equity-researcher

equity-researcher is an agent for coding agents from brainbytes-dev/everything-claude-finance. It costs 37 tokens per session (5,244 once invoked), scanned A, original, MIT.

An AI research assistant for studying companies, their financial results, industries, and stock valuations. It can produce buy, sell, or hold recommendations based on that analysis.

In plain words
What is it for?
Analyzing financial statements, modeling earnings, comparing valuations, reviewing industries, and preparing company or sector research.
Why use it?
It helps organize company research and turn financial data into an investment view instead of relying only on management claims or market consensus.

Agent

Part of the everything-claude-finance plugin — 13 skills, 22 commands, 20 agents shipped together

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.

agentmods
npx agentmods add agents/brainbytes-dev/everything-claude-finance/equity-researcher
Clone the repo
git clone --depth 1 https://github.com/brainbytes-dev/everything-claude-finance

Or install everything-claude-finance, the plugin that ships this one along with the rest of its 13 skills, 22 commands, 20 agents.

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.

agentmods badge for equity-researcher

README.md
[![agentmods](https://agentmods.dev/badge/agents/brainbytes-dev/everything-claude-finance/equity-researcher.svg)](https://agentmods.dev/agents/brainbytes-dev/everything-claude-finance/equity-researcher)
Your own site
<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>
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,244 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce 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

Measured 5d ago against content hash 91153a037646, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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.

agents/equity-researcher.md · 481 lines

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)

Read the full file on GitHub · 481 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.

  1. 5d ago First seen · 481 lines · 37 tokens per session scan A 91153a037646

Subscribe to this mod's changes

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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