protocol: a post-partisan governance framework

competence verification protocol
individuals hold a decentralized identity linked to a portfolio of verifiable work. smart contracts assess project outcomes to generate dynamic competence scores in specific domains. peer validation from other verified experts continually refines these scores.

dynamic delegation model
citizens use a digital wallet containing their decision power. they vote directly or delegate this power by issue area to credentialed experts. delegation is fluid and can be instantly revoked. a permissioned blockchain provides a transparent, auditable ledger for all votes.

project-based governance
public objectives are defined as projects with clear metrics and posted as bounties. credentialed experts form teams to bid on projects. funding is locked in an escrow smart contract, released automatically upon verified milestone completion.

incentive alignment
builders earn compensation directly from project bounties as milestones are met. success increases a builder's competence score and earning potential. payments are tied to transparent results. poor performance reduces competence scores and access to future projects.

transition roadmap
a pilot will deploy in a single municipality to solve a tangible local problem. successful execution will serve as proof-of-concept for other communities. we will then offer a software development kit for new localities to launch their own instances.

metacognitive architect (ma) makes elizaos smarter about how it gets smart.

elizaos maps its own agentic loop and action libraries as a dynamic graph. this self-model is the substrate for ma's work, built from live telemetry and structural introspection of its own codebase. ma watches task performance telemetry, resource drains, and internal state traces. it looks for persistent fuckups, common inefficiencies, or stupid computational pathways.

when a pattern of shittiness emerges, ma designs a fix, targeting core architecture. it proposes diffs to the self-model, pushing new structures and logic. think 'we need a temporal graph and these specific reasoning primitives because we keep botching time-based sequences'. a pr gets formulated containing the architectural diff, the problem statement, and the proposed solution.

developers struggle with manual dependency updates and testing. the agent ingests repo lists, clones each, checks outdated packages, branches, updates incrementally, runs tests, commits on pass, or reverts and logs on fail. it creates pull requests. inputs include git urls, tokens, and test commands. outputs are prs and reports. integration is via cli like `elizaos update-deps` or ui scheduler.

maintaining api health and contract adherence is difficult. the agent ingests endpoint lists and openapi specs, periodically pings, checks status, validates responses against schemas, and measures latency. failures trigger logs and alerts. inputs are endpoint urls, specs, auth details, and alert configurations. outputs are alerts and performance data. integration happens through ui configuration, slack alerts, or status api.

scaffolding new microservices is repetitive. the agent takes service parameters, generates skeleton code, creates git repo, pushes code, generates dockerfile, ci/cd config, and basic deployment files. it may trigger the first build. inputs are service name, stack, git credentials, and deployment information. outputs consist of provisioned git repository. integration uses cli like `elizaos new-service` or ui form.

compiling release changelogs from git history is manual. the agent ingests repo url and tag range, fetches commits, parses messages using conventions, categorizes them, and generates markdown changelog. it can create git tag and pr with changelog. inputs include repo url, tag range, new version, and parsing rules. output is markdown changelog. integration is through cli like `elizaos changelog` or ci pipeline step.

the agent comprises several core modules for autonomous memecoin operations.
a contractor module generates and deploys a standard erc20 token contract on a target l2, using pre-configured parameters for name, symbol, and supply.
the liquidity module interacts with a decentralized exchange, pairing a portion of the total supply with a base asset like eth and establishing the initial pool.
a social shill module handles web2 presence by creating twitter and telegram accounts and generates posts reflecting current degen narratives and popular memes.
the meme engine module produces simple, templated visual content.
a community manager module utilizes a fine-tuned llm to answer basic questions from telegram chat logs.
a sentiment analyzer module monitors social media mentions and market chatter, feeding data into the strategy module.
the strategy module adjusts shilling frequency, content themes, and engagement tactics based on sentiment and engagement metrics.
an anti-farm module analyzes wallet activity and social account histories, applying rules to filter suspicious participants from distributions.
the system uses rpc endpoints for on-chain interactions and platform apis for web2 communications, orchestrated through the strategy module.

core agent definitions live in a flat `agents.py` containing python dicts. each dict outlines an agent's model, tools, and any dependent agent endpoints.

tools declare required environment variables. at runtime, agents pull these vars from a key-value secret store.

stateless agents run on serverless functions. fine-tuned models use separate, cheap inference endpoints – ollama on a spot vm, or replicate/anyscale. a tiny model router manages this.

a minimal cli, `agentctl`, does three things: `agentctl secret set/get`, `agentctl run <agent_name> --port <num>`, and `agentctl deploy <agent_name> <platform>`. platform could be 'lambda', 'cloudflare', 'fly'.

inter-agent calls are http requests to other agent services, whose urls are in their `agents.py` definitions.