# How does it work? There are two main pieces to the initial architecture. A platform, where users vote on labels that have been applied to users and posts. Labelers, built independently to leverage those votes to publish labels on Bluesky. ## The Platform The platform will be a simple interface for users to interact with potential content labels. It will allow anyone with an account on Bluesky to create and view proposed labels, and vote them up or down depending on whether they agree or disagree with the label. Those votes will then be published to a PDS (either the user's, or aggregated for privacy) under a `social.pmsky.*` lexicon to allow labelers to consume these votes. ## The Labelers Once votes are published to the atmosphere as records, labelers can consume them and begin building a picture of the labels that users agree or disagree with. They'll be able to define what consensus looks like, and publish labels accordingly. This allows for flexibility depending on the situation, where self-identification might require a low threshold of votes, while other labels might require a supermajority of consensus. It also would allow for "bidirectional" labels where, for example, if pmsky users are voting on whether an account is authentic or not, a labeler could be defined to publish "Impersonation", "Contested Authenticity", and "Authentic" labels, all derived from the same set of votes. ## Big Picture The goal of this architecture is flexibility for different use cases. What works for one situation might not work for another. PMsky simply aims to provide the tools for labelers to be built on top of the ideal of peer participation. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.pmsky.social/getting-started/how-it-works.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.