Most people save interesting links expecting to come back to them later. Later arrives, and the link is still there — but the reason it seemed worth saving is gone. Decision Rain Library Project exists to close that gap: instead of archiving a URL, you archive a judgment.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/XxYouDeaDPunKxX/decision-rain-library-project/llms.txt
Use this file to discover all available pages before exploring further.
What the project is
Decision Rain Library Project is a small set of rules, note templates, tag definitions, and governance documents that turns Raindrop into a reviewed-decision library. It is a template and operating system, not a code library, plugin, or automation framework. The runtime is the combination of your Raindrop account, a set of SYSTEM governance entries stored inside it, and an AI assistant — such as ChatGPT — connected to that account. The project gives the assistant a repeatable way to inspect a link and propose a structured review before the link disappears into a growing pile of bookmarks.Why plain bookmarks are not enough
Saving a URL preserves the address but nothing else. By the time you return to it, you have lost the signal: what caught your attention, what you were trying to solve, whether the project was maintained, whether it actually fit your setup. A folder full of bookmarks does not answer those questions any faster the second time around. Decision Rain Library Project makes the AI inspect the link at capture time and write down the answers while the context is still fresh.The core principle
The project is built on a single rule from its specification:Do not catalog links — catalog decisions.Every entry in the library carries not just a URL but a judgment: what is known, what is uncertain, what the evidence supports, and what the next action should be. Collections represent lifecycle states — inbox, review, library, archive — not topic folders.
What every reviewed entry captures
A reviewed entry answers six questions before the link is moved out of the inbox:- What it is — the type of resource and what it appears to provide
- Why it may matter — what problem or opportunity it connects to
- What is known — what the official docs, README, source, or pricing confirm
- What is uncertain — what community signals, open issues, or missing evidence reveal
- What the next step should be — a concrete action: test, extract, watch, archive
- Practical fit — whether this is realistically usable given your tools, accounts, setup, and friction tolerance
Who this is for
This project is for people who regularly save GitHub repositories, tools, documentation pages, products, research papers, workflow ideas, or “interesting for later” sparks — and who later forget why. If your bookmark manager contains dozens of links you can no longer evaluate without re-reading them, this project gives you a system for doing the work once, at capture time, and storing the result. You do not need to know why something matters at the moment you save it. The point is to preserve the trace and turn it into a decision before the context is lost.What this project is not
Understanding the limits is as important as understanding what the project provides.- It is not a full knowledge base or personal information system.
- It is not an automation framework — there is no CI, build process, or scheduled workflow.
- It is not a promise that AI can decide for you — the assistant proposes; the operator always validates.
Decision Rain Library Project is an independent, user-created template for organizing reviewed decisions around saved links. It is not affiliated with, endorsed by, or sponsored by Raindrop.io.
Where to go next
How It Works
See the six-step review loop and understand how a link becomes a decision.
Setup
Create the collection structure in Raindrop and connect your ChatGPT assistant.
Collections
Understand each collection’s role in the decision lifecycle.
Tag Registry
Explore the controlled tag grammar that makes entries findable and consistent.