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Financial Analyst

Checked
alirezarezvani·vlatest·data·MIT

This Financial Analyst skill provides insightful data analysis to assess investments and financial trends, boosting informed decision-making.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add alirezarezvani/claude-skills --skill financial-analyst -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

alirezarezvani

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned alirezarezvani/claude-skills at 19392f7a08.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
19392f7a08
Manifest
finance/skills/financial-analyst/SKILL.md
Scanned
14 files, 3,907 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The financial-analyst skill empowers AI agents to process complex economic data, generate detailed market reports, and perform quantitative analysis on financial instruments. It transforms raw numerical datasets into actionable insights through automated modeling and trend identification.

When to use it

  • Analyzing quarterly earnings reports to identify revenue growth patterns across multiple sectors.
  • Forecasting stock price movements based on historical trading data and macroeconomic indicators.
  • Evaluating investment portfolios by calculating risk-adjusted returns and asset correlations.
  • Generating regulatory compliance summaries for financial transactions involving large capital flows.

Key capabilities

  • Automated data ingestion from CSV, JSON, and API sources containing market metrics.
  • Statistical modeling including regression analysis, time-series forecasting, and volatility calculations.
  • Dynamic report generation with visualizations of key performance indicators (KPIs).
  • Scenario planning tools to simulate market impacts under varying economic conditions.

Example prompts

  • "Analyze the attached CSV file containing 12 months of S&P 500 data and identify the top three correlation drivers."
  • "Generate a risk assessment report for a diversified portfolio including tech, energy, and healthcare sectors based on current volatility indices."
  • "Forecast next quarter's revenue trends using the provided historical sales data and apply a moving average model."

Tips & gotchas

Ensure input datasets are cleaned and formatted correctly before analysis to avoid skewed statistical results. This skill requires sufficient historical data points; providing less than three years of data may limit forecasting accuracy.