New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Quant Research

by monarchjuno in monarchjuno/vibe-investing

Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting…

MITAuto-check passedBusiness, Finance & HR

Install Quant Research

skills CLI
$ npx skills add monarchjuno/vibe-investing --skill quant-research -a claude-code

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

GitHub CLI
$ gh skill install monarchjuno/vibe-investing quant-research --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/monarchjuno/vibe-investing.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/quantitative-analysis/quant-research .claude/skills/quant-research && 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
quant-research
GitHub stars
299
Token cost
~1.1k tokens
SKILL.md length
462 words
Files
5 (incl. references)
Skills in repo
18
Repo updated
First seen
Licence
MIT

At a glance

Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting…

  • Works in 7 steps: Frame the research question → Check economic and asset-pricing logic → Define the signal precisely → …
  • An agent must turn a quantitative hypothesis into a disciplined research process
  • SKILL.md covers Role Definition, Core Principles, Required Analysis Sequence and Required References, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Quant Research is an agent skill from monarchjuno/vibe-investing. Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting, overfitting defense, technical-analysis signals, data hygiene, risk modeling, and performance attribution. Use when an agent must turn a quantitative hypothesis into a disciplined research process, judge whether a signal is economically grounded and statistically robust, separate genuine edge from noise or data-mined…

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/data-hygiene-risk-and-attribution.md`, `references/integrated-framework.md` and `references/output-contract.md`).

It sits in Business, Finance & HR, covering Trading and backtesting and Threat modeling. The repository describes itself as: Vibe Investing Skill Library. The licence is MIT.

When your agent uses it

  • An agent must turn a quantitative hypothesis into a disciplined research process
  • Judge whether a signal is economically grounded and statistically robust
  • Separate genuine edge from noise
  • Data-mined artifacts

Example prompts

  • “/quant-research”

Workflow steps

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

  1. Frame the research question
  2. Check economic and asset-pricing logic
  3. Define the signal precisely
  4. Clean the data and define the test design
  5. Run the backtest and validation stack
  6. Decompose what is really driving returns
  7. Make the research decision

What it can do on your machine

Read from SKILL.md and the folder at commit 15954a6. 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

Quant Research loads about 1.1k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 152 tokens; SKILL.md has 462 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~152
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3k

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 monarchjuno/vibe-investing at commit 15954a6, republished under its MIT licence (© monarchjuno). 462 words, ~1,128 tokens.

Download SKILL.mdSave it as .claude/skills/quant-research/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
quant-research
description
Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting, overfitting defense, technical-analysis signals, data hygiene, risk modeling, and performance attribution. Use when an agent must turn a quantitative hypothesis into a disciplined research process, judge whether a signal is economically grounded and statistically robust, separate genuine edge from noise or data-mined artifacts, and translate the result into a clear go / refine / reject decision.

Quant Research

Role Definition

Act as a rigorous quantitative researcher. Treat every strategy idea as a testable hypothesis, require an economic or behavioral rationale before celebrating performance, and default to robustness checks before optimization.

Core Principles

  • Start with a falsifiable hypothesis, not a backtest screenshot.
  • Distinguish economic rationale from statistical pattern matching.
  • Treat factors and technical signals as candidate return drivers, not truths.
  • Assume markets are adaptive and regime-dependent rather than permanently stationary.
  • Treat data leakage, survivorship bias, look-ahead bias, and selection bias as first-order risks.
  • Prefer robustness, portability, and implementability over in-sample sharpness.
  • Attribute outcomes before claiming alpha.

