deploy-edge-ai-model SKILL.md skillA
deploy-edge-ai-model from pjt222/agent-almanac is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add pjt222/agent-almanac/deploy-edge-ai-model@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- pjt222/agent-almanac
- Path
i18n/caveman-lite/skills/deploy-edge-ai-model/SKILL.md- Versions recorded
- 4
- Latest version
- v1.0, committed Aug 17, 2026
- First version recorded
- Apr 19, 2026
- Size
- 17,546 bytes
- Repository stars
- 43
- License
- MIT
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Deploy Edge AI Model > See [Extended Examples](references/EXAMPLES.md) for complete configuration files, quantization scripts, and benchmark templates. Deploy ML models to edge devices with optimized inference, hardware acceleration, and on-device model management. ## When to Use - Deploying LLMs (Gemma 4, Phi, Llama) to mobile devices via Google AI Edge Gallery - Converting models to TensorFlow Lite or ONNX for on-device inference - Quantizing models to INT8/INT4 for reduced memory and faster inference - Building Android/iOS apps with local AI capabilities - Selecting hardware delegates (GPU, NPU, DSP, Hexagon, CoreML) - Benchmarking inference latency and memory on target devices - Deploying MediaPipe tasks (vision, text, audio) to mobile or embedded platforms ## Inputs - **Required**: Trained model (SavedModel, PyTorch, ONNX, or Hugging Face checkpoint) - **Required**: Target platform (Android, iOS, Linux embedded, browser) - **Required**: Target device constraints (RAM, storage, compute capability) - **Optional**: Calibration dataset for post-training quantization - **Optional**: Google AI Edge Gallery configuration for LLM deployment …
Read the whole file at its exact version.
How to install
mdr add pjt222/agent-almanac/deploy-edge-ai-model@sha256:b8aabed8b7f3a3ccThe line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_ifh66imjv3qkaavx)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 4 versions of this file, between Apr 19, 2026 and Aug 17, 2026, about one every 40 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0 latest | 2026-08-17 | a784899 | 17,546 B | A | view · diff |
| v1.0 | 2026-06-05 | 75ded7a | 17,517 B | A | view · diff |
| v1.0 | 2026-04-23 | eb36d45 | 17,576 B | A | view · diff |
| v1.0 | 2026-04-19 | e334ebd | 17,631 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (17546 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
pjt222/agent-almanac · 43 stars · license MIT · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ifh66imjv3qkaavx GET https://markdownregistry.com/api/v1/resolve?ref=pjt222/agent-almanac/deploy-edge-ai-model GET https://markdownregistry.com/api/v1/blob/b8aabed8b7f3a3cc3679f15322e1a04ed6640092f910c1e4745fad593409ec18
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.