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.
git clone --depth 1 https://github.com/quant-sentiment-ai/claude-equity-researchWrote 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.
[](https://agentmods.dev/commands/quant-sentiment-ai/claude-equity-research/research)<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="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.00522 |
| Sonnet 5 | $0.00000 | $0.00209 |
| Haiku 4.5 | $0.00000 | $0.00104 |
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 8d 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.
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]%.
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.
- 8d ago First seen · 103 lines · 0 tokens per session scan A 624123da23a1
research is a command published in the GitHub repository quant-sentiment-ai/claude-equity-research (712 stars, last pushed today), 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.
Other commands, from other repositories
tax-review
Tax-filing compliance check — invokes tax-reviewer to produce TM-tax-{slug}.md with MeF e-file schema, Form 8879, PTIN/Circular 230, and IRC §7216 consent gaps.
compare
Compare two or more payment providers head to head for a concrete situation — real fees on a real amount, coverage, onboarding time, and what PagoKit can actually build for each.
add
Add a second payment provider alongside an existing integration, without touching the one already running.
search-flights
Search award flight availability across airlines.
cogs-sentinel
A command that runs the AI product cost check using provider prices, expected token use, call volume, and price per customer. It reports typical and high-use margins, the effect of free-user misuse, and a green, conditional-go, or red decision.
pm-burn
Cost burn-rate — LLM cost over the last N days vs a human-equivalent baseline (savingsx). Flags cost outliers and abnormal-burn agents.