Agent skill

Model Download Dev

by open-edge-platform in open-edge-platform/edge-ai-libraries

Extend, test, debug, or integrate the Model Download microservice codebase.

Apache-2.0Auto-check passedDevOps & Cloud

Install Model Download Dev

skills CLI
$ npx skills add open-edge-platform/edge-ai-libraries --skill model-download-dev -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install open-edge-platform/edge-ai-libraries model-download-dev --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .claude/skills && cp -r skills-src/microservices/model-download/.github/skills/model-download-dev .claude/skills/model-download-dev && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
model-download-dev
GitHub stars
169
Token cost
~2k tokens
SKILL.md length
572 words
Files
8 (incl. references)
Skills in repo
29
Repo updated
First seen
Licence
Apache-2.0

At a glance

Extend, test, debug, or integrate the Model Download microservice codebase.

  • Works in 3 steps: examples-prompts/plugin-blueprint.md for… → examples-prompts/new-downloader-plugin.md… → examples-prompts/writing-tests.md for…
  • A developer wants to: add a new plugin to the microservice
  • SKILL.md covers When to Use, Reference Lookup, Example Prompts and Plugin Architecture Summary, plus 3 more sections
  • Calls curl, docker and python3

What it does

Model Download Dev is an agent skill from open-edge-platform/edge-ai-libraries. Extend, test, debug, or integrate the Model Download microservice codebase. Use this skill when a developer wants to: add a new plugin to the microservice; write tests for a plugin (including mocking subprocess calls, async methods, or the Ollama server); debug a job stuck in "downloading" or "converting"; understand the plugin interface or registration mechanism; trace how a request flows through ModelManager; extend the OpenVINO conversion parameters; add a new ModelHub value; or embed model-download into an…

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `evals/evals.json`, `examples-prompts/new-downloader-plugin.md` and `examples-prompts/plugin-blueprint.md`).

It sits in DevOps & Cloud, covering Microservices, Test generation and Container orchestration. It works with Ollama and Docker. The repository describes itself as: Libraries, microservices, tools, and other reference software, supporting development of performance-optimized Edge AI applications. The licence is Apache-2.0.

When your agent uses it

  • A developer wants to: add a new plugin to the microservice
  • Write tests for a plugin (including mocking subprocess calls
  • The Ollama server)
  • Debug a job stuck in downloading

Example prompts

  • “downloading”
  • “converting”
  • “add plugin”
  • “/model-download-dev”

Requirements

  • Python 3
  • Docker

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. examples-prompts/plugin-blueprint.md for the reusable class skeleton
  2. examples-prompts/new-downloader-plugin.md for the end-to-end wiring
  3. examples-prompts/writing-tests.md for the unit-test shape

What it can do on your machine

Read from SKILL.md and the folder at commit 3084578. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • docker
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl and docker, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Download Dev loads about 2k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 224 tokens; SKILL.md has 572 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~224
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from open-edge-platform/edge-ai-libraries at commit 3084578, republished under its Apache-2.0 licence (© open-edge-platform). 572 words, ~1,986 tokens.

Download SKILL.mdSave it as .claude/skills/model-download-dev/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
model-download-dev
description
Extend, test, debug, or integrate the Model Download microservice codebase. Use this skill when a developer wants to: add a new plugin to the microservice; write tests for a plugin (including mocking subprocess calls, async methods, or the Ollama server); debug a job stuck in "downloading" or "converting"; understand the plugin interface or registration mechanism; trace how a request flows through ModelManager; extend the OpenVINO conversion parameters; add a new ModelHub value; or embed model-download into an app, Docker Compose stack, Helm deployment, CI/CD flow, or startup path. Trigger on phrases like "add plugin", "write test", "stuck job", "extend microservice", "plugin not working", "how does model_manager work", "mock subprocess", "register new hub", "integrate model-download", "call the model-download API", "poll model job", or "mount downloaded models".
argument-hint
Describe what you want to build or debug (e.g. "add a new downloader plugin for an internal model hub" or "wire model-download into our compose stack")

Model Download Developer Skill

Help developers extend, test, debug, and integrate the Model Download microservice.

