Skill · em Dados, IA e pesquisa
tensorboard
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
Procedência
- Origem: davila7/claude-code-templates
- Caminho:
cli-tool/components/skills/ai-research/mlops-tensorboard - Versão fixada:
57f899e5394bb8ca166f38eacae8f0853cbfe033 - Licença: MIT
- Espelhado em 25/09/2026
- nenhum download no Claude Code Templates (lido em 25/09/2026)
Antes de instalar
4 arquivos · 58,3 KB · só texto, nenhum script
Instalar na sua CLI
O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.
Claude Code
Neste projeto: instala em .claude/skills/tensorboard/.
d=".claude/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Global: instala em ~/.claude/skills/tensorboard/.
d="$HOME/.claude/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Codex
Neste projeto: instala em .agents/skills/tensorboard/.
d=".agents/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Global: instala em ~/.agents/skills/tensorboard/.
d="$HOME/.agents/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Antigravity
Neste projeto: instala em .agents/skills/tensorboard/.
d=".agents/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Global: instala em ~/.gemini/antigravity-cli/skills/tensorboard/.
d="$HOME/.gemini/antigravity-cli/skills/tensorboard" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-tensorboard" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/integrations.md" "$u/references/integrations.md" \ -o "$d/references/profiling.md" "$u/references/profiling.md" \ -o "$d/references/visualization.md" "$u/references/visualization.md"
Peça ao Rook
Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.tensorboard
Prévia do SKILL.md
---
name: tensorboard
description: Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [MLOps, TensorBoard, Visualization, Training Metrics, Model Debugging, PyTorch, TensorFlow, Experiment Tracking, Performance Profiling]
dependencies: [tensorboard, torch, tensorflow]
---
# TensorBoard: Visualization Toolkit for ML
## When to Use This Skill
Use TensorBoard when you need to:
- **Visualize training metrics** like loss and accuracy over time
- **Debug models** with histograms and distributions
- **Compare experiments** across multiple runs
- **Visualize model graphs** and architecture
- **Project embeddings** to lower dimensions (t-SNE, PCA)
- **Track hyperparameter** experiments
- **Profile performance** and identify bottlenecks
- **Visualize images and text** during training
**Users**: 20M+ downloads/year | **GitHub Stars**: 27k+ | **License**: Apache 2.0
## Installation
```bash
# Install TensorBoard
pip install tensorboard
# PyTorch integration
pip install torch torchvision tensorboard
# TensorFlow integration (TensorBoard included)
pip install tensorflow
# Launch TensorBoard
tensorboard --logdir=runs
…