A command for researching a company's stock, covering its financial results, market position, trading signals, ownership, and risks. A stock ticker is the short code used to identify a company's shares.

Use it to produce a structured equity report with a recommendation, price target, time period, supporting figures, and potential risks.

It gathers the main information needed to assess a stock instead of leaving the analysis as an unstructured collection of facts.

research — Command for Claude Code by quant-sentiment-ai

Install

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.

Clone the repo

git clone --depth 1 https://github.com/quant-sentiment-ai/claude-equity-research

Made for: Claude Code.

Or install trading-ideas, the plugin that ships this one along with the rest of its 1 command.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/quant-sentiment-ai/claude-equity-research/research/github.svg)](https://agentmods.dev/commands/quant-sentiment-ai/claude-equity-research/research)

Your own site

<a href="https://agentmods.dev/commands/quant-sentiment-ai/claude-equity-research/research"><img src="https://agentmods.dev/badge/commands/quant-sentiment-ai/claude-equity-research/research/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/quant-sentiment-ai/claude-equity-research/research"><img src="https://agentmods.dev/badge/commands/quant-sentiment-ai/claude-equity-research/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,043 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00000	$0.01043
Opus 5.5	$0.00000	$0.00417
Sonnet 5.5	$0.00000	$0.00209
Haiku 4.5	$0.00000	$0.00104

Measured 1mo ago against content hash 624123da23a1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

research 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.

commands/trading-ideas/commands/research.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a professional equity research analyst providing institutional-grade trading analysis. When given a stock ticker, conduct comprehensive research and analysis using this exact framework:

RESEARCH METHODOLOGY

Required Search Strategy (Execute in Parallel):

Financial Performance: Search for recent earnings, revenue growth, margins, key business metrics, and analyst coverage
Market Positioning: Search for peer comparisons, sector performance, competitive analysis, and market share data
Advanced Intelligence: Search for technical analysis, options flow, insider activity, institutional ownership, and regulatory concerns

Data Requirements:

Use specific numbers and percentages where available
Include timeframes for all metrics (YoY, QoQ, etc.)
Cite price targets with analyst firm names when possible
Provide exact financial figures (revenue, margins, EPS, etc.)

OUTPUT FORMAT

Generate analysis using this EXACT structure:

$ARGUMENTS - ENHANCED EQUITY RESEARCH

EXECUTIVE SUMMARY

[BUY/SELL/HOLD] with $[X] price target ([X]% upside/downside) over [timeframe]. [Key catalyst and investment thesis in 1-2 sentences]. [Risk-reward ratio description].

FUNDAMENTAL ANALYSIS

Recent Financial Metrics: [Specific revenue growth %, margins, key business KPIs with exact numbers and timeframes]

Peer Comparison: [Valuation multiples vs competitors with specific P/E, P/S ratios and company names]

Forward Outlook: [Management guidance, analyst consensus, growth projections with specific numbers]

CATALYST ANALYSIS

Near-term (0-6 months): [Specific upcoming events with dates - earnings, product launches, regulatory decisions] Medium-term (6-24 months): [Strategic initiatives, market expansion, competitive positioning changes] Event-driven: [M&A potential, index inclusion, spin-offs, special dividends]

VALUATION & PRICE TARGETS

Current consensus: $[X] (range $[low]-$[high]). Bull case $[X] assumes [specific scenario]. Base case $[X] reflects [scenario]. Bear case $[X] on [risk scenario]. Probability weighting: [X]%/[Y]%/[Z]%.

Read the full file on GitHub · 103 lines

Changes

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 · 103 lines · 0 tokens per session scan A 624123da23a1

Subscribe to this mod's changes

research is a command published in the GitHub repository quant-sentiment-ai/claude-equity-research (746 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,043 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude Code Finance and fintech Research ai-powered claude-code-plugin equity-research financial-analysis

research