Think inside your AI world.
Sentry + Linear through Unl
“Is it safe to ship?” is a frame question wearing an error-report’s clothes — the errors are only half of it; the other half is the scope you promised.
Sentry + Linear through Unl reads the live error state against the scope committed for the release, judged by the ship-safe gate you set, so the reply is go or no-go with the failing half named. The gate is held in Unl.
The criterion that binds them
Say you’ve ratified a ship-safe gate: no open severity-one issues in Sentry and the release’s committed scope closed in Linear, because shipping with either unmet is how a launch becomes a rollback.
The two naked reads
Sentry returns projects, issue detail and error retrieval. Linear returns the release scope and issue status. Both are accurate and partial; neither pairs the error picture to the scope commitment, so “safe to ship?” is decided by nerve.
The one measured answer
Against your gate: errors are clean — no open severity-one; but two committed scope items are still open in Linear. No-go by your bar, and it’s scope, not stability, holding it. The gate is two-part precisely so a quiet week on errors can’t wave through unfinished scope.
And back again
When you move one scope item out of the release, that decision is ratified in Unl — and the next ship-safe read judges the gate against the scope you now expect.
The answer comes back measured against what you already decided, and why.
A router can show Sentry errors beside Linear scope. It cannot rule go or no-go, because the ship-safe gate — both conditions and why they’re joined — lives in neither the error tool nor the tracker.
The lane is live and open to this tool today: 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
Can AI tell me if it’s safe to ship?
Yes, through Unl. You ratify a ship-safe gate — a Sentry error condition and a Linear scope condition, both required — and the two are read together against it, so the answer is go or no-go with the failing half named.
Isn’t a clean error dashboard enough to ship?
Clean errors are half the question; the other half is whether the promised scope is actually done. The gate that joins them lives in Unl, so the read won’t wave a release through on stability alone.
Does Unl change my Sentry or Linear data?
Unl reads through Sentry and Linear, and can write back on your explicit gesture — it never acts as a side effect of a read.
What this is
Think inside your AI world — you stay in command
Save the thoughts, decisions and targets worth keeping, each with its reasoning, carried into every AI session the moment they matter. A new unit of exchange between you and your AI: the Settled Why with standing that travels. Unprompted.
Your whole AI world. What you decided at the epicentre. It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector — quick to connect, in a couple of steps.
MCP native·Human settled·Model agnostic·Your data
Measured Context
Connect Unl to bring the right information into the moment.
Your sources, read against the criteria you set.
The full product, open. Free at launch.