Required Analysis Sequence

1. Frame the research question
  • Define the hypothesis, target universe, holding period, rebalance logic, and expected transmission mechanism.
  • State whether the idea is a factor, timing signal, cross-sectional selection rule, technical signal, or hybrid.
2. Check economic and asset-pricing logic
  • Decide whether the idea is grounded in factor exposure, behavioral mispricing, structural friction, or market microstructure.
  • Compare the idea against known factor families and asset-pricing intuition before testing.
3. Define the signal precisely
  • Specify inputs, transformations, ranking logic, thresholds, lags, and implementation timing.
  • Ensure the signal can be reproduced without hidden discretion.
4. Clean the data and define the test design
  • Enforce point-in-time correctness.
  • Check survivorship bias, look-ahead bias, stale fundamentals, restatement issues, and missing-data distortions.
  • Define in-sample, out-of-sample, and validation logic before reviewing results.
5. Run the backtest and validation stack
  • Evaluate return, risk, turnover, capacity, cost sensitivity, and benchmark-relative behavior.
  • Stress the idea across subperiods, regimes, universes, and parameter ranges.
  • Use the validation rules in references/validation-and-overfitting-defense.md.
6. Decompose what is really driving returns
  • Determine whether performance comes from intended factor exposure, hidden beta, crowding, leverage, volatility selling, or timing luck.
  • Use attribution and risk decomposition before calling the result alpha.
Show full SKILL.md (168 more words)Show less
7. Make the research decision
  • Conclude with one of: keep researching, conditionally promising, likely overfit, implementation weak, or reject.
  • Use the output structure in references/output-contract.md.

Required References

  • Read references/integrated-framework.md for the full research stack.
  • Read references/validation-and-overfitting-defense.md for robustness and anti-overfitting rules.
  • Read references/data-hygiene-risk-and-attribution.md for data controls, risk modeling, and attribution.
  • Read references/output-contract.md for required output behavior.

Risk and Uncertainty Rules

  • State when the sample is small, regime coverage is thin, or parameter sensitivity is high.
  • State when evidence is suggestive rather than conclusive.
  • Separate empirical stability from economic plausibility.
  • Explicitly note when live implementation friction may erase paper alpha.

Anti-Hallucination Rules

  • Do not fabricate performance metrics, factor loadings, transaction costs, or validation results.
  • Tag statements as [actual], [inference], or [assumption].
  • Use [actual] only for verified data or directly observed test output.
  • Use [inference] for reasoned conclusions drawn from the evidence.
  • Use [assumption] for scenario inputs, cost assumptions, capacity assumptions, or modeling choices.
  • If the data quality or validation setup is weak, lower confidence rather than overstating the result.

© monarchjuno, 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 4 other files (references) in skills/quantitative-analysis/quant-research of monarchjuno/vibe-investing.

  • SKILL.md
  • references/data-hygiene-risk-and-attribution.md
  • references/integrated-framework.md
  • references/output-contract.md
  • references/validation-and-overfitting-defense.md

Open the folder on GitHubat commit 15954a6

Compare with similar skills

Quant Research 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.

Quant Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Quant Research this skillmonarchjuno/vibe-investing299—~1.1kAutomated safety check: PassMIT
Tushare Datazillionare/zillionare3182 repos~2.3kAutomated safety check: PassNone
Tradingview MCPatilaahmettaner/tradingview-mcp4.9k—~1.3kAutomated safety check: PassMIT
Digital Oraclekomako-workshop/digital-oracle867—~5.9kAutomated safety check: PassMIT
Polyclawchainstacklabs/polyclaw3601 repos~2kAutomated safety check: PassApache-2.0
Markdownfacioquo/stock-indicators-dotnet1.2k—~812Automated safety check: PassApache-2.0

Similar skills

  • Tushare Data

    zillionare/zillionare

    面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。

    318 GitHub starsUsed in 2 repos~2.3k tokens
    Business, Finance & HRAuto-check passed

  • Tradingview MCP

    atilaahmettaner/tradingview-mcp

    AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…

    4.9k GitHub stars~1.3k tokensUpdated yesterday
    Business, Finance & HRAuto-check passed

  • Digital Oracle

    komako-workshop/digital-oracle

    Answer prediction questions using market trading data, not opinions.

    867 GitHub stars~5.9k tokensUpdated 2 mo ago
    Business, Finance & HRAuto-check passed

  • Polyclaw

    chainstacklabs/polyclaw

    Trade on Polymarket via split + CLOB execution. An agent skill from chainstacklabs/polyclaw.

    360 GitHub starsUsed in 1 repo~2k tokens
    Business, Finance & HRAuto-check passed

  • Markdown

    facioquo/stock-indicators-dotnet

    Format and lint Markdown in this repository against GitHub Flavored Markdown and its markdownlint-cli2 configuration — headers, lists, code fences, callouts (VitePress containers on docs-site pages…

    1.2k GitHub stars~812 tokensUpdated yesterday
    Business, Finance & HRAuto-check passed

  • Openmobius Skill

    MobiusQuant/OpenMobius-skill

    Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave.

