Codes
High dividend高配当fa_div_o3 or fa_div_o5
Small cap小型株cap_small
Mid cap中型株cap_mid
Large cap大型株cap_large
Mega cap超大型株cap_mega
Value / cheap割安fa_pe_u20,fa_pb_u2
Growth stock成長株fa_epsqoq_o25,fa_salesqoq_o15
Oversold売られすぎta_rsi_os30
Overbought買われすぎta_rsi_ob70
Near 52W high52週高値付近ta_highlow52w_b0to5h
Near 52W low52週安値付近ta_highlow52w_a0to5l
Breakoutブレイクアウトta_highlow52w_b0to5h,sh_relvol_o1.5
Technologyテクノロジーsec_technology
Healthcareヘルスケアsec_healthcare
Energyエネルギーsec_energy
Financial金融sec_financial
Semiconductors半導体ind_semiconductors
Biotechnologyバイオテクind_biotechnology
US stocks米国株geo_usa
Profitable黒字fa_pe_profitable
High ROE高ROEfa_roe_o15 or fa_roe_o20
Low debt低負債fa_debteq_u0.5
Insider buyingインサイダー買いsh_insidertrans_verypos
Short squeezeショートスクイーズsh_short_o20,sh_relvol_o2
Dividend growth増配fa_divgrowth_3yo10
Deep valueディープバリューfa_pb_u1,fa_pe_u10
Momentumモメンタムta_perf_13wup,ta_sma50_pa,ta_sma200_pa
Defensiveディフェンシブta_beta_u0.5 or sec_utilities,sec_consumerdefensive
Liquid / high volume高出来高sh_avgvol_o500 or sh_avgvol_o1000
Pullback from high高値からの押し目ta_highlow52w_10to30-bhx
Near 52W low reversal安値圏リバーサルta_highlow52w_10to30-alx
Fallen angel急落後反発ta_highlow52w_b20to30h,ta_rsi_os40
AI themeAIテーマ--themes "artificialintelligence"
Cybersecurity themeサイバーセキュリティ--themes "cybersecurity"
AI + CybersecurityAI&サイバーセキュリティ--themes "artificialintelligence,cybersecurity"
AI Cloud sub-themeAIクラウド--subthemes "aicloud"
AI Compute sub-themeAI半導体--subthemes "aicompute"
Yield 3-8% (trap excluded)配当3-8%(トラップ除外)fa_div_3to8
Mid-range P/E適正PER帯fa_pe_10to20
EV undervaluedEV割安fa_evebitda_u10
Earnings next week来週決算earningsdate_nextweek
IPO recent直近IPOipodate_thismonth
Target price above目標株価以上targetprice_a20
Recent news最新ニュースありnews_date_today
High institutional機関保有率高sh_instown_o60
Low float浮動株少sh_float_u20
Near all-time high史上最高値付近ta_alltime_b0to5h
High ATR高ボラティリティta_averagetruerange_o1.5

Step 3: Present Filter Selection

Before executing, present the selected filters in a table for user confirmation:

| Type | Value | Meaning |
|---|---|---|
| Theme | artificialintelligence | Artificial Intelligence |
| Sub-theme | aicloud | AI - Cloud & Infrastructure |
| Filter | cap_small | Small Cap ($300M–$2B) |
| Filter | fa_div_o3 | Dividend Yield > 3% |
| Filter | fa_pe_u20 | P/E < 20 |
| Filter | geo_usa | USA |

View: Overview (v=111)
Mode: Public / Elite (auto-detected)

Ask the user to confirm or adjust before proceeding.

Step 4: Execute Script

Run the screener script to build the URL and open Chrome:

python3 scripts/open_finviz_screener.py \
  --filters "cap_small,fa_div_o3,fa_pe_u20,geo_usa" \
  --view overview

# Theme-only screening (no --filters required)
python3 scripts/open_finviz_screener.py \
  --themes "artificialintelligence,cybersecurity" \
  --url-only

# Theme + sub-theme + filters combined
python3 scripts/open_finviz_screener.py \
  --themes "artificialintelligence" \
  --subthemes "aicloud,aicompute" \
  --filters "cap_midover" \
  --url-only

Script arguments:

  • --filters (optional): Comma-separated filter codes. Note: theme_* and subtheme_* tokens are not allowed here — use --themes / --subthemes instead.
  • --themes (optional): Comma-separated theme slugs (e.g., artificialintelligence,cybersecurity). Accepts bare slugs or theme_-prefixed values.
  • --subthemes (optional): Comma-separated sub-theme slugs (e.g., aicloud,aicompute). Accepts bare slugs or subtheme_-prefixed values.
  • --elite: Force Elite mode (auto-detected from $FINVIZ_API_KEY if not set)
  • --view: View type — overview, valuation, financial, technical, ownership, performance, custom
  • --order: Sort order (e.g., -marketcap, dividendyield, -change)
  • --url-only: Print URL without opening browser

At least one of --filters, --themes, or --subthemes must be provided.

Step 5: Report Results

After opening the screener, report:

  1. The constructed URL
  2. Elite or Public mode used
  3. Summary of applied filters
  4. Suggested next steps (e.g., "Sort by dividend yield", "Switch to Financial view for detailed ratios")

Usage Recipes

Real-world screening patterns distilled from repeated use. Each recipe includes a starter filter set, recommended view, and tips for iterative refinement.

Recipe 1: High-Dividend Growth Stocks (Kanchi-Style)

Goal: High yield + dividend growth + earnings growth, excluding yield traps.

