Individual skill
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.
skills/tao-analyze-gaps-vlm-bcq/SKILL.md
Source commit fce3cca4170be75e82c64920654d56ee2811b005
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- Last checked
- Nov 9, 2026
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- 3,931
- Repository last updated
- Nov 11, 2026
- License
- Apache-2.0
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- gap-analysis Declared skill tag
- rcca Declared skill tag
- vlm Declared skill tag
- evaluation Declared skill tag
- false-positive Declared skill tag
- false-negative Declared skill tag
- operations Derived category
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BENCHMARK.mddoc · 7,087 bytesSKILL.mdcore · 5,261 bytesSKILL.mdcore · 5,261 bytesSKILL.mdcore · 5,261 bytesassets/default_vlm_bcq.yamlasset · 54 bytesconfig/skillspector-baseline.yamlasset · 1,102 bytesevals/evals.jsonasset · 873 bytesreferences/skill_info.yamlasset · 871 bytesscripts/prepare_vlm_bcq_spec.pyruns · 2,480 bytesskill-card.mddoc · 4,853 bytesskill.oms.sigasset · 5,573 bytes
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# 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. Run it with a TAO Data Services spec file; the data-services entrypoint requires `-e <spec>`.
## 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
Generate a `vlm_bcq_spec.yaml` with the bundled helper:
