A financial scenario framework that estimates optimistic, pessimistic, and middle outcomes for a company.

Use it to build bull, bear, and base cases from historical financial data, operating assumptions, and implied company value.

It organizes company research and assumptions so possible outcomes can be compared with the current share price.

How daloopa/investing configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to daloopa/investing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/daloopa/investing/main/.claude/skills/bull-bear/SKILL.md

Clone the repo

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

Made for: Claude Code.

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

Your own site

<a href="https://agentmods.dev/skills/daloopa/investing/bull-bear"><img src="https://agentmods.dev/badge/skills/daloopa/investing/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/investing/bull-bear"><img src="https://agentmods.dev/badge/skills/daloopa/investing/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 1,848 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. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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	$0.00015	$0.01848
Opus 5.5	$0.00006	$0.00739
Sonnet 5.5	$0.00003	$0.00370
Haiku 4.5	$0.00002	$0.00185

Measured 7d ago against content hash c6d5a0067bf0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 7d 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

Copies of this mod

1 near-identical copy found in the catalogue:

bull-bear — 100% identical, 3 lines differ

.claude/skills/bull-bear/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 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 · 137 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.

7d ago Changed · +3 lines c6d5a0067bf0
2mo 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/investing (488 stars, last pushed 8d ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,848 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.

Finance and fintech

bull-bear next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
bull-beardaloopa/investing	Skill	488	8d ago	Apache-2.0	A	15	Claude Code
sector-rotationHKUDS/Vibe-Trading	Skill	38k	yesterday	MIT	A	39	Claude Code, Codex
twitter-readerhimself65/finance-skills	Skill	3.5k	2d ago	MIT	A	110	Claude Code
strategy-pivot-designertradermonty/claude-trading-skills	Skill	3.7k	2d ago	MIT	A	28	Claude Code, Codex
energy-procurementhashgraph-online/awesome-codex-plugins	Skill	1.5k	yesterday	Apache-2.0	A	93	Claude Code, Codex
airdrop-hunting-guidenirholas/three.ws	Skill	269	yesterday	Apache-2.0	A	54	Claude Code, Codex
multi-expert-analyzerdigoal/blog	Skill	8.6k	2d ago	GPL-2.0	A	292	Codex

investing: Skill for Claude Code