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Orc

  • Updated January 30, 2026
  • twofoldtech-dakota/ORC

Orc is an autonomous multi-agent system that transforms goals into tested implementations, organizing work as an Epic/Feature/Story hierarchy with quality gates between stages. Developers use it to drive end-to-end feature delivery with multiple coordinated agents and built-in verification.

Key points

  • Autonomous multi-agent system
  • Epic/Feature/Story hierarchy
  • Quality gates, tested output

Orc by the numbers

  • Data as of Jul 7, 2026 (Skillselion catalog sync)
/plugin marketplace add twofoldtech-dakota/ORC
/plugin install orc@orc-marketplace

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Last updatedJanuary 30, 2026
Repositorytwofoldtech-dakota/ORC

What it does

Turn goals into tested implementations via an Epic/Feature/Story multi-agent hierarchy with quality gates.

README.md

ORC (Orchestrator)

Multi-Agent Orchestration System for Autonomous Software Development

ORC is a Claude Code plugin that transforms high-level goals into complete, tested implementations through a structured phased approach with mandatory plan approval.

Quick Start

# Create a plan
/orc:plan "Build REST API with user authentication"

# Review the plan
/orc:show

# Approve the plan
/orc:approve

# Execute
/orc:run

Core Philosophy

Quality > Capability > Developer Experience > Speed > Cost

ORC follows a strict workflow:

  1. Plan Phase - Decompose goal into Epic → Feature → Story hierarchy
  2. User Approval - Mandatory review before any implementation
  3. Execute Phase - Implement with ReAct pattern, validate each story
  4. Review Phase - Final quality gates and deviation analysis
  5. Learn Phase - Extract patterns for future use

Key Features

Plan-First Execution

No code is written until you approve the plan. Review exactly what will be built before it happens.

Self-Validation

Every story has explicit acceptance criteria verified with evidence. No GUI required—the system validates itself.

Strict Approach Adherence

The Implementer follows suggested approaches exactly. Any deviation is documented, categorized, and flagged for review.

Autonomous Recovery

Failed stories retry with different approaches (up to 3 attempts). If still failing, they're marked blocked and non-dependent work continues.

Parallel Execution

Independent stories execute concurrently. Dependencies are analyzed at plan time to prevent conflicts.

Pattern Learning

Successful implementations are extracted as patterns. Future similar tasks automatically receive proven approaches.

Commands

Planning

Command Description
/orc:plan <goal> Create or append to plan
/orc:show Display plan summary
/orc:show <epic-id> Display epic details
/orc:show deviations Show deviations for review
/orc:approve Approve all pending epics
/orc:approve <epic-id> Approve specific epic

Execution

Command Description
/orc:run Execute all approved epics
/orc:run <epic-id> Execute specific epic
/orc:next Execute next priority epic only
/orc:stop Stop execution gracefully
/orc:resume Resume from last checkpoint
/orc:retry <story-id> Retry a blocked story

Learning & Utility

Command Description
/orc:patterns Show learned patterns
/orc:learn Force pattern extraction
/orc:status Show current state
/orc:clear Clear plan and state

Plan Hierarchy

Plan
└── Epic (self-contained project milestone)
    └── Feature (self-contained capability)
        └── Story (atomic task with acceptance criteria)

Rules:

  • Epics are self-contained (no cross-epic dependencies)
  • Features are self-contained (no cross-feature dependencies)
  • Stories can depend on stories within the same feature only

Quality Gates

Story Completion

  • ✓ All acceptance criteria verified
  • ✓ All unit tests pass
  • ✓ Type checking passes
  • ✓ No lint errors
  • ✓ No new security vulnerabilities
  • ✓ Approach compliance verified

Feature Completion

  • ✓ All stories completed
  • ✓ Integration tests pass
  • ✓ Feature-level acceptance criteria verified

Epic Completion

  • ✓ All features completed
  • ✓ E2E tests pass
  • ✓ Final review completed
  • ✓ Full security scan passes

Agent System

Core Agents (6)

Agent Role
Orchestrator State management, phase control
Planner Goal decomposition
Implementer Code implementation (ReAct)
Validator Test & acceptance verification
Reviewer Final quality gate (Reflexion)
Learner Pattern extraction

Specialists (11)

Spawned on-demand: Architect, Product Designer, DevOps, Security, Database, Frontend, Backend, Full Stack, QA, Biz Analyst, Content Strategist

Runtime Directory

ORC maintains state in .orc/:

.orc/
├── plan/
│   ├── state.json           # Execution state
│   ├── plan.json            # Master plan
│   ├── learnings.json       # Patterns
│   ├── embeddings.json      # Vector search
│   ├── deviations.json      # Deviation log
│   └── epics/               # Epic definitions
└── checkpoints/             # Recovery points

Documentation

Example Session

> /orc:plan "Build a blog API with authentication"

📋 Plan Created: Build a blog API with authentication

Epics (2):
  E1: User Authentication [3 features, 9 stories]
  E2: Blog CRUD API [4 features, 14 stories]

Total: 7 features, 23 stories

Run /orc:approve to proceed

> /orc:approve
✓ Plan approved

> /orc:run
Starting execution...

[E1-F1-S1] Creating User model
  ├─ REASON: Loading pattern sp_bcrypt_001
  ├─ ACT: Creating src/models/User.ts
  ├─ VERIFY: ✓ Tests pass (4/4)
  └─ COMPLETE ✓

... (continues) ...

[EXECUTION COMPLETE]
  ├─ Stories: 23/23 completed
  ├─ Patterns learned: 5 new
  └─ Total time: 4m 32s

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