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.
curl -O https://raw.githubusercontent.com/daloopa/investing/main/.claude/skills/bull-bear/SKILL.mdgit clone --depth 1 https://github.com/daloopa/investingWrote 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/skills/daloopa/investing/bull-bear)<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.
<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>- NVIDIA SkillSpector pass
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 |
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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bull-bear — 100% identical, 0 lines differ
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_idlatest_calendar_quarter— anchor for all period calculations below (see../data-access.mdSection 1.5)latest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
../data-access.mdSection 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
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.
- 10d ago First seen · 134 lines · 15 tokens per session scan A 4474c5f24fb7
bull-bear is a skill published in the GitHub repository daloopa/investing (487 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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