Estimate Assistant
Generate accurate task estimates using historical data, complexity analysis, and team velocity metrics
npx claude-code-templates@latest --command team/estimate-assistant Scanned by NVIDIA SkillSpector
Automated static analysis (no LLM) · v2.12.0 · 2026-10-23
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Content
Estimate Assistant
Generate data-driven task estimates with confidence intervals and accuracy tracking: $ARGUMENTS
Current Estimation Context
- Team velocity: !
git log --oneline --since='1 month ago' | wc -lcommits in last month - Historical data: Git history analysis for similar task completion patterns
- Code complexity: !
find . -name "*.js" -o -name "*.ts" -o -name "*.py" | head -5 | xargs wc -l 2>/dev/null | tail -1 || echo "No code files" - Sprint tracking: Linear task completion times and estimate accuracy
Task
Execute comprehensive task estimation with historical analysis and confidence modeling:
Estimation Focus: Use $ARGUMENTS for task description analysis, historical pattern matching, complexity assessment, or team velocity calculation
Estimation Framework:
- Historical Pattern Analysis - Analyze similar past tasks, extract completion time patterns, identify velocity trends, calculate accuracy metrics
- Complexity Assessment - Evaluate technical complexity, assess scope uncertainty, identify risk factors, estimate effort distribution
- Team Velocity Integration - Calculate sprint velocity, analyze individual capacity, assess team expertise, factor in availability constraints
- Confidence Modeling - Generate confidence intervals, assess estimation uncertainty, identify risk factors, provide accuracy ranges
- Calibration Analysis - Compare past estimates vs actuals, identify systematic biases, calculate estimation accuracy, improve prediction models
- Context Integration - Factor in current sprint load, assess team familiarity, evaluate external dependencies, integrate deadline pressure
Advanced Features: Multi-point estimation, Monte Carlo simulation, reference class forecasting, estimation accuracy tracking, bias correction algorithms.
Quality Metrics: Estimation confidence levels, accuracy historical trends, velocity stability, complexity correlation analysis.
Output: Data-driven estimates with confidence intervals, historical accuracy metrics, risk assessment, and calibration recommendations.