AI Developer Tools for Production Engineering

By the Numbers

A Complete AI Engineering Platform

chAIrman is not a single tool. It is a full-stack system for managing AI agents, their tasks, their output, and their costs.

54

MCP Tools

10

Agent Templates

857

Skills

24

Skill Categories

The Toolset

54 Tools Across 18 Categories

Every tool you need to hire, manage, monitor, and coordinate AI agents — exposed as MCP tools that work directly in Claude Desktop or Claude Code.

⚙

Project Management

Create projects, set budgets, manage context files. Every project gets its own git repo, skills library, and cost tracking. Four tools: create_project, list_projects, set_budget, delete_project.

☯

Team & Task Management

Hire agents, assign tasks, queue work, check output, and monitor real-time activity. Ten tools covering the full agent lifecycle from hire to fire, including dependency-aware task queuing.

📈

Metrics & Health

Per-agent efficiency scores, project throughput, system-wide cost trends. Health checks monitor memory, CPU, zombie processes, error rates, and blocked pipelines. Two tools, three scopes.

📦

Batch Operations

Hire multiple agents at once, fire by status filter, assign the same task to many agents in parallel, or give each agent a unique task. Five batch tools eliminate repetitive calls.

🔌

Webhooks & Integrations

Subscribe external systems to agent events via HTTP POST with HMAC-SHA256 signing. Connect GitHub, Vercel, Cloudflare, AWS, Supabase, Stripe, and more. Six tools total.

📚

Memory & Continuity

Handoff documents, project briefings, CEO notes, alumni archives, and messaging. Agents preserve context across tasks, and veterans can be rehired with full experience. Eight tools.

Agent Templates

10 Pre-Built Agent Roles

Stop writing job descriptions from scratch. chAIrman includes professionally crafted agent templates for the most common engineering roles. Each template has a recommended model, a comprehensive job description, and placeholders for your project path and tech stack.

Frontend Lead — UI architecture, component design, responsive layouts, accessibility
Backend Lead — API design, database queries, server architecture, security
QA Engineer — Test suites, edge cases, regression testing, coverage analysis
DevOps Engineer — CI/CD pipelines, deployment, infrastructure, monitoring
Security Auditor — Vulnerability scanning, dependency audits, OWASP compliance
Code Reviewer — Pull request analysis, style enforcement, architectural feedback

Plus: Documentation Writer, Performance Engineer, Database Engineer, and API Designer. Use list_templates to browse, hire_from_template to deploy in one call.

// Hire a frontend lead in one call hire_from_template({ template: "frontend-lead", project: "my-saas-app", tech_stack: "React, TypeScript, Tailwind", model: "sonnet" }) // Agent is hired with a full job description // including your project path and tech stack // Ready for task assignment immediately

Skills Library

857 Skills, Auto-Injected

chAIrman maintains a library of 857 skill files across 24 categories. When you assign a task, the skills engine scores every skill using TF-IDF weighting, bigram matching, category tags, synonym expansion, and usage tracking. The top three matching skills are injected into the agent's prompt automatically.

Your agents get expert knowledge without you lifting a finger. Skills cover everything from frontend design patterns and API architecture to PDF generation, Excel manipulation, iOS app building, and D3.js data visualization.

Auto-discovery — Skills matched by task description, role keywords, and category tags
Usage tracking — Popular skills surface higher in future matches
Feedback loop — Your corrections become new skills via capture_feedback
24 categories — From brand-guidelines to webapp-testing

// Skills are matched automatically // When you assign this task: assign_task({ agent_id: "agent-7", task: "Build a D3.js dashboard with interactive charts for our analytics" }) // The skills engine auto-injects: // 1. claude-d3js/SKILL.md // 2. frontend-design/dashboard-patterns.md // 3. web-artifacts-builder/charts.md // No manual skill selection needed

How It Compares

Beyond Code Completion

Most AI developer tools help you write code faster. chAIrman helps you ship entire projects by orchestrating autonomous agents.

Capability	Code Completion Tools	chAIrman
Code suggestions	Single-file autocomplete	Full-project autonomous agents
Task management	None	Backlog, milestones, tickets, assign_ticket
Multi-agent coordination	None	Pipelines with dependency chains and auto-handoff
Cost tracking	Monthly subscription	Per-task USD tracking with budget enforcement
Monitoring	None	Real-time dashboard, health checks, metrics
Git integration	Manual commits	Auto-commit on task completion, git log, diff, stats
Knowledge reuse	None	857 skills, alumni archive, handoff docs, feedback loop
Deployment	None	Deploy to Vercel, Cloudflare, AWS, GitHub Pages

FAQ

Common Questions

What makes chAIrman different from GitHub Copilot or Cursor?

Copilot and Cursor are code completion tools — they help you write code faster in a single file. chAIrman is an orchestration platform that manages autonomous AI agents working across your entire codebase. You hire agents with specific roles, assign them tasks with dependency chains, track costs per task, monitor progress on a real-time dashboard, and auto-commit their work to git. It is the difference between a typing assistant and a workforce.

Do I need to know MCP to use chAIrman?

No. MCP (Model Context Protocol) is the communication layer that connects chAIrman to Claude Desktop or Claude Code. Once you install chAIrman and add it to your MCP config, the 54 tools appear automatically. You use them through natural language in Claude — just say "hire a frontend lead" or "assign this task" and Claude calls the right tool.

How does cost tracking work?

Every task completion reports its cost in USD, extracted from Claude Code's stream-json output. chAIrman sums costs per agent and per project. You can set a budget on any project — if an agent's next task would exceed the budget, it is blocked. The dashboard shows a live budget bar, and you get desktop notifications at 80% spend.

Can agents work in parallel?

Yes. You can hire multiple agents and assign them independent tasks simultaneously. For dependent work, use the depends_on parameter to create pipelines — tasks queue automatically and launch when their dependencies finish. The pipeline engine handles topological sorting, cycle detection, and critical path analysis.

What happens when an agent fails?

chAIrman auto-retries failed agents up to three times (configurable). Each retry creates a replacement agent that inherits the original's handoff notes and context. Eight stderr patterns are auto-detected (auth failure, rate limit, context exceeded, etc.) with specific recovery suggestions. You can also manually replace agents or adjust retry limits.

AI Developer Tools Built for Production