Agent skill · Data & Analytics

economic-calendar-fetcher

Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report.

BaggaT236github.com/BaggaT236GitHub ↗
claude-codeships scriptsMIT
Install
npx skills add BaggaT236/AI-Trading-Skills --skill economic-calendar-fetcher --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 15 KB
Bundled scripts: yes
Path: skills/economic-calendar-fetcher/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 118
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Economic Calendar Fetcher ## Overview Retrieve upcoming economic events and data releases from the Financial Modeling Prep (FMP) Economic Calendar API. This skill fetches scheduled economic indicators including central bank monetary policy decisions, employment reports, inflation data (CPI/PPI), GDP releases, retail sales, manufacturing data, and other market-moving events that impact financial markets. The skill uses a Python script to query the FMP API and returns raw JSON or text output. The assistant then filters events, assesses market impact, and generates a chronological Markdown report for each scheduled event. No files are generated automatically. **Key Capabilities:** - Fetch economic events for specified date ranges (max 90 days) - Support flexible API key provision (environment variable or CLI argument) - Filter by impact level, country, or event type (filtering performed by the assistant) - Present filtered results as structured Markdown reports with impact analysis (assistant-generated, not script-generated) - Default to next 7 days for quick market outlook **Data Source:** - FMP Economic Calendar API: `https://financialmodelingprep.com/api/v3/economic_calendar` - C

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Prerequisites
  4. Workflow
  5. Step 1: Obtain FMP API Key
  6. Step 2: Determine Date Range
  7. Step 3: Execute API Fetch Script
  8. Step 4: Parse and Filter Events
  9. Step 5: Assess Market Impact
  10. Step 6: Generate Output Report
  11. Assistant-Generated Report Format
  12. Resources
Ships with 3 files
  • references/fmp_api_documentation.md
  • scripts/get_economic_calendar.py
  • scripts/tests/test_get_economic_calendar.py
Commands it runs
python3 skills/economic-calendar-fetcher/scripts/get_economic_calendar.py --api-key YOUR_KEY
python3 skills/economic-calendar-fetcher/scripts/get_economic_calendar.py \
export FMP_API_KEY=your_key_here
python3 - <<'PY'
More from AI-Trading-Skills
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About this skill
What does the economic-calendar-fetcher skill do?

Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report.

How do I install it?

Run `npx skills add BaggaT236/AI-Trading-Skills --skill economic-calendar-fetcher --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going