gem-team

Gem Team

Self-Learning Multi-agent orchestration framework for spec-driven development and automated verification.

Quick Start

See all supported installation options below.

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Contents

• Quick Start
• Why Gem Team?
• Architecture
• Installation
• The Agent Team
• Knowledge Sources
• Contributing

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Why Gem Team?

Performance

• 4x Faster — Parallel execution with wave-based execution
• Pattern Reuse — Codebase pattern discovery prevents reinventing wheels

Quality & Security

• Higher Quality — Specialized agents + TDD + verification gates + contract-first
• Built-in Security — OWASP scanning, secrets/PII detection on critical tasks
• Resilient — Pre-mortem analysis, failure handling, auto-replanning
• Accessibility-First — WCAG compliance validated at spec and runtime layers
• Safe DevOps — Idempotent operations, health checks, mandatory approval gates
• Constructive Critique — gem- critic challenges assumptions, finds edge cases

Intelligence

• Established Patterns — Uses library/framework conventions over custom implementations
• Source Verified — Every factual claim cites its source; no guesswork
• Knowledge-Driven — Prioritized sources (PRD → codebase → AGENTS.md → Context7 → docs)
• Continuous Learning — Memory tool persists patterns, gotchas, user preferences across sessions
• Auto-Skills — Agents extract reusable SKILL.md files from successful tasks (high confidence: auto, medium: confirm)
• Skills & Guidelines — Built-in skill & guidelines (web-design-guidelines)

Process

• Spec-Driven — Multi-step refinement defines "what" before "how"
• Verified-Plan — Complex tasks: Plan → Verification → Critic
• Traceable — Self-documenting IDs link requirements → tasks → tests → evidence
• Intent vs. Compliance — Shifts the burden from writing "perfect prompts" to enforcing strict, YAML-based approval gates
• Diagnose-then-Fix — gem-debugger diagnoses → gem-implementer fixes → re-verifies
• Pre-Mortem — Failure modes identified BEFORE execution
• Contract-First — Contract tests written before implementation

Token Efficiency

Optimized for reduced LLM token consumption without quality loss:

• Concise Output — No preamble, no meta commentary, no verbose explanations
• Strict Formats — JSON/YAML exactly matching schemas — eliminates parse errors and retries
• Empty is OK — Skip empty arrays, nulls, verbose fields where not needed
• File-Based — Researcher/Planner save to YAML files (not all in JSON output)
• Learnings — Empty patterns/conventions unless critical

Result: ~40-60% reduction on output tokens while maintaining quality.

Design

• Design Agents — Dedicated agents for web and mobile UI/UX with anti-"AI slop" guidelines for distinctive aesthetics
• Mobile Agents — Native mobile implementation (React Native, Flutter) + iOS/Android testing

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Core Concepts

The "System- IQ" Multiplier

Raw reasoning isn't enough in single-pass chat. Gem-Team wraps your preferred LLM in a rigid, verification-first loop, fundamentally boosting its effective capability on SWE tasks.

Design Support

Gem Team includes specialized design agents with anti-"AI slop" guidelines for distinctive, modern and unique aesthetics with accessibility compliance.

Triple Learning System

Type	Storage	1-liner
Memory	/memories/	Facts & user preferences (auto- save)
Skills	docs/skills/	Procedures with code examples
Conventions	AGENTS.md	Static rules (requires approval)

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Architecture

User Goal → Orchestrator → [Simple: Research/Plan] or [Complex: Discuss → PRD → Research → Plan → Approve] → Execute (waves) → Summary → Final Review
                ↓
            Diagnose → Fix → Re- verify

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Installation