By Selene29
Self-improving AI workflow system. Crystallize requirements before execution with Socratic interview, ambiguity scoring, and 3-stage evaluation.
Cancel stuck or orphaned executions
Evaluate execution with three-stage verification pipeline
Start or monitor an evolutionary development loop
Full reference guide for Ouroboros commands and agents
Socratic interview to crystallize vague requirements
Scan and manage brownfield repository defaults for interviews
Cancel stuck or orphaned executions
Evaluate execution with three-stage verification pipeline
Start or monitor an evolutionary development loop
Full reference guide for Ouroboros commands and agents
Modifies files
Hook triggers on file write and edit operations
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
English | 한국어
◯ ─────────── ◯
O U R O B O R O S
◯ ─────────── ◯
Stop prompting. Start specifying.
Agent OS for replayable, specification-first AI coding workflows
Quick Start · Why · Results · How It Works · Commands · Philosophy
Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, and Hermes.
Ouroboros is an Agent OS for AI coding: a local-first runtime layer that turns non-deterministic agent work into a replayable, observable, policy-bound execution contract. It replaces ad-hoc prompting with a structured specification-first workflow: interview, crystallize, execute, evaluate, evolve.
Most AI coding fails at the input, not the output. The bottleneck is not AI capability -- it is human clarity.
| Problem | What Happens | Ouroboros Fix |
|---|---|---|
| Vague prompts | AI guesses, you rework | Socratic interview exposes hidden assumptions |
| No spec | Architecture drifts mid-build | Immutable seed spec locks intent before code |
| Manual QA | "Looks good" is not verification | 3-stage automated evaluation gate |
Install — one command, everything auto-detected:
curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | bash
Build — open your AI coding agent and go:
> ooo interview "I want to build a task management CLI"
Works with Claude Code, Codex CLI, OpenCode, and Hermes. The installer detects Claude Code, Codex CLI, and Hermes CLI automatically and registers the MCP server. For OpenCode, run
ouroboros setup --runtime opencodeafter installation.
Claude Code plugin only (no system package):
claude plugin marketplace add Q00/ouroboros && claude plugin install ouroboros@ouroboros
Then run ooo setup inside a Claude Code session.
pip / uv / pipx:
pip install ouroboros-ai # base
pip install ouroboros-ai[claude] # + Claude Code deps
pip install ouroboros-ai[litellm] # + LiteLLM multi-provider
pip install ouroboros-ai[mcp] # + MCP server/client support
pip install ouroboros-ai[tui] # + Textual terminal UI
pip install ouroboros-ai[all] # everything (claude + litellm + mcp + tui + dashboard)
ouroboros setup # configure runtime
Legacy compatibility: ouroboros-ai[dashboard] is still accepted as a compatibility alias while extras migrate.
See runtime guides: Claude Code · Codex CLI · Hermes · OpenCode
ouroboros uninstall
Removes all configuration, MCP registration, and data. See UNINSTALL.md for details.
Python >= 3.12 required. See pyproject.toml for the full dependency list.
After one loop of the Ouroboros cycle, a vague idea becomes a verified codebase:
| Step | Before | After |
|---|---|---|
| Interview | "Build me a task CLI" | 12 hidden assumptions exposed, ambiguity scored to 0.19 |
| Seed | No spec | Immutable specification with acceptance criteria, ontology, constraints |
| Evaluate | Manual review | 3-stage gate: Mechanical (free) -> Semantic -> Multi-Model Consensus |
npx claudepluginhub selene29/ouroborosHarness-native ECC plugin for engineering teams - 67 agents, 278 skills, 94 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.
v9.52.0 - Reliability wave: tangle contextual review correction loop with hard round ceiling, progress-supervised review rounds (per-agent stall watch, descendant-tree kills), council diversity and agy pin fixes, marketplace generator source-of-truth fix, provider troubleshooting runbook and cost-expectations docs. Run /octo:setup.
Persistent file-based planning for AI coding agents. Crash-proof markdown plans (task_plan.md, findings.md, progress.md) that survive context loss and /clear, with an opt-in completion gate and multi-agent shared state. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard. Includes Arabic, German, Spanish, and Chinese (Simplified and Traditional).
Claude harness - A harness for solo developers (Vibecoders) to handle full-cycle contract development.
Core skills library for Claude Code: TDD, debugging, collaboration patterns, and proven techniques