A workflow for comparing several public companies across an industry using their stock ticker symbols. It gathers company details and compares financial results across aligned calendar quarters.

It helps compare revenue, profit, margins, operating income, and EBITDA across multiple companies over several quarters.

It reduces the manual work of finding comparable figures when companies use different fiscal calendars.

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/industry/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/industry/github.svg)](https://agentmods.dev/skills/daloopa/investing/industry)

Your own site

<a href="https://agentmods.dev/skills/daloopa/investing/industry"><img src="https://agentmods.dev/badge/skills/daloopa/investing/industry/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/industry"><img src="https://agentmods.dev/badge/skills/daloopa/investing/industry.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 10 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,785 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	$0.00010	$0.01785
Opus 5.5	$0.00004	$0.00714
Sonnet 5.5	$0.00002	$0.00357
Haiku 4.5	$0.00001	$0.00178

Measured 7d ago against content hash 236c33571f89, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

industry 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:

industry — 100% identical, 3 lines differ

.claude/skills/industry/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Perform an industry comparison across the companies specified by the user: $ARGUMENTS

The user will provide multiple tickers separated by spaces (e.g., "AAPL MSFT GOOG AMZN").

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 Lookups

Look up all provided tickers using discover_companies. For each company, capture:

company_id
latest_calendar_quarter — use the earliest latest_calendar_quarter across all companies as the anchor for period calculations (see ../data-access.md Section 1.5)
latest_fiscal_quarter
Note each company's fiscal year end — this is critical for calendar quarter alignment
Firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5

2. Comparable Financial Metrics

Calculate 8 quarters backward from the anchor latest_calendar_quarter. For each company, find and pull these metrics:

Income Statement:

Revenue
Gross Profit / Gross Margin
Operating Income / Operating Margin
EBITDA (if not reported, compute as Operating Income + D&A — label "(calc.)")
Net Income / Net Margin
Diluted EPS
R&D Expense
Stock-Based Compensation (SBC)

Cash Flow:

Operating Cash Flow
CapEx (Purchases of property, plant and equipment)
Free Cash Flow (compute as OCF - CapEx — label "(calc.)")
D&A (needed for EBITDA calc if not directly reported)

For any derived/computed metric, mark it with "(calc.)" so the reader knows it's not directly sourced.

3. Company-Specific KPIs

First, think about what KPIs matter for the specific industry being compared. Use the full sector taxonomy to guide discovery:

SaaS/Cloud: ARR, net revenue retention, RPO/cRPO, customers >$100K, cloud gross margin
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, installed base
Financial Services: AUM, NIM, loan growth, credit quality metrics, fee income ratio
Pharma/Biotech: pipeline stage, patient starts, scripts, market share
Industrials/Energy: backlog, book-to-bill, utilization, production volumes, reserves

Read the full file on GitHub · 118 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 236c33571f89
2mo ago First seen · 115 lines · 10 tokens per session scan A 58eb4c069f2f

Subscribe to this mod's changes

industry is a skill published in the GitHub repository daloopa/investing (488 stars, last pushed 8d ago), licensed Apache-2.0. It adds 10 tokens to every session and 1,785 once invoked, about $0.0000 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.

Analytics and metrics Finance and fintech

industry next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
industrydaloopa/investing	Skill	488	8d ago	Apache-2.0	A	10	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