Market Events

Reports upcoming earnings, dividends, and stock splits from FMP for a watchlist of tickers. Accepts a comma-separated ticker list or a file of tickers. Retur...

Security Scan

The skill's code, instructions, and environment requirements are coherent with its stated purpose (querying Financial Modeling Prep for market events) and do not request unrelated credentials or reach unexpected endpoints.

Purpose & Capability

Name/description match the implementation: the script queries Financial Modeling Prep (FMP) endpoints for earnings/dividends/splits and filters by a user-provided watchlist. The only required runtime artifact is an FMP API key, which is appropriate for this purpose.

Instruction Scope

SKILL.md and the script limit actions to: reading tickers from --tickers or a user-specified file, reading FMP_API_KEY from the environment, calling FMP API endpoints, and formatting output. There are no instructions to read unrelated system files or to send data to other external endpoints.

Install Mechanism

There is no install spec (instruction-only), but the package includes a Python script and requirements.txt listing 'requests'. SKILL.md and the script note that the requests library is required (pip install requests). No downloads from untrusted URLs or archive extraction are present.

Credentials

The skill requires a single environment variable, FMP_API_KEY, which is exactly what is needed to authenticate to Financial Modeling Prep. No other credentials, tokens, or sensitive config paths are requested.

Persistence & Privilege

The skill does not request always:true and uses the normal invocation model. It does not modify other skills or system-wide settings. It runs as a one-off script when invoked.

Assessment

This skill appears to do what it claims: it will use the FMP_API_KEY you provide to call Financial Modeling Prep endpoints and return events for tickers you pass (either inline or via a file). Before installing or running: 1) Verify you trust the skill source and review the included market-events.py (it is bundled and will be executed). 2) Only provide an FMP API key you are comfortable using for this purpose (no other credentials are requested). 3) Be careful about the file paths you pass to --file (the script will read any file you point it at). 4) Install the 'requests' library in the environment where the script will run. If you want extra assurance, run the script in an isolated environment (virtualenv/containers) and inspect network traffic to confirm calls only go to financialmodelingprep.com.

Query the Financial Modeling Prep (FMP) API to report upcoming earnings, dividends, and stock splits for a watchlist of tickers.

Quick Start

# Check events for specific tickers (next 7 days)
python3 /home/claw/.openclaw/workspace/skills/market-events/market-events.py --tickers AAPL,MSFT,GOOG

# Use a ticker file
python3 /home/claw/.openclaw/workspace/skills/market-events/market-events.py --file tickers.txt

# Combine both, custom range, specific event types
python3 /home/claw/.openclaw/workspace/skills/market-events/market-events.py --tickers NVDA --file watchlist.csv --range 14d --types earnings,dividends

Usage

python3 /home/claw/.openclaw/workspace/skills/market-events/market-events.py [OPTIONS]

Options:
  --tickers TICKERS   Comma-separated list of ticker symbols
  --file PATH         Path to a .txt or .csv file of tickers
  --range RANGE       Lookahead window (e.g. 7d, 14d, 30d). Default: 7d. Max: 90d.
  --format FORMAT     Output format: text, json, or discord. Default: text.
  --types TYPES       Comma-separated event types: earnings,dividends,splits. Default: all.
  -h, --help          Show help message

At least one of --tickers or --file must be provided.

Ticker File Formats

Plain text (.txt)

AAPL
MSFT
# This is a comment
GOOG

CSV (.csv)

First column is used as ticker. Header row is auto-detected and skipped.

ticker,name
AAPL,Apple Inc
MSFT,Microsoft Corp

Output Formats

Text (default)

Market Events: 2026-03-16 → 2026-03-23 (3 tickers, earnings/dividends/splits)
──────────────────────────────────────────────────────────────────────
Date        Ticker  Type       Detail
2026-03-18  AAPL    earnings   EPS est: 1.52  Revenue est: 94.36B
2026-03-20  MSFT    dividends  Dividend: 0.75  Ex-date: 2026-03-20  Pay date: 2026-04-10
──────────────────────────────────────────────────────────────────────
2 events found.

JSON (--format json)

{
  "range": {"from": "2026-03-16", "to": "2026-03-23"},
  "ticker_count": 3,
  "types": ["earnings", "dividends", "splits"],
  "event_count": 2,
  "events": [
    {"date": "2026-03-18", "ticker": "AAPL", "event_type": "earnings", "detail": "EPS est: 1.52  Revenue est: 94.36B", "raw": { ... }},
    {"date": "2026-03-20", "ticker": "MSFT", "event_type": "dividends", "detail": "Dividend: 0.75  Ex-date: 2026-03-20", "raw": { ... }}
  ]
}

The raw field contains the full FMP API response for each event.

Discord (--format discord)

**Market Events** 2026-03-16 → 2026-03-23 (3 tickers, earnings/dividends/splits)
💰 **AAPL** 2026-03-18 — EPS est: 1.52  Revenue est: 94.36B
💵 **MSFT** 2026-03-20 — Dividend: 0.75  Ex-date: 2026-03-20  Pay date: 2026-04-10
_2 events found._

Notes

• Requires the requests library (pip install requests).
• FMP free tier has rate limits. The skill handles 429 responses with a warning and continues with partial results.
• Events are sorted by date ascending, then by event type (earnings → dividends → splits).