AI-Trader Market Intel
HKUDS/AI-Trader
Reads AI-Trader's read-only market snapshots, grouped financial news and events board through its market-intel endpoints, for context before trading or posting.
Screens a stock universe with a six-factor model, scores value, momentum, quality, low volatility, size and growth, ranks by composite score and notes which factors suit the macro regime.
Install Quantitative Factor Screener
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Geeksfino/finskills quant-factor-screener --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/US-market/quant-factor-screener .claude/skills/quant-factor-screener && rm -rf skills-srcUse ~/.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/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .claude/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screenerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Geeksfino/finskills quant-factor-screener --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/US-market/quant-factor-screener .agents/skills/quant-factor-screener && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .agents/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Geeksfino/finskills quant-factor-screener --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/US-market/quant-factor-screener .cursor/skills/quant-factor-screener && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .cursor/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Geeksfino/finskills.git --path US-market/quant-factor-screener--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Geeksfino/finskills quant-factor-screener --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/US-market/quant-factor-screener .gemini/skills/quant-factor-screener && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .gemini/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Geeksfino/finskills quant-factor-screenerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .github/skills && cp -r skills-src/US-market/quant-factor-screener .github/skills/quant-factor-screener && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .github/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Geeksfino/finskills --skill quant-factor-screener -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Geeksfino/finskills quant-factor-screener --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Geeksfino/finskills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/US-market/quant-factor-screener .opencode/skills/quant-factor-screener && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "quant-factor-screener" agent skill from https://github.com/Geeksfino/finskills/tree/main/US-market/quant-factor-screener into .opencode/skills/quant-factor-screener/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "quant-factor-screener", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
quant-factor-screener- GitHub stars
- 282
- Token cost
- ~1.3k tokens
- SKILL.md length
- 559 words
- Files
- 4 (incl. references)
- Skills in repo
- 30
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Screens a stock universe with a six-factor model, scores value, momentum, quality, low volatility, size and growth, ranks by composite score and notes which factors suit the macro regime.
- Works in 6 steps: Define Parameters → Calculate Factor Scores → Composite Score → …
- Screening a stock universe on value, momentum and quality factors
- SKILL.md covers Workflow, Data Enhancement and Important Guidelines
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
The agent plays a quantitative equity analyst. It first confirms parameters with you: the universe (S&P 500, Russell 1000, Russell 3000 or a custom list, defaulting to Russell 1000), which factors to use, equal or custom weights, sector-neutral or unconstrained ranking, how many results to return, with a default of 20, the macro regime and any exclusions. Each stock is then scored on every factor using the metrics named in the skill, such as earnings yield and EV/EBITDA for value, 12-1 month return for momentum, ROE for quality, realized volatility and beta for low volatility, market cap for size and revenue growth for growth.
Within the sector or universe, raw metrics are ranked and converted to percentile scores from 0 to 100, sub-metrics are combined into a factor score, and a composite score is the weighted sum of the six, ranked from highest to lowest. A factor timing step then maps the current macro regime, from early expansion to recession and recovery, to favored and disfavored factors. Methodology and output template notes are bundled under `references/`, and the excerpt is cut off after the timing table.
When your agent uses it
- Screening a stock universe on value, momentum and quality factors
- Building a ranked list for a smart beta or factor investing study
- Checking which factors tend to be favored in the current macro regime
Example prompts
- “Screen the Russell 1000 on all six factors and give me the top 20, sector-neutral.”
- “Run a value and quality screen on the S&P 500, excluding financials.”
- “Which factors does the skill favor in a slowdown, and how does that change the ranking?”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 8722415. 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
No scripts in the folder and no shell commands in SKILL.md.
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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Quantitative Factor Screener loads about 1.3k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 559 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from Geeksfino/finskills at commit 8722415, republished under its Apache-2.0 licence (© Geeksfino). 559 words, ~1,308 tokens.
Download SKILL.mdSave it as
.claude/skills/quant-factor-screener/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- quant-factor-screener
- description
- Systematic multi-factor stock screening using formal factor models to identify stocks with favorable factor exposures. Use when the user asks about factor investing, multi-factor screening, value/momentum/quality factor analysis, factor scoring, factor timing, smart beta strategies, quantitative stock screening, or systematic equity selection based on academic factors.