--filters "fa_div_3to8,fa_sales5years_pos,fa_eps5years_pos,fa_divgrowth_5ypos,fa_payoutratio_u60,geo_usa"
--view financial
Filter CodePurpose
fa_div_3to8Yield 3-8% (caps high-yield traps)
fa_sales5years_posPositive 5Y revenue growth
fa_eps5years_posPositive 5Y EPS growth
fa_divgrowth_5yposPositive 5Y dividend growth
fa_payoutratio_u60Payout ratio < 60% (sustainability)
geo_usaUS-listed stocks

Iterative refinement: Start broad with fa_div_o3 → review results → add fa_div_3to8 to cap yield → add fa_payoutratio_u60 to exclude traps → switch to financial view for payout and growth columns.

Recipe 2: Minervini Trend Template + VCP

Goal: Stocks in a Stage 2 uptrend with volatility contraction (VCP setup).

--filters "ta_sma50_pa,ta_sma200_pa,ta_sma200_sb50,ta_highlow52w_0to25-bhx,ta_perf_26wup,sh_avgvol_o300,cap_midover"
--view technical
Filter CodePurpose
ta_sma50_paPrice above 50-day SMA
ta_sma200_paPrice above 200-day SMA
ta_sma200_sb50200 SMA below 50 SMA (uptrend)
ta_highlow52w_0to25-bhxWithin 25% of 52W high
ta_perf_26wupPositive 26-week performance
sh_avgvol_o300Avg volume > 300K
cap_midoverMid cap and above

VCP tightening filters (add to narrow): ta_volatility_wo3,ta_highlow20d_b0to5h,sh_relvol_u1 — low weekly volatility, near 20-day high, below-average relative volume (contraction signal).

Recipe 3: Unfairly Sold-Off Growth Stocks

Goal: Fundamentally strong companies with recent sharp declines — potential mean reversion candidates.

--filters "fa_sales5years_o5,fa_eps5years_o10,fa_roe_o15,fa_salesqoq_pos,fa_epsqoq_pos,ta_perf_13wdown,ta_highlow52w_10to30-bhx,cap_large,sh_avgvol_o200"
--view overview
Filter CodePurpose
fa_sales5years_o55Y sales growth > 5%
fa_eps5years_o105Y EPS growth > 10%
fa_roe_o15ROE > 15%
fa_salesqoq_posPositive QoQ sales growth
fa_epsqoq_posPositive QoQ EPS growth
ta_perf_13wdownNegative 13-week performance
ta_highlow52w_10to30-bhx10-30% below 52W high
cap_largeLarge cap
sh_avgvol_o200Avg volume > 200K

After review: Switch to valuation view to check P/E and P/S for entry attractiveness.

Recipe 4: Turnaround Stocks

Goal: Companies with previously declining earnings now showing recovery — bottom-fishing with fundamental confirmation.

--filters "fa_eps5years_neg,fa_epsqoq_pos,fa_salesqoq_pos,ta_highlow52w_b30h,ta_perf_13wup,cap_smallover,sh_avgvol_o200"
--view performance
Filter CodePurpose
fa_eps5years_negNegative 5Y EPS growth (prior decline)
fa_epsqoq_posPositive QoQ EPS growth (recovery)
fa_salesqoq_posPositive QoQ sales growth (recovery)
ta_highlow52w_b30hWithin 30% of 52W high (not at bottom)
ta_perf_13wupPositive 13-week performance
cap_smalloverSmall cap and above
sh_avgvol_o200Avg volume > 200K

Recipe 5: Momentum Trade Candidates

Goal: Short-term momentum leaders near 52W highs with increasing volume.

--filters "ta_sma50_pa,ta_sma200_pa,ta_highlow52w_b0to3h,ta_perf_4wup,sh_relvol_o1.5,sh_avgvol_o1000,cap_midover"
--view technical
Filter CodePurpose
ta_sma50_paPrice above 50-day SMA
ta_sma200_paPrice above 200-day SMA
ta_highlow52w_b0to3hWithin 3% of 52W high
ta_perf_4wupPositive 4-week performance
sh_relvol_o1.5Relative volume > 1.5x
sh_avgvol_o1000Avg volume > 1M
cap_midoverMid cap and above

Recipe 6: Theme Screening (AI + Sub-theme Drill-Down)

Goal: Find mid-cap+ AI stocks focused on cloud infrastructure and compute acceleration.

--themes "artificialintelligence"
--subthemes "aicloud,aicompute"
--filters "cap_midover"
--view overview
TypeValuePurpose
ThemeartificialintelligenceAI theme universe
Sub-themeaicloudCloud & Infrastructure vertical
Sub-themeaicomputeCompute & Acceleration vertical
Filtercap_midoverMid cap and above

Multi-theme example: --themes "artificialintelligence,cybersecurity" selects stocks tagged with either theme (OR logic via | grouping).

Tips: Iterative Refinement Pattern

Screening works best as a dialogue, not a one-shot query:

  1. Start broad — use 3-4 core filters to get an initial result set
  2. Review count — if too many results (>100), add tightening filters; if too few (<5), relax constraints
  3. Switch views — start with overview for a quick scan, then switch to financial or valuation for deeper inspection
  4. Layer in technicals — after confirming fundamental quality, add ta_ filters to time entries
  5. Save and iterate — bookmark the URL, then adjust one filter at a time to understand its impact

Resources

  • references/finviz_screener_filters.md — Complete filter code reference with natural language keywords (includes industry code examples; full 142-code list is in the Industry Codes section)
  • scripts/open_finviz_screener.py — URL builder and Chrome opener

平台分布

Codex

37.46%
按下载量换算802

Claude

31.78%
按下载量换算680

Cursor

17.19%
按下载量换算368

Gemini CLI

9.07%
按下载量换算194

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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