nvidia/skills · Official

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.

All-time #7496 First seen Jun 8, 2026
8-week activity · all time api

Installation

$ npx skills add nvidia/skills --skill tao-analyze-gaps-vlm-bcq

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from nvidia/skills · top by installs.

npx skills add nvidia/skills

Browse all from nvidia/skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 3.2K
License LICENSE-APACHE
Default branch main
Open issues 5
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version0.1.0
LicenseApache-2.0
CompatibilityRequires docker + nvidia-container-toolkit.
Allowed toolsRead Bash
More metadata
author
NVIDIA Corporation
version
0.1.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,786 B
  • docs SUMMARY.md 363 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,543 installs

SKILL.md

VLM Binary Classification Gap Analysis

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

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 positive) and FN (false negative) samples that downstream RCCA stages (e.g., cosmos generation, root cause analysis) consume to drive a DEFT iteration.

Usage

Invoke the vlm_bcq action inside the TAO Toolkit data services container with Hydra-style key=value overrides:

gap_analysis vlm_bcq \
  predictions_json=/path/to/results.json \
  results_dir=/path/to/output/gaps

Include videosdir when videoid values in the predictions are relative paths:

gap_analysis vlm_bcq \
  predictions_json=/path/to/results.json \
  results_dir=/path/to/output/gaps \
  videos_dir=/path/to/videos/root

After the run, surface the FP/FN counts from kpigapsreport.txt and point downstream stages at kpi_gaps.jsonl.

Inputs

  • predictionsjson: Path to predictions JSON file. Must be a JSON array where each item has videoid, response, and gt fields. response and gt are parsed with word-boundary matching — 'yes' or 'no' anywhere in the string is recognized. Samples where both or neither are present are skipped with a warning.
  • videosdir (optional): Base directory for resolving relative videoid paths. If omitted, video_id values are used as absolute paths.

Predictions JSON format:

[
  {
    "video_id": "/path/to/video.mp4",
    "response": "Yes, there is a collision.",
    "gt": "B. No",
    "question": "Is there a collision?"
  }
]

Outputs

  • kpigaps.jsonl: One JSON object per line for each FP/FN case. Fields: videoid (absolute path), errortype (FP or FN), question, groundtruth, response.
  • kpigapsreport.txt: Human-readable table with total FP/FN counts.

If no gaps are found, no files are written and a message is logged.

Key Parameters

Parameter Required Description
predictions_json Yes Path to predictions JSON file
results_dir Yes Output directory; created if it does not exist
videos_dir No Base directory for resolving relative video_id paths

Error Patterns

Error Cause Fix
FileNotFoundError predictions_json does not exist Check the path
ValueError: must be a JSON array Predictions file is not a list Wrap predictions in [...]
ValueError: missing 'gt'/'response'/'video_id' A prediction item is missing a required field Inspect and fix the predictions JSON
Samples silently skipped response or gt contains both or neither 'yes'/'no' Check logs for warnings; inspect those samples