Velocity Analysis

Velocity Analysis

Analyze velocity trends and predict capacity for "$ARGUMENTS". Chart historical velocity, calculate statistical measures, correlate changes with events, and recommend sprint commitment ranges.

Prerequisites

Check that .project/$ARGUMENTS/00-initiate.md exists. If it does not, tell the user to run an Initiate skill first (e.g., project-charter) and stop.

If the user passes --skip-checks, skip the prerequisite check and log the skip to .project/$ARGUMENTS/skip-log.md with a timestamp and reason.

Process

1. Read existing context:

   • Read .project/$ARGUMENTS/00-initiate.md for project context, team size, and sprint cadence
   • Read .project/$ARGUMENTS/03-review.md if it exists (for sprint review results, sprint metrics, and historical velocity data)
   • Read .project/$ARGUMENTS/04-improve.md if it exists (for retrospective action items and process changes that may have affected velocity)
   • Read .project/$ARGUMENTS/01-plan.md if it exists (for sprint planning data, committed points, and estimation approach)
   • Read .project/$ARGUMENTS/02-sprint.md if it exists (for sprint execution data and blocker history)

2. Chart velocity over last 5-10 sprints:

   • Collect completed story points for each of the last 5-10 sprints (or as many as are available)
   • Present velocity data as a table: sprint name/number, committed points, completed points, commitment ratio
   • Create a text-based chart showing velocity per sprint (horizontal bar chart or similar visualization)
   • If fewer than 5 sprints of data are available, note this limitation and adjust analysis accordingly
   • Separate velocity into categories if possible: new features, bug fixes, tech debt, unplanned work

3. Calculate average velocity and standard deviation:

   • Calculate the arithmetic mean of completed points across all available sprints
   • Calculate the standard deviation to measure velocity consistency
   • Calculate the coefficient of variation (standard deviation / mean x 100%) to express variability as a percentage
   • Interpret variability:
     • < 15% — highly predictable team, estimates are reliable
     • 15-30% — normal variability, plan with a range
     • > 30% — high variability, investigate causes before relying on velocity for planning
   • If available, calculate a weighted moving average (more recent sprints weighted higher) for a more current picture

4. Identify trends:

   • Determine the overall velocity direction: improving, declining, or stable
   • Use linear regression direction across the data points to classify the trend:
     • Improving — positive slope, team is delivering more over time
     • Declining — negative slope, team is delivering less over time
     • Stable — slope near zero (within +/- 5% of average), consistent delivery
   • If the trend is improving, identify likely causes: team maturity, better estimation, process improvements, reduced tech debt
   • If the trend is declining, flag for investigation: team changes, increasing tech debt, morale issues, scope complexity growth
   • Note any outlier sprints (more than 2 standard deviations from the mean) and explain them separately

5. Correlate velocity changes with events:

   • Review the project timeline for events that may explain velocity changes:
     • Team member changes — new joins (ramp-up period reduces velocity), departures (knowledge loss)
     • Holidays and vacations — reduced capacity sprints
     • Incidents and outages — unplanned work displacing planned work
     • Process changes — new practices from retrospectives or process improvements
     • Technical changes — major refactors, platform migrations, new tools
   • Map each significant velocity change to its likely cause
   • Distinguish between temporary dips (holidays, incidents) and structural shifts (team size change, process change)
   • Note which events had the largest impact on velocity and how long recovery took

6. Calculate sustainable velocity range:

   • Define sustainable velocity as the range the team can reliably deliver without burnout
   • Calculate the range: average velocity +/- 1 standard deviation
   • This gives a low estimate (conservative) and high estimate (optimistic)
   • Exclude outlier sprints from the calculation if they were caused by one-time events
   • If the team has been consistently above average for 3+ sprints, consider updating the baseline upward
   • If the team has been consistently below average, investigate whether the average is still realistic

7. Predict capacity for next 3 sprints:

   • For each of the next 3 sprints, estimate expected capacity:
     • Start with the sustainable velocity range
     • Adjust for known factors: planned vacations, holidays, team changes, expected incidents
     • Express as a range: conservative (low end minus known reductions) to optimistic (high end with ideal conditions)
   • Flag any sprints with significantly reduced capacity (holidays, team members out)
   • If the team is mid-trend (improving or declining), factor the trend direction into predictions
   • Note confidence level for each prediction: high (stable team, good data), medium (some unknowns), low (major changes expected)

8. Recommend sprint commitment range:

   • Based on the analysis, recommend how many story points to commit to in the next sprint
   • Provide two numbers:
     • Conservative commitment — what the team can almost certainly deliver (low end of sustainable range, rounded down)
     • Optimistic commitment — what the team could deliver if everything goes well (average velocity, rounded to nearest whole number)
   • Recommend which number to use based on the team's situation:
     • Use conservative when: team is rebuilding confidence, new members are ramping up, high-risk work ahead
     • Use optimistic when: team is stable and predictable, work is well-understood, team has momentum
   • Note that velocity is a planning tool, not a performance metric — do not use it to pressure the team

9. Write the artifact — append to .project/$ARGUMENTS/04-improve.md under a ## Velocity Analysis section:

   • Velocity History — table and chart of velocity over last 5-10 sprints
   • Statistical Summary — average velocity, standard deviation, coefficient of variation
   • Trend Analysis — direction (improving/declining/stable) with supporting data
   • Event Correlation — velocity changes mapped to team and project events
   • Sustainable Velocity Range — calculated range with methodology
   • Capacity Predictions — next 3 sprints with expected ranges and confidence levels
   • Commitment Recommendation — conservative and optimistic commitment for next sprint with guidance on which to use

Output

Velocity analysis appended to .project/$ARGUMENTS/04-improve.md. Present a summary highlighting:

• Average velocity and variability (coefficient of variation)
• Trend direction with key contributing factors
• Sustainable velocity range
• Recommended commitment for next sprint (conservative and optimistic)
• Any sprints with reduced capacity in the next 3-sprint window