Agent skill · nvidia
warp-debug-gradients
Use to diagnose and fix incorrect gradients in differentiable Warp programs. Anything trained, optimized, calibrated, or fit through Warp kernels depends on wp.Tape gradients, so treat any misbehavior of such a workflow as a gradient problem until proven otherwise — use this when training diverges or NaNs, won't train at all, stalls or plateaus above the expected loss, converges to a wrong or biased answer, is worse than a reference implementation, works at small scale but fails at production scale, or fails a QA/validation recheck. Also for explicit symptoms — exploding, NaN/inf, zero, or subtly wrong gradients, suspected wp.Tape/backward issues, gradcheck failures — but users usually describe only the surface symptom ("the sim explodes", "the fit gets dragged toward outliers") without mentioning gradients: make that leap. Not for forward-only Warp work, build/install problems, or autograd issues in other frameworks without Warp.
What it needs
About 11k tokens when loaded.
What this skill does
Debugging Gradients in Warp Gradient bugs in Warp are almost never math bugs. The forward simulation looks perfectly healthy while the backward pass silently reads clobbered values, skips arrays, or double-counts adjoints. Users routinely burn days tuning physics knobs, loss functions, and assets when the real cause is a two-line taping-pattern fix. Your job is to find that fix with evidence, not intuition. The single most important discipline: measure before hypothesizing. It is cheap for you to run a shrunk reproduction and compare autodiff against finite differences. The way the gradient is wrong (its signature) prunes the hypothesis space far faster than reading code ever will. Do not start proposing fixes from code reading alone — plausible-looking diagnoses of differentiability bugs are very often wrong, and an unverified "fix" that happens to perturb the numbers wastes everyone's time. When to Use This Skill Anything trained, optimized, calibrated, or fit through Warp kernels flows through wp.Tape gradients — so when such a workflow misbehaves, gradients are the prime suspect even if the user never says the word. Activate on the symptoms users actually report: training that diverges, NaNs, or does nothing; loss that stalls or plateaus above where it should; fits that converge to a wrong or biased answer or are worse than a reference implementation; pipelines that work at small scale but fail at production scale or fail a QA recheck. Also activate on explicit gradient symptoms — exploding, NaN/inf, zero, or subtly wrong gradients, wp.autograd.gradcheck failures, suspected wp.Tape/backward issues — and when the user asks whether their gradients can be trusted. Do not activate for forward-only Warp work (kernel authoring, rendering, performance tuning), Warp build or installation problems, autograd questions in other frameworks with no Warp involvement, or pure performance work on a backward pass whose gradients the user has already validated. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/warp-debug-gradients