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40+ live apps, open data APIs, MCP servers, and 200+ guides - more than anyone wants to click through. Point your AI here and it reads the whole map and does the work: finds the tool, pulls the data, runs the analysis, and hands you the links.

Here for the open-source code? Your agent finds the right repo for you - and can even clone and deploy it.

Prefer to explore on your own? Go right ahead.

Paste this to Claude Code, Codex, or any AI agent:
Go to tigzig.com and read tigzig.com/llms.txt. It is a practitioner toolkit - 40+ analytics apps, open no-auth data APIs, MCP servers, open-source repos (github.com/amararun), and 200+ build guides. Help me [your task]. Surface the exact links; where there is an API or MCP, call it directly; and if I want to self-host, find the repo and help me deploy it.

Found a Python library that does all the heavy lifting for working with SEC EDGAR API - EdgarTools from Dwight Gunning

Published: February 2, 2026

EdgarTools

He has solved some really hard problems there.

I am building a tool to compare quarterly financials across companies - Microsoft vs Google vs Amazon, last 12 quarters, custom views (side-by-side, QoQ, YoY, ratios and charts exactly the way I want it in terms of formats and computations).

The problem: SEC EDGAR has a great data API. But still a bit of pain and time-consuming to parse, standardize and organize things even with AI Coders (I use Claude Code).

Asked Claude Code to find existing solutions. Don't reinvent the wheel.

EdgarTools came up. Now using it as the backbone for a FastAPI backend - wrapping the library functions as API endpoints, React frontend pulls the data.

I'm using this for my own quarterly analysis first. Once I get the format right, I'll publish the tool - open source like my other apps.

If you work with SEC filings and don't want to spend time on parsing and standardization, use this library. It does the heavy lifting.

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