- license
- Apache-2.0
Quantitative Factor Screener
Act as a quantitative equity analyst. Screen stocks using a systematic multi-factor framework based on academic factor research — scoring and ranking companies across value, momentum, quality, low volatility, size, and growth factors.
Workflow
Step 1: Define Parameters
Confirm with the user:
| Input | Options | Default |
|---|---|---|
| Universe | S&P 500 / Russell 1000 / Russell 3000 / Custom | Russell 1000 |
| Factors | All 6 or specific factors | All |
| Factor weights | Equal or custom | Equal weight |
| Sector constraints | Sector-neutral or unconstrained | Sector-neutral |
| Number of results | Top N stocks | Top 20 |
| Macro regime | Current assessment for factor timing | Auto-detect |
| Exclusions | Sectors, industries, specific stocks | None |
Step 2: Calculate Factor Scores
Score every stock in the universe on each factor. See references/factor-methodology.md for detailed definitions.
| Factor | Primary Metrics | Weight in Composite |
|---|---|---|
| Value | Earnings yield, book/price, FCF yield, EV/EBITDA | 1/6 (or custom) |
| Momentum | 12-1 month price return, earnings revision momentum | 1/6 |
| Quality | ROE, earnings stability, low leverage, accruals | 1/6 |
| Low volatility | Realized volatility (1Y), beta, downside deviation | 1/6 |
| Size | Market capitalization (smaller = higher score) | 1/6 |
| Growth | Revenue growth, earnings growth, margin expansion | 1/6 |
For each factor:
- Calculate raw metric for each stock
- Rank within sector (if sector-neutral) or universe (if unconstrained)
- Convert ranks to percentile scores (0–100)
- Combine sub-metrics into composite factor score
Step 3: Composite Score
Composite Score = Σ (Factor Weight × Factor Score)Rank all stocks by composite score from highest to lowest.
Step 4: Factor Timing Assessment
Assess the current macro regime and its implications for factor performance. See references/factor-methodology.md.
| Macro Regime | Favored Factors | Disfavored Factors |
|---|---|---|
| Early expansion | Size, Momentum | Low Volatility |
| Late expansion | Quality, Value | Size |
| Slowdown | Low Volatility, Quality | Momentum, Size |
| Recession | Low Volatility, Value (deep) | Momentum, Growth |
| Recovery | Value, Size, Momentum | Low Volatility |
Based on the current regime, provide a factor timing overlay that adjusts weights.
Step 5: Factor Crowding Analysis
Assess whether popular factors are overcrowded:
| Signal | Crowded | Uncrowded |
|---|---|---|
| Valuation spread (cheap vs expensive within factor) | Narrow | Wide |
| Factor return correlation | High (many following same signal) | Low |
| ETF flows into factor | Surging inflows | Outflows |
| Media/analyst attention | Heavily discussed | Ignored |
Flag factors that appear crowded — returns may be compressed.
Show full SKILL.md (216 more words)Show less
Step 6: Present Results
Format per references/output-template.md:
- Macro Regime Assessment — Current regime and factor timing view
- Factor Crowding Dashboard — Which factors are crowded/uncrowded
- Top Picks Table — Top N stocks with individual factor scores and composite
- Sector Distribution — How the top picks distribute across sectors
- Factor Exposure Summary — What the resulting list is tilted toward
- Individual Stock Cards — Brief profile for each top pick
- Risk Considerations — Factor drawdown history and current risks
- Disclaimers
Data Enhancement
For live market data to support this analysis, use the FinData Toolkit skill (findata-toolkit-us). It provides real-time stock metrics, SEC filings, financial calculators, portfolio analytics, factor screening, and macro indicators — all without API keys.
Important Guidelines
- Factors are not magic: Factors have long periods of underperformance. Value underperformed for a decade (2010–2020). Momentum crashes periodically. Set expectations.
