People Metrics

Capability fit — is assignment producing matched work?

The single most useful people metric is capability fit: across recent assignments, how well does the assignee's skill profile match the task's skill tags?

Kavanah computes this as the geometric mean of the assignee's strength on the task's skill tags. A score near 1.0 means the assignee is well-matched; a score near 0.5 means the assignee is plausible but not ideal; below 0.4 means the assignment was a stretch.

The metric should sit in the 0.6–0.8 band for a healthy team. Below 0.6 across the board means assignment is defaulting to availability or social proximity, not capability. Above 0.85 across the board means you are over-typecasting — every task goes to the most obvious person — and nobody is growing.

The action when capability fit drifts low: audit the recommend_assignees acceptance rate. The recommender is probably surfacing better matches and being overruled. Find out why.

Stretch rate — are people getting growth assignments?

Capability fit measures match quality; stretch rate measures growth opportunity. It is the fraction of assignments per member where the assignee was not the top capability recommendation but was a deliberate stretch — usually with a stronger member available to consult.

A healthy team has a stretch rate around 15–25%. Most assignments go to the best match (efficiency), but a meaningful slice goes to a stretch (growth). Zero stretch rate produces a team that ships fast and stagnates; 50% stretch rate produces a team that grows fast and ships unevenly.

Kavanah surfaces stretch rate per member: 'Sarah Chen — 22% stretch in the last quarter, all in skills where she's now climbing from Proficient to Expert.' That is a useful coaching artifact. It is not a performance metric — high stretch and high stretch-task completion both look healthy.

Throughput per member — with skill weighting

Raw task count per member is a misleading metric because tasks are not interchangeable. A senior contributor shipping three load-bearing platform tasks per sprint is producing more value than the same person shipping ten cleanup tasks.

The useful version is throughput weighted by task estimate: how many estimated-hours of work each member shipped per sprint. This is comparable across members in the same way a sprint plan is comparable across members.

The healthy signal is stable per-member throughput sprint over sprint, with seasonal variation matching capacity (leave, focus blocks). The unhealthy signal is a downward trend on one member with no explanation, which is usually the early sign of burnout, disengagement, or a brewing performance issue. The first action is a 1:1 conversation, not a metric review.

Do not publish raw throughput-per-member as a leaderboard. It will corrupt the data within a quarter (members game the easy tasks). It is a 1:1 input, not a public scoreboard.

Active load distribution — the burnout early warning

The single most useful early-warning metric for burnout is the active task count distribution across the team. Specifically, the p90 of active tasks per member.

When p90 active load is 1–2, the team is healthy. When it climbs to 3, the most loaded members are starting to context-switch in costly ways. When it reaches 4+, the system is loaded past sustainable capacity, and the next month will produce both quality issues and a member quietly disengaging.

The Resources tab on /portfolio surfaces the distribution as a horizontal bar; the p90 is visible at a glance. The action when p90 climbs is to redistribute — even at the cost of short-term context-loss — because the alternative is burnout. The system supports this with availability requests and clean reassignment.

Skill growth — are people getting more capable over time?

The reinforcement loop produces a measurable signal: how many members crossed a proficiency threshold (Learning → Proficient, Proficient → Expert) on at least one skill in the last quarter.

A team where every active member is growing on at least one skill per quarter is a team that is learning. A team where most members are flat over a quarter is either not getting stretch assignments, not getting their skill tags reinforced (untagged tasks), or not getting the kinds of work that produce growth.

The action when growth flattens is to look at the assignment patterns. If everyone is doing what they already know, the team is optimizing for short-term throughput at the cost of long-term capability. A 10% bump in stretch rate often fixes this within two quarters.

Idle ratio — the soft signal you usually miss

Idle ratio is the fraction of recent days where a member had no active task and no scheduled focus block. It is a soft signal — sometimes idle means resting, sometimes it means stuck, sometimes it means the system isn't routing work to them.

A healthy team has individual idle ratios under 10% with occasional spikes. A spike on one member (say, 30% idle over two weeks) is a coaching signal: ask what is going on. The default assumption should be 'I am routing them wrong' before 'they are slacking.' The former produces a system fix; the latter produces a defensive employee.

Kavanah surfaces the idle ratio on the Resources tab. The intent is not to police it. It is to make visible the case where the system is failing a member — assignments not being routed their way, capabilities under-used — before the member feels obligated to escalate.