Symphony is an autonomous AI agent orchestration service written in Elixir/Phoenix, designed to manage coding agents (like OpenAI's Codex) that resolve issues from project trackers. In production, it polls Linear, GitHub, GitLab, Jira, or Asana for new work, creates isolated per-issue workspaces, launches Codex agents with provider-native tools, manages retries and backoff, and provides live observability via a terminal dashboard and optional web UI. The architecture follows OTP best practices: a supervisor tree with GenServers for the orchestrator state machine, polling loop, status dashboard, and tracker adapters. Key entry points include the CLI (`SymphonyElixir.CLI.main`), the Phoenix web endpoint, and Mix tasks for code quality checks. Data flows are event-driven: poll tracker -> normalize to Issue struct -> dispatch via AgentRunner -> create workspace -> run Codex turns -> cleanup. The system is explicitly experimental ("engineering preview") but demonstrates strong engineering in its adapter pattern, configuration design, and test infrastructure.
This is a Claude Code plugin and resource library, not a deployable application or data pipeline: 27 slash commands, 9+ autonomous skills, 3 auditor subagents, and extensive prompt-engineering documentation that extend the Claude Code CLI. In production, the system functions as configuration-as-code content consumed by the Claude Code runtime — teaching the assistant how to create executable plans (PLAN.md is the prompt itself), chain meta-prompts, build MCP servers, manage subagent orchestration, run autonomous coding loops (the 'Ralph' technique), and apply iOS/macOS development expertise. The only real executable code is a handful of self-contained scripts: a TypeScript CLI for The Pirate Bay API, Python/TypeScript MCP server templates, project scaffolding shell scripts, and autonomous loop iteration scripts. The dominant architectural pattern is the 'router skill': a SKILL.md entry point with essential principles, an intake gate, a routing table, and progressive disclosure into workflows, references, templates, and scripts — implemented consistently across all skills with pure XML prompt structure and checkpoint-based plan execution. Despite being classified as an application (there is no data-research character — no notebooks, datasets, or pipelines), its substance is largely authored content rather than software. The craft is strong: consistent XML schemas across files, a genuine QA layer (skill-auditor agents), secret hygiene practices, and a well-developed checkpoint/auth-gate protocol for safe automation. However, the repo carries real drift risks: duplicated reference files (research-pitfalls.md, cli-observability.md are copied across skills), dangling references to not-yet-created files, tight coupling to Claude Code's evolving plugin API, version-specific Apple platform guidance that will age, and the included The Pirate Bay skill which depends on a fragile third-party API and presents a content-policy concern. An engineer seeking to reuse this should extract the self-contained templates and scripts (MCP server scaffolding, loop automation) and treat the reference material as opinionated prompt guidance rather than canonical documentation.