What is earnings-prep?

A report prepared before a company announces its earnings, meaning its latest financial results. It highlights what an equity analyst should watch when the announcement arrives.

Use it to prepare for an earnings announcement by researching the company, reviewing primary reports, and identifying points to monitor.

It focuses attention on the key figures, questions, and risks most relevant to interpreting the results.

earnings-prep — 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 earnings-prep

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

Your own site

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

Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,001 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	$0.00018	$0.04001
Opus 5	$0.00009	$0.02001
Sonnet 5	$0.00004	$0.00800
Haiku 4.5	$0.00002	$0.00400

Measured 10d ago against content hash 98c10749445b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

earnings-prep 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.

Origin

This is a copy

100% identical to earnings-prep — 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/earnings-prep/SKILL.md · 262 lines

How it starts

The opening of the file, as written. The whole thing — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generate a pre-earnings preparation report for the company specified by the user: $ARGUMENTS

This is the note a L/S equity analyst reads the night before a company reports — it tells them exactly what to focus on when the print drops.

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.

Source quality (MANDATORY, applies to every web search in this skill): Follow ../data-access.md Section 2.5 — cite only primary sources (SEC filings, IR pages, press releases, transcripts) and Tier-1 financial press (Reuters, Bloomberg, WSJ, FT). Never use or cite Yahoo Finance editorial, Benzinga, Seeking Alpha, Motley Fool, Zacks, TipRanks, StockTwits, Reddit, or similar aggregators/blogs.

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

Determine the upcoming quarter — the one AFTER latest_calendar_quarter. This is the quarter the company is about to report. All analysis is oriented around preparing the analyst for this print.

2. Last Quarter Recap

Pull the most recent quarter's full financials from Daloopa. Calculate 4 quarters backward from latest_calendar_quarter (for YoY context).

Pull:

Revenue, Gross Profit, Operating Income, EBITDA, Net Income, Diluted EPS
Operating Cash Flow, CapEx, FCF (calc.)
Segment/product revenue breakdown
Company-specific KPIs (use the business-model taxonomy: SaaS → ARR/NRR/RPO; Consumer → DAU/ARPU; E-commerce → GMV/take rate; etc.)

Summarize the story of last quarter in 3-5 bullets:

What beat expectations (guidance or consensus)?
What missed or disappointed?
What was the stock reaction? (use get_stock_prices per ../data-access.md Section 1.7 to get the actual next-day move; supplement with WebSearch for narrative context if needed)
What narrative emerged from the call? (e.g., "AI monetization acceleration," "margin expansion story intact," "consumer weakness")
What was the single most debated metric?

Read the full file on GitHub · 262 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.

10d ago First seen · 262 lines · 18 tokens per session scan A 98c10749445b

Subscribe to this mod's changes

earnings-prep is a skill published in the GitHub repository daloopa/daloopa-plugin-claude (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 4,001 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 earnings-prep, differing in 0 lines, and is treated as a copy.

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