kindle-tablet-ui-review

Review and improve AetherCode UI for Kindle Fire tablet constraints (7-11 inch displays, touch ergonomics, offline resilience).

6 stars

Best use case

kindle-tablet-ui-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Review and improve AetherCode UI for Kindle Fire tablet constraints (7-11 inch displays, touch ergonomics, offline resilience).

Teams using kindle-tablet-ui-review should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/kindle-tablet-ui-review/SKILL.md --create-dirs "https://raw.githubusercontent.com/issdandavis/SCBE-AETHERMOORE/main/external/codex-skills-live/kindle-tablet-ui-review/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/kindle-tablet-ui-review/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How kindle-tablet-ui-review Compares

Feature / Agentkindle-tablet-ui-reviewStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Review and improve AetherCode UI for Kindle Fire tablet constraints (7-11 inch displays, touch ergonomics, offline resilience).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Kindle Tablet UI Review

Use this skill when refining the Kindle app UX for real tablet usage.

## Review Checklist
1. Touch targets: at least 48dp-equivalent for primary actions.
2. Readability: minimum body text 14-16px with high contrast.
3. Layout: avoid dense two-column flows on 7-inch devices.
4. Offline mode: clear state indicator when network drops.
5. Latency guard: show loading states for remote model calls.
6. Navigation: keep key controls in thumb-accessible zones in landscape.

## Working Method
1. Compare `src/aethercode/app.html` and `kindle-app/www/index.html`.
2. Patch source UI first (`src/aethercode/app.html`), then regenerate `www` assets:
```powershell
Set-Location C:\Users\issda\SCBE-AETHERMOORE\kindle-app
npm run copy:assets
```
3. Validate on Kindle via ADB install.
4. Emit cross-talk packet with before/after notes and screenshot paths.

## Minimum Evidence
- Updated file paths
- At least one device-targeted test note (7", 8", 10", or 11")
- One clear UX improvement claim tied to a visible change

Related Skills

scbe-ai2ai-kindle-research

6
from issdandavis/SCBE-AETHERMOORE

Run Kindle-focused AI2AI research/debate workflows through n8n and SCBE bridge endpoints with governance thresholds.

multi-agent-review-gate

6
from issdandavis/SCBE-AETHERMOORE

Run a structured review gate before merging multi-agent outputs. Use when multiple agents have produced work packets that need quality checks, conflict detection, and approval before integration.

kindle-app-ops

6
from issdandavis/SCBE-AETHERMOORE

Build, sideload, and validate the SCBE Kindle/Fire OS app loop with Capacitor, ADB, and release-readiness checks.

kindle-app-delivery

6
from issdandavis/SCBE-AETHERMOORE

Build, sideload, and publish the SCBE Kindle/Fire app using Capacitor, Gradle, ADB, and Amazon Appstore checklist steps.

scbe-training-pair-authoring

6
from issdandavis/SCBE-AETHERMOORE

Create prompt and response and metadata training pairs from SCBE documents, repair traces, terminal sessions, and operational workflows using the repository's canonical dataset contract and provenance rules.

scbe-spin-conversation-engine

6
from issdandavis/SCBE-AETHERMOORE

Generate SFT training data via radial matrix conversation pivots with D&D-style combat research mode. Produces diverse, cost-effective training pairs with Sacred Tongue encoding, golden spiral problem distribution, and harmonic re-attunement.

scbe-research-training-bridge

6
from issdandavis/SCBE-AETHERMOORE

Stage arXiv evidence and Obsidian markdown into source-grounded Hugging Face training bundles for research, review, and later SFT runs.

scbe-document-management

6
from issdandavis/SCBE-AETHERMOORE

Consolidate overlapping docs, classify files by authority, and keep SCBE repo documents aligned with runtime truth. Use when the repo has drift between canonical docs, public docs, proposal notes, research branches, and generated evidence.

scbe-colab-bridge

6
from issdandavis/SCBE-AETHERMOORE

Control Google Colab notebooks from Claude Code via Chrome extension. Execute cells, run terminal commands, read outputs, and manage GPU compute remotely.

scbe-claim-to-code-evidence

6
from issdandavis/SCBE-AETHERMOORE

Map SCBE Notion technical claims, proof pages, and patent-facing architecture notes to concrete repository evidence such as code paths, tests, demos, and docs. Use when Codex needs to build a due-diligence packet, claim-to-code audit, implementation crosswalk, patent support note, or proof summary from local Notion exports and repo artifacts.

scbe-autonomous-worker-productizer

6
from issdandavis/SCBE-AETHERMOORE

Turn SCBE automation, autonomous worker, and revenue-system notes into concrete offers, workflow packs, pilot plans, or SaaS-facing product packets. Use when Codex needs to package Notion automation pages into buyer-ready offerings, n8n/Zapier workflow designs, flock-backed worker systems, or implementation roadmaps tied to existing SCBE repo surfaces.

multi-agent-cloud-offload

6
from issdandavis/SCBE-AETHERMOORE

Deterministically sort, bundle, verify, and offshore local files through multiple AI/model lanes while capturing training rows and method evidence. Use when Codex needs to inventory folders, batch-process files, upload them to cloud targets such as rclone-backed Google Drive, Hugging Face, or GitHub, and only delete sources after the configured number of verified targets succeed.