Codebase root: microservices/model-download/

When to Use

  • Adding a new download or conversion plugin
  • Writing unit tests for a plugin (subprocess mocking, async fixtures)
  • Debugging a job stuck in downloading or converting
  • Understanding how ModelManager, PluginRegistry, or PluginVenv work
  • Extending the ModelHub enum or Config schema
  • Tracing plugin activation and ACTIVATED_PLUGINS env flow
  • Integrating model-download into a backend, gateway, Compose stack, Helm deployment, or CI/CD path
  • Designing app-side download/conversion workflows around /models/download and /jobs/{job_id}
  • Wiring model storage, health checks, plugin activation, and failure handling into a wider system

Reference Lookup

ReferenceWhen to read
plugin-architecture.mdPlugin interface contract, PluginRegistry, ModelManager, PluginVenv
testing-patterns.mdSubprocess mocking, async fixtures, conftest patterns, parametrize
integration-patterns.mdApp-side architecture, request flow, polling, error handling, storage wiring

Example Prompts

FileCovers
examples-prompts/plugin-blueprint.mdReusable skeleton for new downloader and converter plugins
examples-prompts/new-downloader-plugin.mdWire a new downloader plugin end-to-end
examples-prompts/writing-tests.mdUnit test patterns for plugins with subprocess and async mocks

Plugin Architecture Summary

src/
├── api/
│   ├── main.py          ← FastAPI app, endpoints, job dispatch
│   └── models.py        ← Pydantic models, ModelHub enum, ModelType, Config
├── core/
│   ├── interfaces.py    ← ModelDownloadPlugin ABC (plugin_name, plugin_type, can_handle, download)
│   ├── model_manager.py ← Job lifecycle, ThreadPoolExecutor, status tracking
│   ├── plugin_registry.py ← Auto-discovery, activation check, find_plugin_for_model
│   └── plugin_venv.py   ← Per-plugin venv management
└── plugins/
    ├── __init__.py      ← PLUGINS tuple mapping — register module path + class name here
    ├── huggingface_plugin.py
    ├── ollama_plugin.py
    ├── openvino_plugin.py
    ├── ultralytics_plugin.py
    ├── geti_plugin.py
    ├── hls_plugin.py
    └── pipeline_zoo_models_plugin.py

Procedure: Adding a New Plugin

Read plugin-architecture.md first, then use the example prompts in this order:

  1. examples-prompts/plugin-blueprint.md for the reusable class skeleton
  2. examples-prompts/new-downloader-plugin.md for the end-to-end wiring
  3. examples-prompts/writing-tests.md for the unit-test shape

The minimum set of surfaces that must stay aligned is:

  1. plugin_name in the class
  2. the key in src/plugins/__init__.py
  3. the ModelHub enum value in src/api/models.py
  4. the optional dependency extra in pyproject.toml
  5. activation support in docker/entrypoint.sh

Use the current tuple-based plugin registration format:

python
PLUGINS = {
    # ... existing entries ...
    "myhub": ("src.plugins.myhub_plugin", "MyHubPlugin"),
}

[!IMPORTANT]

  • ENABLED_PLUGINS controls which modules are imported by src/plugins/__init__.py
  • ACTIVATED_PLUGINS in /opt/activated_plugins.env is what PluginRegistry checks later

If the plugin is implemented but does not appear in /api/v1/plugins, assume one of those registration or activation surfaces is out of sync before you assume the core plugin logic is wrong.


Show full SKILL.md (275 more words)Show less

Procedure: Integrating into an Application or Platform

Read integration-patterns.md first when the user is embedding model-download into another service or deployment stack.

Start by identifying the integration role:

  • Provisioning service: pre-download models during deployment or CI/CD
  • Runtime dependency: app calls model-download on demand when a model is missing
  • Ops/admin service: internal tooling triggers downloads and exposes status to operators

Prefer the public REST API as the integration boundary:

  1. Check readiness with GET /api/v1/health
  2. Submit work with POST /api/v1/models/download?download_path=<subdir>
  3. Store the returned job_id
  4. Poll GET /api/v1/jobs/{job_id} until completed or failed
  5. Use the reported download_path or conversion_path

Before proposing code or deployment changes, capture these decisions:

ConcernDecide
Trigger pointdeploy time, app startup, first request, or admin action
Model sourcehuggingface, ollama, ultralytics, openvino, geti, pipeline-zoo-models, hls
Needed pluginsminimal --plugins list
Persistencewhere MODEL_PATH lives and which services mount it
Completion modelsynchronous wait in caller, async background job, or external orchestrator
Failure behaviorretry, fail startup, partial availability, or operator intervention

Expected integration outputs include one or more of:

  • an application architecture recommendation
  • Docker Compose or Helm changes
  • app-side client code for submit + poll + result handling
  • env var, plugin, and storage/mount checklists
  • a failure-handling and retry strategy

Ground recommendations in the current API, deployment scripts, and plugin activation flow.


