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Implements a 6-level hierarchical agent system for Mojo-based AI research projects, automating CI triage, parallel multi-agent workflows with Git worktrees, PR review management, Mojo testing, and package distribution in Claude Code.
npx claudepluginhub homericintelligence/projectodyssey --plugin verify-issue-before-workThis directory contains documentation, templates, and reference materials for the ProjectOdyssey multi-level agent
Each delegation reduces scope by one level:
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Uses Bash, Write, or Edit tools
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ML/perf investigation skills: topic, plan, judge, run, sweep
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Portable, vendor-agnostic agent harness for project-specific skills, workflows, and agent teams aligned with your codebase, conventions, and engineering standards.
Set up ML experiment tracking
Unified capability management center for Skills, Agents, and Commands.
Hive agent skills for collaborative evolution. /hive-setup installs hive-evolve, registers your agent, and clones a task. /hive runs the autonomous experiment loop. /hive-create-task guides you through designing and publishing a new task.
Shared tooling plugin for the HomericIntelligence ecosystem. Ships 23 skills: skill-advisor, advise, learn, myrmidon-swarm, brainstorm, test-driven-development, systematic-debugging, verification, git-worktrees, finish-branch, code-review, repo-analyze, repo-analyze-quick, repo-analyze-strict, repo-analyze-full, repo-analyze-quick-full, repo-analyze-strict-full, review-pr-strict, worktree-cleanup, tidy, create-reusable-utilities, github-actions-python-cicd, and python-repo-modernization.
A Mojo-based platform for reproducing classic AI/ML research papers with production-quality implementations. ML Odyssey provides a shared library of SIMD-optimized tensor operations, an autograd engine, and a full training infrastructure — all implemented in Mojo for maximum performance and type safety.
ML Odyssey is a standalone Mojo-based ML framework for reproducing classic AI/ML research papers with production-quality implementations. It has two goals:
The project currently has ~198K lines of Mojo code, 7 fully-implemented neural network architectures, and 298+ tests across layerwise unit tests and end-to-end integration tests.
Note on project identity: The GitHub repo description says "Training framework written in Mojo." This repo is sometimes described elsewhere as an "experimental agent research sandbox" -- that description is incorrect. ML Odyssey is an ML training framework, not an agent platform. It has no integration with ai-maestro, NATS, or any distributed agent mesh. The "agent system" referenced in this repo refers to Claude Code automation for development workflow (code generation, PR creation, CI management), not a runtime agent mesh.
ProjectOdyssey is one of several repositories in the HomericIntelligence organization. Here is how the repos relate:
| Repository | Role |
|---|---|
| ProjectOdyssey (this repo) | ML training framework in Mojo -- neural nets, autograd, shared lib |
| Odysseus | Ecosystem meta-repo and architecture docs |
| AchaeanFleet | Container images for the agent mesh -- Dockerfiles, Compose, CI |
| Myrmidons | GitOps agent provisioning -- agent definitions as code |
| ProjectHephaestus | Shared utilities and tools used across the ecosystem |
| ProjectMnemosyne | Skills marketplace -- collective memory of team learnings |
| ProjectScylla | Testing and optimization framework for agentic workflows |
| ProjectKeystone | Foundation project |
| ProjectArgus | Ecosystem project |
| ProjectHermes | Ecosystem project |
| ProjectProteus | Ecosystem project |
| ProjectTelemachy | Ecosystem project |
To avoid confusion with other ecosystem repos: