Think inside your AI world.
The objective health check, through Unl
An objective’s health is not the average of its key results — one critical key result below its bar can put the whole objective at risk while the average still looks fine. Through Unl each objective is read against your thresholds, so the health check names the real risk instead of averaging it into a reassuring colour.
Rolling key results up into a single colour hides exactly the thing a health check should surface: a critical result quietly under its bar. Hold each objective’s thresholds in Unl and the health check reads the parts that matter, so risk is named with its cause, not smoothed into an average.
Averages hide the critical miss
A common health check averages an objective’s key results into one status. That smooths away the signal: an objective with three healthy key results and one critical one below its bar averages to “mostly fine,” when in truth the critical miss puts the whole objective at risk. The average is arithmetic; health is judgement about which parts matter most.
So a rolled-up colour can report an objective green while its load-bearing key result is failing. The health check that most needs to catch that is the one an average defeats.
Reading the parts that matter
Say you lead a team whose objectives carry per-key-result thresholds and a note on which are load-bearing. The health check reads each objective against those: this objective is at risk — not because most key results are behind, but because its critical one has dropped below its bar, and that’s the one the objective depends on. The reason is named.
So your health check surfaces a genuinely at-risk objective that an average would have shown green, and clears an objective that looks patchy on average but whose critical key result is fine. Health is judged on structure, not smoothed into a mean.
Health you can act on
Because the check names the cause — which key result, how far below its bar, why it’s load-bearing — the response is targeted. You don’t rescue a whole objective; you act on the one result that put it at risk, which is where the leverage is.
The objective health check through Unl reads each objective against your thresholds and its structure, so health is a named verdict with a cause — the real risk surfaced with the reason — rather than an average that reassures you about the wrong thing.
An objective’s health is not the average of its key results — one critical result below its bar can put the whole objective at risk while the average looks fine; through Unl each objective is read against your thresholds and structure, so the health check names the real risk and its cause, rather than smoothing a load-bearing miss into a reassuring colour.
Reads through Unl arrive with measured context — in the presence of the decisions you’ve already settled. The reach lane is live: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
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Questions people ask
How do I check the real health of an objective?
Read it against per-key-result thresholds and which results are load-bearing, not an average. Through Unl the health check names an objective at risk because its critical key result dropped below its bar — the thing an average of all its key results would smooth into a reassuring colour.
Why can an objective look healthy but be at risk?
Because averaging its key results into one status hides a critical miss — three healthy results and one failing load-bearing one average to “mostly fine.” Health is about which parts matter most, which an average can’t capture, so the load-bearing failure stays hidden.
What does an objective health check through Unl show?
A named verdict with a cause — which key result put the objective at risk, how far below its bar, and why it’s load-bearing — so you act on the specific result rather than the whole objective. Health is judged on structure, not smoothed into a mean.
Why isn’t an objective’s health just the average of its key results?
Because one critical result below its bar can put the whole objective at risk while the average still looks fine. Through Unl the health check reads each key result against its own bar, so a single decisive miss surfaces instead of being averaged away.
What this is
Think inside your AI world — you stay in command
Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.
It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.
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