Think inside your AI world.
Should this key result be graded a miss?
A key result that landed near its target is exactly where grading gets argued: miss, or respectable partial? The answer is your scoring rule, not how the quarter felt — and if the rule was set in advance, the borderline result grades itself. Through Unl the read applies it, so the call is settled, not negotiated.
Borderline grades are where sentiment sneaks in: a good quarter rounds up, a hard one rounds down. A rule set before the result removes the sentiment. Hold it in Unl and the near-miss is graded against your standard, with the reason it lands where it does.
The borderline is where rules earn their keep
Clear wins and clear failures grade themselves. The value of a scoring rule shows up at the margin — the key result at seventy-two per cent, the one that hit the number late, the one that met the letter but not the spirit. Without a rule, these are decided by mood and advocacy; with one, they are decided by the standard.
So “should this be a miss?” is really “what does my rule say about a result like this?” The question only feels hard because the rule is usually not to hand at the moment of grading.
The rule settles it
Say your rule is ratified: a committed key result is a miss below ninety per cent regardless of how close, because committed means committed. Your flagship result lands at eighty-seven. The read grades it a miss, with the reason: it’s a committed goal below your ninety line, and you set that line so commitments couldn’t be rounded up.
Eighty-seven feels like a win and grades as a miss, and that is the rule working. You set it precisely to stop a strong-sounding number from laundering a broken commitment, and the read holds you to your own standard.
A grade you can stand behind
Because the grade follows a rule you set in advance, you can stand behind it — to yourself, to a board, to the team — without relitigating. The read is not being harsh or generous; it is applying the standard, and the reason is attached for anyone who asks.
And if you decide the rule was wrong — that eighty-seven on this goal should earn partial credit — you change the rule deliberately and ratify it for next time, rather than bending this grade. The standard evolves; individual grades don’t get negotiated.
Whether a near-target key result is a miss or a partial is decided by the scoring rule you set in advance, not by how the quarter felt; through Unl the read grades the borderline result against your standard, with the reason, so the call is settled and defensible — and the rule, not the individual grade, is what you revise.
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.
Read further
Questions people ask
Should a key result that landed close to target be graded a miss?
That depends on the scoring rule you set, not on how close it felt. If your rule says a committed key result is a miss below ninety per cent, then eighty-seven is a miss regardless of how strong it sounds. Through Unl the read applies your standard, so the borderline call is settled rather than argued.
How do I grade a borderline OKR result fairly?
With a rule set before the result is known. Borderline grades are where sentiment sneaks in — a good quarter rounds up, a hard one down. A ratified rule removes the mood, so the near-miss grades against your standard with the reason, and you can stand behind it without relitigating.
What if I think the grade my rule produces is wrong?
Change the rule, not the grade. Through Unl you revise the scoring rule deliberately and ratify it for next time, rather than bending this quarter’s grade — so the standard evolves while individual grades stay defensible and consistent.
What decides whether a near-target result is a partial or a miss?
The scoring rule you set in advance, not the feeling at quarter-end. Through Unl that ratified rule is applied to the number, so a result that landed close is graded the same way it would be on any objective, argument-free.
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.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
Alpha is invite-only · free at launch.