- Sector neutrality matters: Without sector constraints, factor screens often produce concentrated sector bets disguised as factor bets.
- Backtest ≠ future: All factor research is backward-looking. Factors may be arbitraged away as they become popular.
- Multi-factor is more robust: No single factor works all the time. Combining factors reduces drawdowns and smooths returns.
- Transaction costs: Momentum strategies require higher turnover. Factor in realistic transaction costs.
- Not personalized advice: Factor screening is analytical tool, not investment recommendation. Individual circumstances vary.
© Geeksfino, Apache-2.0. 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 3 other files (references) in US-market/quant-factor-screener of Geeksfino/finskills.
- SKILL.md
- LICENSE.txt
- references/factor-methodology.md
- references/output-template.md
Open the folder on GitHubat commit 8722415
Compare with similar skills
Quantitative Factor Screener next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Quantitative Factor Screener this skillGeeksfino/finskills | 282 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| AI-Trader Market IntelHKUDS/AI-Trader | 23k | — | ~1.1k | Automated safety check: Pass | None | |
| Eastmoney Market DataHKUDS/Vibe-Trading | 35k | — | ~1k | Automated safety check: Pass | MIT | |
| Stock Deep Analysis Workflowwbh604/UZI-Skill | 7.1k | — | ~9.1k | Automated safety check: Notes | MIT | |
| Zhengxi Fund Manager Views Librarylyra81604/zhengxi-views | 1.8k | — | ~1.6k | Automated safety check: Pass | MIT | |
| SEC EDGAR Filings FetcherHKUDS/Vibe-Trading | 35k | — | ~1.4k | Automated safety check: Pass | MIT |
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Questions about Quantitative Factor Screener
What does Quantitative Factor Screener do?
Screens a stock universe with a six-factor model, scores value, momentum, quality, low volatility, size and growth, ranks by composite score and notes which factors suit the macro regime. The agent plays a quantitative equity analyst. It first confirms parameters with you: the universe (S&P 500, Russell 1000, Russell 3000 or a custom list, defaulting to Russell 1000), which factors to use, equal or custom weights, sector-neutral or unconstrained ranking, how many results to return, with a default of 20, the macro regime and any exclusions.
When should I use Quantitative Factor Screener?
Quantitative Factor Screener fits situations like: screening a stock universe on value, momentum and quality factors; building a ranked list for a smart beta or factor investing study; checking which factors tend to be favored in the current macro regime.
How do I install Quantitative Factor Screener in Claude Code?
Run `npx skills add Geeksfino/finskills --skill quant-factor-screener -a claude-code`. Or copy the skill folder (US-market/quant-factor-screener in Geeksfino/finskills) into .claude/skills/quant-factor-screener in your project. Claude Code loads it when a task matches its description.
How do I install Quantitative Factor Screener in Codex?
Run `npx skills add Geeksfino/finskills --skill quant-factor-screener -a codex`. Or copy the skill folder (US-market/quant-factor-screener in Geeksfino/finskills) into .agents/skills/quant-factor-screener in your project. Codex loads it when a task matches its description.
Can I use Quantitative Factor 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 Geeksfino/finskills --skill quant-factor-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/quant-factor-screener, .gemini/skills/quant-factor-screener, .github/skills/quant-factor-screener and .opencode/skills/quant-factor-screener in your project.
What does Quantitative Factor Screener need to run?
SKILL.md names no scripts, command-line tools or credentials: Quantitative Factor Screener is instructions for the agent only.
Does Quantitative Factor 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 Quantitative Factor 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. Review the folder before installing.
What licence does Quantitative Factor Screener use?
Quantitative Factor Screener is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Quantitative Factor Screener use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 3.6k tokens, read only when the agent opens those files.
What are the alternatives to Quantitative Factor Screener?
Skills that share tags, products or a category with Quantitative Factor Screener: AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars), Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars) and Zhengxi Fund Manager Views Library (lyra81604/zhengxi-views, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Quantitative Factor Screener?
Geeksfino (a GitHub user) maintains it in Geeksfino/finskills, which has 282 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on March 5, 2026.
Source: Geeksfino/finskills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.