earnings-calendar

earnings-calendar is a skill for Claude Code, Codex from BaggaT236/AI-Trading-Skills. It costs 107 tokens per session (5,704 once invoked), scanned B, a copy of earnings-calendar, MIT.

A tool for finding upcoming earnings announcements from US companies through the Financial Modeling Prep service. Earnings announcements report a company's recent financial results and are often watched by investors.

In plain words
What is it for?
Use it to create a weekly earnings calendar grouped by date, market timing, and company size. It requires an FMP API key.
Why use it?
It removes the need to search company announcements one by one and sort them by reporting date or time. It focuses on US companies valued above $2 billion and includes expected earnings and revenue figures.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: names the AskUserQuestion tool; positional $N argument; mentions Claude Code.

Good fit Use it to create a weekly earnings calendar grouped by date, market timing, and company size. It requires an FMP API key.

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Install with agentmods
npx agentmods add skills/baggat236/ai-trading-skills/earnings-calendar
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 BaggaT236/AI-Trading-Skills --skill earnings-calendar
Clone the repo
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills

Made for: Claude Code, Codex.

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.

agentmods badge for earnings-calendar

README.md
[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/earnings-calendar/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/earnings-calendar)
Your own site
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/earnings-calendar"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/earnings-calendar/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.

agentmods 80×15 button for earnings-calendar

Your own site · 80×15
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/earnings-calendar"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/earnings-calendar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,704 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00107 $0.05704
Opus 5 $0.00053 $0.02852
Sonnet 5 $0.00021 $0.01141
Haiku 4.5 $0.00011 $0.00570

Measured 13d ago against content hash 29873fb5a68c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade B, and why

earnings-calendar scanned grade B with 1 finding 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 13d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/fetch_earnings_fmp.py, scripts/generate_report.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Asks the agent to reveal its instructionsmediumSystem prompt leakage

Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.

2. "No, get free key" → Show instructions (3.2.1)
Origin

This is a copy

100% identical to earnings-calendar — 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.

examples/weekly-trade-strategy/skills/earnings-calendar/SKILL.md · 722 lines

How it starts

The opening of the file, as written. The whole thing — 722 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Earnings Calendar

Overview

This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. It focuses on companies with significant market capitalization (mid-cap and above, over $2B) that are likely to impact market movements. The skill generates organized markdown reports showing which companies are reporting earnings over the next week, grouped by date and timing (before market open, after market close, or time not announced).

Key Features:

  • Uses FMP API for reliable, structured earnings data
  • Filters by market cap (>$2B) to focus on market-moving companies
  • Includes EPS and revenue estimates
  • Multi-environment support (CLI, Desktop, Web)
  • Flexible API key management
  • Organized by date, timing, and market cap

Prerequisites

FMP API Key

This skill requires a Financial Modeling Prep API key.

Get Free API Key:

  1. Visit: https://site.financialmodelingprep.com/developer/docs
  2. Sign up for free account
  3. Receive API key immediately
  4. Free tier: 250 API calls/day (sufficient for weekly earnings calendar)

API Key Setup by Environment:

Claude Code (CLI):

export FMP_API_KEY="your-api-key-here"

Claude Desktop: Set environment variable in system or configure MCP server.

Claude Web: API key will be requested during skill execution (stored only for current session).

Core Workflow

Step 1: Get Current Date and Calculate Target Week

CRITICAL: Always start by obtaining the accurate current date.

Retrieve the current date and time:

  • Use system date/time to get today's date
  • Note: "Today's date" is provided in the environment ( tag)
  • Calculate the target week: Next 7 days from current date

Date Range Calculation:

Current Date: [e.g., November 2, 2025]
Target Week Start: [Current Date + 1 day, e.g., November 3, 2025]
Target Week End: [Current Date + 7 days, e.g., November 9, 2025]

Why This Matters:

  • Earnings calendars are time-sensitive
  • "Next week" must be calculated from the actual current date
  • Provides accurate date range for API request

Read the full file on GitHub · 722 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

  1. 13d ago First seen · 722 lines · 107 tokens per session scan B 29873fb5a68c

Subscribe to this mod's changes

earnings-calendar is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 107 tokens to every session and 5,704 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). It is 100% identical to earnings-calendar, differing in 0 lines, and is treated as a copy.

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