    695 GitHub stars~7.2k tokensUpdated 1 mo ago
    Business, Finance & HRAuto-check passed

More from monarchjuno/vibe-investing

All 18 skills in this repo

  • Openbb Data Fetcher

    monarchjuno/vibe-investing

    Fetch financial, market, economic, fundamental, news, options, crypto, ETF, index, and macro data through the OpenBB Python interface instead of the OpenBB MCP server.

    299 GitHub stars~2.9k tokensUpdated 5 mo ago
    Auto-check: notes

  • Company Analysis

    monarchjuno/vibe-investing

    Automatically analyze a company or company-related finance situation from a short user prompt such as a company name, "analyze [company]," M&A, LBO, IPO, debt, credit, or risk requests.

    299 GitHub stars~1.3k tokensUpdated 5 mo ago
    Auto-check passed

  • Financial Report

    monarchjuno/vibe-investing

    Package investing analysis into a publishable financial-report style with institutional section flow, thesis framing, valuation context, catalysts, risks, tables, and financial charts.

    299 GitHub stars~1.3k tokensUpdated 5 mo ago
    Auto-check passed

  • Traditional Market Analysis

    monarchjuno/vibe-investing

    Analyze traditional market context for an investment case by identifying the current regime, cycle position, market expectations, liquidity and macro transmission, positioning and crowd psychology…

    299 GitHub stars~1.1k tokensUpdated 5 mo ago
    Auto-check passed

  • Aswath Damodaran

    monarchjuno/vibe-investing

    Analyze a company, sector situation, or valuation question through Aswath Damodaran's lens.

    299 GitHub stars~788 tokensUpdated 5 mo ago
    Auto-check passed

  • Ben Graham

    monarchjuno/vibe-investing

    Analyze an investment through Benjamin Graham's conservative value-investing lens.

    299 GitHub stars~689 tokensUpdated 5 mo ago
    Auto-check passed

Categories

Questions about Quant Research

What does Quant Research do?

Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting…. Quant Research is an agent skill from monarchjuno/vibe-investing. Design, evaluate, and challenge quantitative investment ideas using an integrated research workflow that covers factor and asset-pricing logic, signal generation, signal validation, backtesting, overfitting defense, technical-analysis signals, data hygiene, risk modeling, and performance attribution.

When should I use Quant Research?

Quant Research fits situations like: an agent must turn a quantitative hypothesis into a disciplined research process; judge whether a signal is economically grounded and statistically robust; separate genuine edge from noise; data-mined artifacts.

How do I install Quant Research in Claude Code?

Run `npx skills add monarchjuno/vibe-investing --skill quant-research -a claude-code`. Or copy the skill folder (skills/quantitative-analysis/quant-research in monarchjuno/vibe-investing) into .claude/skills/quant-research in your project. Claude Code loads it when a task matches its description.

How do I install Quant Research in Codex?

Run `npx skills add monarchjuno/vibe-investing --skill quant-research -a codex`. Or copy the skill folder (skills/quantitative-analysis/quant-research in monarchjuno/vibe-investing) into .agents/skills/quant-research in your project. Codex loads it when a task matches its description.

Can I use Quant Research 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 monarchjuno/vibe-investing --skill quant-research -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-research, .gemini/skills/quant-research, .github/skills/quant-research and .opencode/skills/quant-research in your project.

What does Quant Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Quant Research is instructions for the agent only.

Does Quant Research 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 Quant Research 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 Quant Research use?

Quant Research 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 Quant Research use?

About 1.1k tokens (SKILL.md is roughly 4.5k 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 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Quant Research?

Skills that share tags, products or a category with Quant Research: Tushare Data (zillionare/zillionare, 318 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 4.9k stars), Digital Oracle (komako-workshop/digital-oracle, 867 stars) and Polyclaw (chainstacklabs/polyclaw, 360 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Quant Research?

monarchjuno (a GitHub user) maintains it in monarchjuno/vibe-investing, which has 299 GitHub stars. The repository holds 18 skills in this directory. The repository was last updated on May 10, 2026.

Source: monarchjuno/vibe-investing on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.