tao-analyze-gaps-vlm-bcq
Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure cases from a predictions JSON for DEFT root-cause analysis on a binary-classification VLM workflow.
npx skills add NVIDIA/skills --skill tao-analyze-gaps-vlm-bcq --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# VLM Binary Classification Gap Analysis Reads a VLM predictions JSON, compares each model response against ground truth, and writes FP/FN failure cases to a JSONL file with a summary report. ## Purpose After running a VLM on a binary yes/no evaluation task, the predictions need to be compared against ground truth to identify failure cases. This skill produces a structured list of FP (false positi
What does the tao-analyze-gaps-vlm-bcq skill do?
Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure cases from a predictions JSON for DEFT root-cause analysis on a binary-classification VLM workflow.
How do I install it?
Run `npx skills add NVIDIA/skills --skill tao-analyze-gaps-vlm-bcq --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.