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team-workload-balancer — Claude Code command: what it does and setup

Analyzes developer workload, matches skills to sprint tasks, and balances assignments using local Git logs and Linear.

Install into Claude Code · claude-code-templates All methods and platforms ↓
npx claude-code-templates@latest --command team/team-workload-balancer
Open on GitHub

What team-workload-balancer does

This command reviews team capacity directly inside your workspace. It inspects local Git history to count active committers over the past month and track commit volume from the last week. To inspect active sprint assignments, it connects through Linear MCP.

You can direct the output using flags like --current-workload, --skill-matching, --capacity-planning, or --assignment-optimization. The prompt evaluates bottlenecks, checks skill coverage, and suggests task rebalancing based on workload equity.

Without a working Linear MCP integration, sprint task evaluation cannot run. The template relies on standard tools: Read, Bash, Grep, and Glob.

What it helps with

  • identify overloaded team members using commit logs and sprint boards
  • match developer skill sets to pending sprint tasks
  • forecast team capacity before sprint kickoff
  • reassign tickets to eliminate single points of failure

Example prompts for the agent

  • /team-workload-balancer --current-workload
  • /team-workload-balancer --skill-matching
  • /team-workload-balancer --capacity-planning

How to install team-workload-balancer

1claude-code-templates

npx claude-code-templates@latest --command team/team-workload-balancer

2Manually: put the files in place

.claude/commands/team-workload-balancer.md

Call it with /team-workload-balancer. For all projects use ~/.claude/commands/

Questions about team-workload-balancer

How to install team-workload-balancer in Claude Code?

Run npx claude-code-templates@latest --command team/team-workload-balancer in your terminal or save the template to .claude/commands/team-workload-balancer.md.

What does team-workload-balancer do?

It parses Git history and Linear sprint items to evaluate team bandwidth, identify skill gaps, and optimize task distribution.

Is Linear MCP required for team-workload-balancer?

Yes, for sprint task analysis. Local Git commands calculate commit activity without external tools, but task rebalancing queries Linear MCP.

Source text

team-workload-balancer.md · MIT
---
allowed-tools: Read, Bash, Grep, Glob
argument-hint: "[analysis-type] | --current-workload | --skill-matching | --capacity-planning | --assignment-optimization"
description: Analyze and optimize team workload distribution with skill matching and capacity planning
---

# Team Workload Balancer

Analyze and optimize team workload distribution with intelligent assignment recommendations: **$ARGUMENTS**

## Current Team Context

- Team size: !`git log --format='%ae' --since='1 month ago' | sort -u | wc -l` active team members
- Active tasks: Linear MCP query for current sprint tasks and assignments
- Recent activity: !`git log --oneline --since='1 week ago' | wc -l` commits in last week
- Capacity metrics: Analysis of team velocity and individual contribution patterns

## Task

Execute comprehensive workload analysis with intelligent assignment optimization:

**Analysis Type**: Use $ARGUMENTS to focus on current workload assessment, skill matching, capacity planning, or assignment optimization

**Workload Balancing Framework**:
1. **Current Workload Assessment** - Analyze task distribution, evaluate individual capacity, assess deadline pressure, identify overloaded team members
2. **Skill Matching Analysis** - Map team member expertise, identify skill gaps, assess learning opportunities, optimize skill utilization
3. **Capacity Planning** - Calculate available capacity, project future workload, plan skill development, optimize resource allocation
4. **Performance Integration** - Analyze historical performance, identify productivity patterns, assess collaboration effectiveness, factor in availability constraints
5. **Assignment Optimization** - Generate optimal task assignments, balance workload distribution, maximize skill utilization, minimize bottlenecks
6. **Risk Mitigation** - Identify single points of failure, plan cross-training, assess knowledge distribution, ensure backup coverage

**Advanced Features**: Predictive workload modeling, skill gap analysis, burnout prevention, performance-based assignment, dynamic rebalancing recommendations.

**Quality Metrics**: Workload distribution equity, skill utilization efficiency, team satisfaction indicators, delivery predictability measures.

**Output**: Comprehensive workload analysis with optimized assignments, capacity recommendations, skill development plans, and team health insights.

© davila7 · MIT · davila7/claude-code-templates

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