wehub-resource-sync
36 lines
2.3 KiB
Markdown
36 lines
2.3 KiB
Markdown
---
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allowed-tools: Read, Bash, Glob, Grep
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argument-hint: [task-description] | --historical | --complexity-analysis | --team-velocity | --confidence-intervals
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description: Generate accurate task estimates using historical data, complexity analysis, and team velocity metrics
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---
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# Estimate Assistant
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Generate data-driven task estimates with confidence intervals and accuracy tracking: **$ARGUMENTS**
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## Current Estimation Context
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- Team velocity: !`git log --oneline --since='1 month ago' | wc -l` commits in last month
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- Historical data: Git history analysis for similar task completion patterns
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- 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"`
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- Sprint tracking: Linear task completion times and estimate accuracy
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## Task
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Execute comprehensive task estimation with historical analysis and confidence modeling:
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**Estimation Focus**: Use $ARGUMENTS for task description analysis, historical pattern matching, complexity assessment, or team velocity calculation
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**Estimation Framework**:
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1. **Historical Pattern Analysis** - Analyze similar past tasks, extract completion time patterns, identify velocity trends, calculate accuracy metrics
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2. **Complexity Assessment** - Evaluate technical complexity, assess scope uncertainty, identify risk factors, estimate effort distribution
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3. **Team Velocity Integration** - Calculate sprint velocity, analyze individual capacity, assess team expertise, factor in availability constraints
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4. **Confidence Modeling** - Generate confidence intervals, assess estimation uncertainty, identify risk factors, provide accuracy ranges
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5. **Calibration Analysis** - Compare past estimates vs actuals, identify systematic biases, calculate estimation accuracy, improve prediction models
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6. **Context Integration** - Factor in current sprint load, assess team familiarity, evaluate external dependencies, integrate deadline pressure
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**Advanced Features**: Multi-point estimation, Monte Carlo simulation, reference class forecasting, estimation accuracy tracking, bias correction algorithms.
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**Quality Metrics**: Estimation confidence levels, accuracy historical trends, velocity stability, complexity correlation analysis.
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**Output**: Data-driven estimates with confidence intervals, historical accuracy metrics, risk assessment, and calibration recommendations. |