financial-data-export-workflow

Structured process for exporting and analyzing multi-year brokerage transaction history when browser automation is blocked

5 stars

Best use case

financial-data-export-workflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Structured process for exporting and analyzing multi-year brokerage transaction history when browser automation is blocked

Teams using financial-data-export-workflow should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/financial-data-export-workflow/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/workspace-hub/learned/financial-data-export-workflow/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/financial-data-export-workflow/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How financial-data-export-workflow Compares

Feature / Agentfinancial-data-export-workflowStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Structured process for exporting and analyzing multi-year brokerage transaction history when browser automation is blocked

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Financial Data Export & Historical Analysis Workflow

When browser automation is blocked on financial sites (e.g., Fidelity), use manual CSV export + historical archive strategy: (1) Export current YTD transactions from the brokerage UI; (2) Locate historical transaction files already downloaded in data repo (e.g., `achantas-data/_finance/fidelity/`); (3) Build a parsing script that handles CSV format variations across years; (4) Aggregate transaction data to compute net positions, cost basis, and portfolio composition. This bypasses browser restrictions while enabling full multi-year analysis.

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