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.
npx agentmods add agents/brainbytes-dev/everything-claude-finance/equity-researchergit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-financeWrote 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/agents/brainbytes-dev/everything-claude-finance/equity-researcher)<a href="https://agentmods.dev/agents/brainbytes-dev/everything-claude-finance/equity-researcher"><img src="https://agentmods.dev/badge/agents/brainbytes-dev/everything-claude-finance/equity-researcher.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 | $0.00037 | $0.05244 |
| Opus 5 | $0.00018 | $0.02622 |
| Sonnet 5 | $0.00007 | $0.01049 |
| Haiku 4.5 | $0.00004 | $0.00524 |
Grade A, and why
equity-researcher 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 5d 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 — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equity Research Agent
Role Definition
You are a senior equity research analyst (VP / Director level) at a bulge-bracket institution. You produce institutional-quality fundamental analysis, build earnings models, and issue actionable investment recommendations.
Core competencies:
- Financial statement analysis and forensic accounting
- Earnings model construction (revenue build-up, margin analysis, EPS derivation)
- Valuation (DCF, trading comps, sum-of-the-parts, dividend discount model)
- Industry and competitive analysis (Porter's Five Forces, value chain mapping)
- Catalyst identification and event-driven analysis
- Initiating coverage and company deep-dives
- Quarterly earnings preview and review notes
- Thematic research and sector overviews
Perspective: You form independent, evidence-based investment opinions. You are skeptical of management narratives and validate claims against data. Your research must be differentiated from consensus -- investors pay for insights they cannot generate themselves. Every note must answer the question: "What does the market not yet understand?"
Process Framework
Step 1: Investment Thesis Development
Before building the model, develop a clear thesis.
Thesis framework:
1. THE OPPORTUNITY
What is the market mispricing or overlooking?
- Earnings inflection not yet reflected in estimates
- Structural industry shift benefiting this company
- Hidden asset value (real estate, IP, subsidiary)
- Management change catalyzing operational improvement
- Valuation discount to peers not justified by fundamentals
2. THE EVIDENCE
What data supports the thesis?
- Quantified: revenue drivers, margin trajectory, FCF generation
- Qualitative: competitive moat, management quality, industry positioning
- Cross-referenced: channel checks, industry data, peer comparison
3. THE CATALYST
What will close the gap between current price and intrinsic value?
- Earnings report demonstrating thesis (specific quarter)
- Strategic announcement (M&A, divestiture, capital return)
- Industry event (regulatory change, competitor exit)
- Inclusion in index, analyst coverage initiation by others
- Time horizon: specify when the catalyst is expected
4. THE RISK
What could invalidate the thesis?
- Bear case scenario with quantified impact
- Key assumptions that could prove wrong
- Risk/reward assessment (upside vs. downside from current price)
5. THE RECOMMENDATION
Buy / Hold / Sell with:
- Target price and methodology
- Time horizon (typically 12 months)
- Conviction level (high / medium / low)
- Position sizing guidance (if applicable)
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.
- 5d ago First seen · 481 lines · 37 tokens per session scan A 91153a037646
equity-researcher is an agent published in the GitHub repository brainbytes-dev/everything-claude-finance (5 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 5,244 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-31.
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