Agent skill

Finviz Screener

by tradermonty in tradermonty/claude-trading-skills

Build and open FinViz screener URLs from natural language requests.

MITAuto-check passedBusiness, Finance & HR

Install Finviz Screener

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill finviz-screener -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills finviz-screener --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/finviz-screener .claude/skills/finviz-screener && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
finviz-screener
GitHub stars
3k
Used in
1 other repo
Token cost
~3.2k tokens
SKILL.md length
1,172 words
Files
6 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Build and open FinViz screener URLs from natural language requests.

  • Works in 5 steps: Load Filter Reference → Interpret User Request → Present Filter Selection → …
  • User wants to screen stocks
  • SKILL.md covers Overview, When to Use This Skill, Workflow and Usage Recipes, plus 1 more section
  • Runs Python scripts from its folder; calls python3; needs FINVIZ_API_KEY

What it does

Finviz Screener is an agent skill from tradermonty/claude-trading-skills. Build and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE").

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/finviz_screener_filters.md`, `scripts/open_finviz_screener.py` and `scripts/tests/conftest.py`).

It sits in Business, Finance & HR, covering Stock and market analysis. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

  • User wants to screen stocks
  • Find stocks matching criteria
  • Filter by fundamentals
  • Asks to open FinViz with specific conditions

Example prompts

  • “高配当で成長している小型株を探したい”
  • “Find oversold large caps with high ROE”
  • “/finviz-screener”

Requirements

  • Python 3
  • A credential in FINVIZ_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Load Filter Reference
  2. Interpret User Request
  3. Present Filter Selection
  4. Execute Script
  5. Report Results

What it can do on your machine

Read from SKILL.md and the folder at commit c8d58f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • FINVIZ_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Finviz Screener loads about 3.2k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 1,172 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~23k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit c8d58f0, republished under its MIT licence (© tradermonty). 1,172 words, ~3,195 tokens.

Download SKILL.mdSave it as .claude/skills/finviz-screener/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
finviz-screener
description
Build and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE").

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:

bash
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:

markdown
| 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:

bash
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.

Show full SKILL.md (442 more words)Show less
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

© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/finviz-screener of tradermonty/claude-trading-skills.

  • SKILL.md
  • references/finviz_screener_filters.md
  • requirements.txt
  • scripts/open_finviz_screener.py
  • scripts/tests/conftest.py
  • scripts/tests/test_open_finviz_screener.py

Open the folder on GitHubat commit c8d58f0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Finviz Screener

What does Finviz Screener do?

Build and open FinViz screener URLs from natural language requests. Finviz Screener is an agent skill from tradermonty/claude-trading-skills. Build and open FinViz screener URLs from natural language requests.

When should I use Finviz Screener?

Finviz Screener fits situations like: user wants to screen stocks; find stocks matching criteria; filter by fundamentals; asks to open FinViz with specific conditions.

How do I install Finviz Screener in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill finviz-screener -a claude-code`. Or copy the skill folder (skills/finviz-screener in tradermonty/claude-trading-skills) into .claude/skills/finviz-screener in your project. Claude Code loads it when a task matches its description.

How do I install Finviz Screener in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill finviz-screener -a codex`. Or copy the skill folder (skills/finviz-screener in tradermonty/claude-trading-skills) into .agents/skills/finviz-screener in your project. Codex loads it when a task matches its description.

Can I use Finviz Screener in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill finviz-screener -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/finviz-screener, .gemini/skills/finviz-screener, .github/skills/finviz-screener and .opencode/skills/finviz-screener in your project.

What does Finviz Screener need to run?

Going by SKILL.md and its folder, Finviz Screener needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named FINVIZ_API_KEY. Our summary lists: Python 3; A credential in FINVIZ_API_KEY.

Does Finviz Screener access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Finviz Screener safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Finviz Screener use?

Finviz Screener is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Finviz Screener use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 19k tokens, read only when the agent opens those files.

What are the alternatives to Finviz Screener?

Skills that share tags, products or a category with Finviz Screener: Stock API (zhangxiangliang/stock-api, 2k stars), Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars) and Digital Oracle (komako-workshop/digital-oracle, 878 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Finviz Screener?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.