A bull, bear, and base case analysis for a company. It models optimistic, pessimistic, and middle-ground outcomes and compares their estimated values with the current stock price.

Use it to compare scenarios using historical financial results, current market data, operating assumptions, and implied upside or downside.

It shows how different business assumptions could affect a company’s value instead of relying on one forecast.

bull-bear — Skill for Claude Code by daloopa

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.

Any agent

npx skills add daloopa/daloopa-plugin-claude --skill bull-bear

Clone the repo

git clone --depth 1 https://github.com/daloopa/daloopa-plugin-claude

Made for: Claude Code.

Or install daloopa, the plugin that ships this one along with the rest of its 21 skills, 2 MCP servers.

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/skills/daloopa/daloopa-plugin-claude/bull-bear/github.svg)](https://agentmods.dev/skills/daloopa/daloopa-plugin-claude/bull-bear)

Your own site

<a href="https://agentmods.dev/skills/daloopa/daloopa-plugin-claude/bull-bear"><img src="https://agentmods.dev/badge/skills/daloopa/daloopa-plugin-claude/bull-bear/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/skills/daloopa/daloopa-plugin-claude/bull-bear"><img src="https://agentmods.dev/badge/skills/daloopa/daloopa-plugin-claude/bull-bear.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,705 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 100% copy Near-identical to another mod 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	$0.00015	$0.01705
Opus 5	$0.00008	$0.00852
Sonnet 5	$0.00003	$0.00341
Haiku 4.5	$0.00002	$0.00170

Measured 12d ago against content hash 4474c5f24fb7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bull-bear 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 12d 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.

Origin

This is a copy

100% identical to bull-bear — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/bull-bear/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Build a bull/bear/base case scenario framework for the company specified by the user: $ARGUMENTS

Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.

Follow these steps:

1. Company Lookup

Look up the company by ticker using discover_companies. Capture:

company_id
latest_calendar_quarter — anchor for all period calculations below (see ../data-access.md Section 1.5)
latest_fiscal_quarter
Firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5

1b. Current Stock Price

Get the current stock price using get_stock_prices (see ../data-access.md Section 1.7). Pass company_id and dates for the 3 most recent calendar days — use the most recent returned close price. This is the anchor for scenario comparison: each scenario's implied value will be compared against this price to show upside/downside.

2. Historical Financial Baseline

Calculate 8 quarters backward from latest_calendar_quarter. Pull:

Revenue
Gross Profit / Gross Margin %
Operating Income / Operating Margin %
EBITDA (if not reported, compute as Operating Income + D&A — label "(calc.)")
Net Income
Diluted EPS
Operating Cash Flow
CapEx
Free Cash Flow (compute as OCF - CapEx — label "(calc.)")
Segment-level revenue breakdowns
Geographic revenue breakdowns

Compute trailing 4-quarter totals for revenue, EBITDA, net income, EPS, and FCF — these are the baseline the scenarios build from.

Flag any one-time items that distort quarters.

3. Key Operating KPIs

First, think about what the most important KPIs are for THIS specific company based on its business model and what drives its valuation. For example:

SaaS/cloud: ARR, net revenue retention, RPO/cRPO, customers >$100K
Consumer tech: DAU/MAU, ARPU, engagement metrics, installed base, paid subscribers
E-commerce/marketplace: GMV, take rate, active buyers/sellers, order frequency
Retail: same-store sales, store count, average ticket, transactions
Telecom/media: subscribers, churn, ARPU, content spend
Hardware: units shipped, ASP, attach rate
Financial services: AUM, NIM, loan growth, credit quality metrics
Pharma/biotech: pipeline stage, patient starts, scripts, market share

Read the full file on GitHub · 134 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.

12d ago First seen · 134 lines · 15 tokens per session scan A 4474c5f24fb7

Subscribe to this mod's changes

bull-bear is a skill published in the GitHub repository daloopa/daloopa-plugin-claude (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,705 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bull-bear, differing in 0 lines, and is treated as a copy.

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