Skip to content
Back to skills

Optimization Building Blocks

ASecurity

"Routes advanced keras-vis customization requests that combine

  • 247 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 8, 2026
toolsrailsdebuggingapi

Works with

  • api

Security analysis

A100/100

Pro scans all 5 files and shows the line behind each finding

Scanned September 8, 2026

npx -y skills add VectorSpaceLab/AREX-Skill --skill optimization-building-blocks --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Optimization Building Blocks?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Optimization Building Blocks
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/vectorspacelab-optimization-building-blocks/badge)](https://www.skillsdirectory.com/skills/vectorspacelab-optimization-building-blocks)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: optimization-building-blocks
description: "Routes advanced keras-vis customization requests that combine
  custom losses, regularizers, Optimizer control, input or gradient modifiers,
  backprop modifiers, and callbacks."
disable-model-invocation: true
metadata:
  disco-role: operating
license: MIT
---
# optimization-building-blocks

Use this sub-skill for low-level optimization pieces that sit underneath the higher-level activation and saliency flows.

## Route away first
- For high-level activation maximization requests, use [activation-maximization](../activation-maximization/SKILL.md).
- For saliency maps or Grad-CAM, use [saliency-and-cam](../saliency-and-cam/SKILL.md).
- For image loading, drawing, stitching, and display helpers, use [image-utilities](../image-utilities/SKILL.md).

## Stay here for
- Writing custom `Loss.build_loss()` implementations.
- Combining weighted loss tuples with `Optimizer`.
- Choosing `wrt_tensor`, `grad_modifier`, `input_modifiers`, and callbacks.
- Using `ActivationMaximization`, `TotalVariation`, `LPNorm`, `Jitter`, `Print`, `GifGenerator`, and gradient/backprop modifiers directly.
- Debugging TensorFlow guided/rectified backprop overrides, graph mode, and optional callback dependencies.

## Reference map
- API signatures and constraints: [references/api-reference.md](references/api-reference.md)
- Practical workflow and composition rules: [references/customization-guide.md](references/customization-guide.md)
- Known failures and fixes: [references/troubleshooting.md](references/troubleshooting.md)
- Best-effort deterministic smoke check: [scripts/optimizer_smoke.py](scripts/optimizer_smoke.py)

## Guardrails
- `Optimizer.minimize()` uses a fixed RMSProp-style loop with internal loss weighting; it only updates the input tensor itself.
- When `wrt_tensor` is not the input tensor, treat the loop as a gradient probe and read `grads` / `wrt_value`.
- `verbose=True` already adds the bundled `Print` callback.
- TensorFlow backprop modifiers require graph mode and do not support advanced activations here.
- `GifGenerator` needs both `imageio` and Pillow at runtime.

Files in this skill

  • SKILL.md2.1 KB
  • references/api-reference.md5.4 KB
  • references/customization-guide.md4.3 KB
  • references/troubleshooting.md5.6 KB
  • scripts/optimizer_smoke.py3.4 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.