Skill: openlark/financial-report-tracker (version 1.0.0)

--- name: financial-report-tracker description: Automatically track tech company financial reports and generate investment summaries. Supports retrieving earnings calendars, market expectation comparisons, key metric interpretation, and more. --- # Financial Report Tracker Automatically track tech company financial reports and generate investment summaries. Suitable for investors tracking portfolio companies' earnings calendars and automatically summarizing earnings highlights and risks. ## Use Cases When users mention earnings reports, financial reports, EPS, revenue expectations, earnings interpretation, tracking a company's financials, and similar scenarios. ## Prerequisites Install Python dependencies before first use: ```bash pip install yfinance requests pandas ``` ## Core Capabilities 1. **Earnings Calendar Tracking** — Automatically retrieve target company earnings release dates 2. **Market Expectation Comparison** — EPS/Revenue expectations vs. actual data 3. **Earnings Interpretation** — Key metric changes and management guidance summary ## Command List | Command | Description | Usage | |---------|-------------|-------| | `track` | Track earnings release dates | `python scripts/earnings_tracker.py track <ticker>` | | `preview` | Earnings preview analysis | `python scripts/earnings_tracker.py preview <ticker>` | | `review` | Earnings interpretation | `python scripts/earnings_tracker.py review <ticker> --quarter <Q1/Q2/Q3/Q4>` | ## Usage Workflow ### Scenario 1: Track Earnings Date ``` Track Apple's next earnings release date and market expectations ``` ```bash python scripts/earnings_tracker.py track AAPL ``` ### Scenario 2: Earnings Preview Analysis ``` Pre-earnings expectation analysis ``` ```bash python scripts/earnings_tracker.py preview AAPL ``` ### Scenario 3: Earnings Review ``` Interpret key data from the latest earnings report ``` ```bash python scripts/earnings_tracker.py review AAPL --quarter Q1 ``` ## Output Format All commands output a standard Markdown format report: ```markdown # 📊 Financial Report Tracker Report **Generated on**: YYYY-MM-DD HH:MM ## Key Findings 1. [Key finding 1] 2. [Key finding 2] 3. [Key finding 3] ## Data Overview | Metric | Value | Trend | Rating | |--------|-------|-------|--------| | Metric A | XXX | ↑ | ⭐⭐⭐⭐ | | Metric B | YYY | → | ⭐⭐⭐ | ## Detailed Analysis [Multi-dimensional analysis based on actual data] ## Actionable Recommendations | Priority | Recommendation | Expected Outcome | |----------|----------------|------------------| | 🔴 High | [Specific recommendation] | [Quantified expectation] | | 🟡 Medium | [Specific recommendation] | [Quantified expectation] | | 🟢 Low | [Specific recommendation] | [Quantified expectation] | ``` ## References - [yfinance Library](https://pypi.org/project/yfinance/) — Earnings calendar and earnings data - [Financial Modeling Prep API](https://site.financialmodelingprep.com/developer/docs) — Financial report data ## Notes - All analysis is based on data retrieved by the script; data is not fabricated - Missing data fields are marked "Data Unavailable" rather than guessed - It is recommended to combine with human judgment; AI analysis is for reference only


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)