Context engineering assembles what your AI knows. Spiderbrain takes the next step and understands it, reasoning about how everything connects the way you would.

Point Spiderbrain at a pile of things, files, records, trial criteria, and it scores how important each one is. Signal lift is how much higher the important few score than a typical item. Picture a 1,000-page manual where only a few pages are load-bearing: a signal lift of +140% (2.4×) means those pages score 140% more than the average page, so they jump out instead of hiding in the pile. The higher the lift, the easier it is to find the needle in the haystack.

Spiderbrain isn't just for code,  whether you're writing documents, organizing knowledge, analyzing insurance risk, understanding employee attrition, reviewing clinical trial data, exploring scientific research, managing HR information, or helping agents retain context, Spiderbrain is built to identify the signals that matter and suppress the noise that doesn't.

Each project generates metrics like: 

➝ Webscore: Severity. How much it would hurt if this one thing failed, on its own. Your login module scores 9.4/10. Lose it and everything goes down.
➝ Spikescore: Blast radius. If this breaks, how much breaks along with it. A shared config can be low-severity but high-spread, change it and dozens of things wobble.
➝ blastVolume: Total risk in one number, combining severity and blast radius. The single “how worried should I be?” score for a dashboard.
➝ Masters: Keystones. The few hubs everything leans on, which also act as firebreaks that stop damage spreading. One real codebase had 61 masters out of 1,672 files.
➝ Blind-spot index: Important but unexamined. Things that score critical yet no one has reviewed or documented. Catches the file that quietly became load-bearing while no one was watching.
➝ Drift: Going stale. Flags when something has changed in ways that no longer match its past. Tells you which knowledge has aged out, before it bites you.

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Spiderbrain

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