Use when you need to VISUALLY confirm a specific event in a QuakeWorld demo — a flagged rocket/explosive jump, a teleport, a contact, a suspect route segment, "did this really happen at match-time T?" — by rendering that player's first-person POV (1 screenshot per second, full HUD) on the real GPU and reading the frames yourself. The closest-to-truth check for any detector decision (e.g. route_observatory's suspect_trick verifier). aws-dev orchestrates the GPU box (winnacle) which owns demoshots; you pull the frames and read them. Pairs with route-signature / eval-integrity (same validate-by-eye-then-read-the-render philosophy).
Grab ONE real ezQuake first-person POV screenshot (full HUD) of a QuakeWorld demo at an exact player + demo-second, rendered headless on servexeri (the always-on Linux hub) and pulled to aws-dev for Claude to read. The single-moment / Linux-headless sibling of the demo-eyecheck skill (which renders a whole 1-fps POV sweep on the Windows GPU box). Use when you need to SEE exactly what a player saw at a moment in a demo — validate a route START/END spot, a spawn position, a trick frame, "is this the right place?", "show me <player>'s POV at T". Wraps the owner's proven clipsmith render.cfg recipe (f_spawn -> demo_jump -> track -> screenshot). Triggers: "screenshot this demo at second N", "grab <player>'s POV", "in-game shot of <demo> at T".
Use when mapping a dm3 route from real play or extracting/validating a player's MOVEMENT SIGNATURE along a route leg from a parsed MVD demo (+ POV frames) — i.e. producing the route-conditioned BC target or the believability rubric, or fusing a POV recording against demo state. Turns the resource→route canon into per-route human signatures (speed profile, jump cadence, look-vs-move) and a self-validatable POV+route fused view. Pairs with eval-integrity (the fused state must match the pixels) and the route_observatory route canon.
MANDATORY whenever evaluating, validating, summarizing, or reporting ANY data — eval results, metrics, gates, benchmarks, experiment or test output, training runs, measurements — and before stating ANY conclusion about what the data shows. Counters overstating results. Forces validating a metric's MEANING (not just its value), tagging every number's provenance, inspecting the ground-truth artifact, applying equal skepticism to passes and fails, and keeping claims within the evidence. Apply it on every single data evaluation, positive or negative.