msitarzewski/agency-agents
Agency-Agents is a production-ready collection of 144+ specialized AI agent personas organized across 12 divisions — Engineering, Design, Paid Media, Sales, Marketing, Product, Project Management, Testing, Support, Spatial Computing, Game Development, and Specialized. Each agent is a structured Markdown file that gives any LLM a specific professional identity, complete with domain expertise, a distinct communication style, battle-tested workflows, concrete deliverables, and measurable success metrics. Rather than relying on generic prompting, you activate a focused expert — a Frontend Wizard, Security Engineer, Brand Guardian, UX Researcher, or one of 140+ others — and the LLM narrows its context accordingly, reducing hallucinations and enforcing domain best practices. The project was born from a Reddit discussion about AI agent specialization and grew through months of community iteration into one of the fastest-starred repositories on GitHub. It integrates natively with Claude Code's /agents system by placing Markdown files in ~/.claude/agents/, and ships automated install scripts that convert agents for Cursor, Aider, Windsurf, Gemini CLI, OpenCode, and more. With 43.9K stars and 6.6K forks as of March 2026, agency-agents has become the de facto starting point for teams that want to run structured multi-agent workflows from their IDE.
Why It Matters
Generic AI assistants produce generic results. Agency-Agents solves this by giving every major role in a software organization its own opinionated LLM persona — complete with workflows, deliverables, and success criteria baked in. For developers using Claude Code, this means dropping a Markdown file into ~/.claude/agents/ and instantly having a Security Engineer that flags vulnerabilities the way a CISO would, a QA Specialist that writes test plans with real coverage metrics, or a Project Shepherd that produces sprint retrospectives in standard formats. For teams, it means everyone invokes the same baseline expert, reducing variance and making AI output reviewable. The 12-division structure mirrors how actual product companies are organized, so multi-agent scenarios — spinning up a Frontend Dev, Brand Guardian, and Growth Hacker simultaneously on a product launch — map directly to real org charts. The viral adoption signals that the pain of generic LLM outputs is widespread and that structured personas are rapidly becoming standard practice for AI-augmented teams.