Turn Linear's velocity data into metric trees that prove engineering drives business outcomes.

Bug Escape Rate measures the percentage of bugs that are discovered in production rather than caught during development or QA stages in Linear. It quantifies the effectiveness of pre-release quality gates and testing processes.

Code Review Bottleneck Analysis examines the time Linear issues spend waiting for or undergoing code review. It identifies reviewer capacity constraints, uneven review distribution, and workflow states where issues accumulate, slowing overall delivery throughput.

Comment Collaboration Rate tracks the volume and distribution of comments on Linear issues, measuring how actively team members engage in discussions, share context, and collaborate on solutions. It distinguishes between meaningful collaboration and minimal status updates.

Cross-Team Dependency Impact measures how dependencies between Linear teams affect delivery timelines. It quantifies the additional cycle time, blocked time, and coordination overhead caused by cross-team handoffs and blockers.

Cycle Burndown Rate tracks the rate at which remaining work decreases during a Linear cycle. It compares the actual burndown trajectory against the ideal straight-line path, revealing whether the team is on pace to complete their cycle commitment.

Cycle Commitment Accuracy measures the percentage of issues committed at the start of a Linear cycle that are completed by cycle end. It excludes work added mid-cycle to provide a clean measure of planning accuracy.

Developer Workload Balance analyses the distribution of assigned issues, estimated effort, and active work across team members in Linear. It identifies imbalances where some developers carry disproportionate loads while others have available capacity.

Epic Completion Forecasting uses historical team velocity data and remaining scope to predict when Linear projects and epics will be completed. It applies probabilistic models to provide a range of likely completion dates rather than a single point estimate.

Feature Delivery Cycle Time measures the total elapsed time from when work begins on a feature in Linear to when it is delivered. It captures the full pipeline duration including development, review, testing, and deployment stages.

Integration Impact Analysis evaluates how connected tools and integrations with Linear affect team workflows, productivity, and data quality. It measures the volume and value of automated actions, synced data, and cross-tool workflows.

Issue Aging Analysis examines the age profile of open issues in Linear, categorising them into brackets to reveal how much of the backlog is recent versus stale. It tracks aging trends over time and identifies patterns in which issue types age fastest.

Issue Priority Distribution Analysis examines the proportion of Linear issues at each priority level over time. It detects priority inflation, where too many issues are marked urgent or high, and identifies whether prioritisation practices are producing a workable distribution.

Issue Reopening Rate measures the percentage of Linear issues that are moved back to an active state after being marked as done. It serves as a quality indicator, reflecting whether work is genuinely complete when marked as such.

Issue Resolution Time measures the total elapsed time from when a Linear issue is created to when it is resolved. It encompasses both waiting time and active work time, providing a full lifecycle view of how long issues take to address.

Label Work Classification Analysis examines how Linear labels are used to categorise work into types such as features, bugs, improvements, and maintenance. It measures the distribution of effort across work categories and the consistency of labelling practices.

Milestone Delivery Predictability measures the consistency with which Linear milestones and projects are delivered within their target dates. It tracks the variance between planned and actual delivery across milestones to assess organisational forecasting reliability.

Project Health Score is a composite metric that combines multiple Linear project indicators, such as issue completion progress, velocity trends, reopening rates, and milestone adherence, into a single normalised score for at-a-glance project assessment.

Roadmap Progress Tracking monitors the advancement of strategic initiatives on the Linear roadmap by aggregating progress across constituent projects and milestones. It provides a high-level view of whether the organisation is executing against its planned direction.

Seasonal Development Patterns identifies recurring cyclical trends in Linear development activity, such as productivity dips during holiday periods, velocity surges before major releases, or reduced throughput during hiring seasons. It helps teams anticipate and plan for predictable capacity fluctuations.

Team Capacity Utilisation measures the proportion of available team capacity that is actively allocated to Linear issues. It compares the total estimated work assigned to the team against their available capacity, accounting for team size and any known availability constraints.

Team Velocity Analysis measures and analyses the amount of work completed per cycle by Linear teams. It tracks velocity trends, variability, and the factors that influence throughput to provide a reliable basis for capacity planning and delivery forecasting.

Technical Debt Accumulation Rate measures the net change in technical debt issues in Linear over time. It compares the rate at which new debt issues are created against the rate at which existing debt is resolved, indicating whether overall debt levels are growing or shrinking.

User Story Size Consistency analyses the distribution and variance of issue size estimates in Linear. It measures whether teams consistently break work into similarly sized pieces and identifies patterns of oversized or undersized stories that affect planning accuracy.

Workflow State Transition Analysis examines how issues move through Linear workflow states, including forward progress, backwards transitions, and time spent in each state. It identifies the most common transition paths, bottleneck states, and unexpected backflows that indicate process problems.