Procedure: Debugging a Stuck Job

Read plugin-architecture.md → "Job Lifecycle" section.

Quick diagnosis checklist:

bash
# 1. Check service logs for exceptions
docker logs model-download 2>&1 | tail -100

# 2. Inspect the job status
curl -s http://localhost:8200/api/v1/jobs/<job-id>

# 3. Verify the plugin was activated and discovered
curl -s http://localhost:8200/api/v1/plugins

# 4. Test the plugin in isolation
python3 -c "
import asyncio
from src.plugins.myhub_plugin import MyHubPlugin
p = MyHubPlugin()
result = asyncio.run(p.download('my-model', '/tmp/test'))
print(result)
"

Common causes of stuck jobs:

  • Plugin raised an exception that was swallowed — check logs
  • Plugin is blocking the event loop (use asyncio.to_thread for sync I/O)
  • Lock held by a crashed previous job (Ollama _ollama_download_lock) — restart container
  • Plugin was implemented but not activated — verify docker/entrypoint.sh, ENABLED_PLUGINS, and ACTIVATED_PLUGINS

© open-edge-platform, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in microservices/model-download/.github/skills/model-download-dev of open-edge-platform/edge-ai-libraries.

  • SKILL.md
  • evals/evals.json
  • examples-prompts/new-downloader-plugin.md
  • examples-prompts/plugin-blueprint.md
  • examples-prompts/writing-tests.md
  • references/integration-patterns.md
  • references/plugin-architecture.md
  • references/testing-patterns.md

Open the folder on GitHubat commit 3084578

Compare with similar skills

Model Download Dev next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Questions about Model Download Dev

What does Model Download Dev do?

Extend, test, debug, or integrate the Model Download microservice codebase. Model Download Dev is an agent skill from open-edge-platform/edge-ai-libraries. Extend, test, debug, or integrate the Model Download microservice codebase.

When should I use Model Download Dev?

Model Download Dev fits situations like: A developer wants to: add a new plugin to the microservice; write tests for a plugin (including mocking subprocess calls; the Ollama server); debug a job stuck in downloading.

How do I install Model Download Dev in Claude Code?

Run `npx skills add open-edge-platform/edge-ai-libraries --skill model-download-dev -a claude-code`. Or copy the skill folder (microservices/model-download/.github/skills/model-download-dev in open-edge-platform/edge-ai-libraries) into .claude/skills/model-download-dev in your project. Claude Code loads it when a task matches its description.

How do I install Model Download Dev in Codex?

Run `npx skills add open-edge-platform/edge-ai-libraries --skill model-download-dev -a codex`. Or copy the skill folder (microservices/model-download/.github/skills/model-download-dev in open-edge-platform/edge-ai-libraries) into .agents/skills/model-download-dev in your project. Codex loads it when a task matches its description.

Can I use Model Download Dev in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add open-edge-platform/edge-ai-libraries --skill model-download-dev -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-download-dev, .gemini/skills/model-download-dev, .github/skills/model-download-dev and .opencode/skills/model-download-dev in your project.

What does Model Download Dev need to run?

Going by SKILL.md and its folder, Model Download Dev needs the command-line tools its instructions call (curl, docker and python3). Our summary lists: Python 3; Docker.

Does Model Download Dev access the network?

SKILL.md contains no URLs. Its commands use curl and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Model Download Dev safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Model Download Dev use?

Model Download Dev is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Download Dev use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.3k tokens, read only when the agent opens those files.

What are the alternatives to Model Download Dev?

Skills that share tags, products or a category with Model Download Dev: Frontmcp Deployment (agentfront/frontmcp, 146 stars), Data Processing (aiskillstore/marketplace, 430 stars), Gem Devops Guidelines (github/awesome-copilot, 40k stars) and Create Pipeline (harness/harness-skills, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Download Dev?

open-edge-platform (a GitHub organization) maintains it in open-edge-platform/edge-ai-libraries, which has 169 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 8, 2026.

Source: open-edge-platform/edge-ai-libraries on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.