---

name: attention-views description: "Route BertViz head_view and model_view attention-visualization tasks." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

attention-views

Use this sub-skill for BertViz attention visualizations built from bertviz.head_view.head_view, bertviz.model_view.model_view, and bertviz.util.format_attention.

Route here when

• You need self-attention, sentence-pair, or encoder-decoder/cross-attention visualizations.
• You need html_action='return' for saved HTML or non-notebook workflows.
• You need include_layers, include_heads, layer, heads, sentence_b_start, display_mode, or token prettification control.
• You want a quick offline smoke check using synthetic tensors.

Do not route here when

• The task is about query/key neuron inspection or get_attention; use ../neuron-view/SKILL.md.
• The task is generic Hugging Face training, serving, or model selection.
• The task needs vendored JS assets; BertViz loads its own package data.

Start with

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md
• scripts/render_synthetic_attention.py

Routing shortcuts

• Self-attention: pass attention plus tokens.
• Sentence pairs: add sentence_b_start to split the token list.
• Encoder-decoder: pass encoder_attention, decoder_attention, cross_attention, encoder_tokens, and decoder_tokens.
• head_view uses layer and heads as default selections.
• model_view uses include_layers and include_heads as filters, plus display_mode='dark'|'light'.
• For notebooks and scripts that save HTML, call with html_action='return' and write the returned .data.
• Use include_layers and include_heads to keep long inputs responsive.