Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/lhbsaa/embedded-dev-skill/embedded-gui-feedbacknpx skills add lhbsaa/embedded-dev-skill --skill embedded-gui-feedbackgit clone --depth 1 https://github.com/lhbsaa/embedded-dev-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lhbsaa/embedded-dev-skill/embedded-gui-feedback)<a href="https://agentmods.dev/skills/lhbsaa/embedded-dev-skill/embedded-gui-feedback"><img src="https://agentmods.dev/badge/skills/lhbsaa/embedded-dev-skill/embedded-gui-feedback.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.01316 |
| Opus 5 | $0.00014 | $0.00658 |
| Sonnet 5 | $0.00006 | $0.00263 |
| Haiku 4.5 | $0.00003 | $0.00132 |
Grade A, and why
embedded-gui-feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embedded GUI Feedback
Overview
Visual verification for LCD/GUI projects. Human eye misses timing, alignment, and color issues. Camera capture + AI analysis catches them.
Core principle: Camera evidence, not human judgment.
Context: Run after embedded-verification passes for display projects.
When NOT to use: Projects without GUI/display components.
When to Use
- LCD driver projects
- Display configuration changes
- GUI layout updates
- Font/color modifications
- Any visual output project
Camera Setup
Requirements
- USB webcam (1080p recommended)
- Centered on LCD screen
- Uniform lighting (avoid glare)
- Distance: 15-30cm from screen
Windows Setup
# List cameras
python scripts/camera_capture.py --list
# Capture
python scripts/camera_capture.py --resolution 1920x1080 --session
Linux/macOS Setup
# List cameras
python scripts/camera_capture.py --list
# Capture
python scripts/camera_capture.py --resolution 1920x1080 --session
Process
1. Check Camera Available
Run: python scripts/camera_capture.py --list
If no cameras:
- Prompt user to connect camera
- Offer alternative: manual photo upload
2. Capture Display
Run: python scripts/camera_capture.py --session
Output: screenshots/capture_YYYYMMDD_HHMMSS.png
Session folder: Creates timestamped folder for multiple captures.
3. Image Analysis
Use image_read tool with analysis prompt:
Analyze LCD display for:
- Layout: Correct positioning?
- Font: Readable, correct size?
- Color: Expected colors displayed?
- Alignment: Elements aligned properly?
- Overlap: Any overlapping elements?
- Blank areas: Unexpected empty regions?
Compare to expected: [describe expected output]
4. Issue Detection
| Issue Type | Visual Indicator | Likely Cause |
|---|---|---|
| Blank screen | No pixels | Init sequence wrong |
| Partial display | Top/bottom missing | MADCTL value incorrect |
| Color wrong | Wrong colors | Color format mismatch |
| Offset display | Content shifted | y_gap/x_gap incorrect |
| Mirror/flip | Content reversed | MADCTL rotation bits |
| Flickering | Brightness varies | Refresh timing |
| Lines missing | Horizontal gaps | DMA chunking issue |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 245 lines · 28 tokens per session scan A 886269379818
embedded-gui-feedback is a skill published in the GitHub repository lhbsaa/embedded-dev-skill (9 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,316 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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esp32-log-triage
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embedded-debug
Firmware crash analysis, stack trace decoder, and register dump interpreter for ESP32/ARM/AVR platforms. Use when debugging device crashes, panics, guru meditation errors, hard faults, or analyzing core dumps.
ci-fix
Scan all CI builds and tests, find failures, fetch error logs, and fix the code. Prioritizes unit tests, example tests, then uno, attiny85, esp32s3, esp32c6, teensy41. Use when CI is red and you need to diagnose and repair build/test failures.
memory-audit
Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks. Use when investigating OOM crashes, optimizing memory usage, or reviewing memory-critical code on constrained devices.
timing-analysis
Analyze real-time constraints, ISR latency, DMA transfer times, and LED protocol timing for embedded systems. Use when debugging timing-sensitive code, optimizing frame rates, or verifying protocol compliance.