Deepstream Sop
NVIDIA/skills
A skill your agent uses when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether…
Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and…
Install Multimodal Dataprep Dev
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-dev --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .claude/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.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/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .claude/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-devType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-dev --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .agents/skills && cp -r skills-src/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .agents/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .agents/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-dev --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .cursor/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .cursor/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/open-edge-platform/edge-ai-libraries.git --path microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-dev --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .gemini/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .gemini/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-devInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .github/skills && cp -r skills-src/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .github/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .github/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-edge-platform/edge-ai-libraries multimodal-dataprep-dev --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/edge-ai-libraries.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev .opencode/skills/multimodal-dataprep-dev && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "multimodal-dataprep-dev" agent skill from https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev into .opencode/skills/multimodal-dataprep-dev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multimodal-dataprep-dev", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
multimodal-dataprep-dev- GitHub stars
- 168
- Token cost
- ~1.3k tokens
- SKILL.md length
- 462 words
- Files
- 6 (incl. references)
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and…
- Changing source
- SKILL.md covers Start with the relevant…, Build-context rule, Local development loop and Build and run, plus 3 more sections
- Calls poetry and docker; needs MINIO_ROOT_PASSWORD
- Adding a backend
What it does
Multimodal Dataprep Dev is an agent skill from open-edge-platform/edge-ai-libraries. Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and pluggable VDMS/Milvus vector stores plus MinIO/local storage. Use when changing source, adding a backend, running pytest/coverage/format checks, or building the service image. Use multimodal-dataprep-user for deployment and API-consumer workflows.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `evals/evals.json`, `example-prompts/onboard-embedding-model.md` and `example-prompts/update-test-cases.md`).
It sits in Backend & APIs, covering Microservices, Embeddings and Backend development. It works with Milvus, FastAPI, pytest 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
- Changing source
- Adding a backend
- Running pytest/coverage/format checks
- Building the service image
Example prompts
- “/multimodal-dataprep-dev”
Requirements
- Python 3
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 960d2e4. 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:
poetrydocker
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use 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 these keys or tokens, usually read from environment variables:
MINIO_ROOT_PASSWORD
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Multimodal Dataprep Dev loads about 1.3k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 462 words of instructions outside code blocks.
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 960d2e4, republished under its Apache-2.0 licence (© open-edge-platform). 462 words, ~1,335 tokens.
Download SKILL.mdSave it as
.claude/skills/multimodal-dataprep-dev/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- multimodal-dataprep-dev
- description
- Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and pluggable VDMS/Milvus vector stores plus MinIO/local storage. Use when changing source, adding a backend, running pytest/coverage/format checks, or building the service image. Use multimodal-dataprep-user for deployment and API-consumer workflows.
Multimodal DataPrep — Dev
Work from
microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/
inside an edge-ai-libraries checkout. If the request only deploys or consumes
the service, use
../multimodal-dataprep-user/SKILL.md.
Start with the relevant reference
| Reference | Read when |
|---|---|
references/source-map.md | Locating endpoints, pipeline code, backend abstractions, or configuration |
references/testing-and-build.md | Installing dependencies, testing, formatting, building, or debugging containers |
Example tasks:
| Example | Purpose |
|---|---|
| onboard-embedding-model.md | Exercise a different embedding model safely |
| update-test-cases.md | Add coverage for a source change |
Build-context rule
Use ./build.sh; do not run docker build ... . from this directory. The
Dockerfile copies both this service and the sibling
multimodal-embedding-serving source, so its context must be
microservices/. build.sh supplies that context.
Local development loop
poetry install --with dev
poetry run python -m pytest tests
poetry run coverage run --rcfile ./pyproject.toml -m pytest tests
poetry run coverage report -m
poetry run black --check src tests
poetry run isort --check-only src testsRun a focused test file while iterating:
poetry run python -m pytest tests/test_vectorstores.pyDo not conceal collection failures or attribute unrelated failures to the
current change. See
references/testing-and-build.md.
Build and run
setup.sh must be sourced. With no argument it exports defaults and creates the
YOLOX model volume; it does not start the stack.
export MINIO_ROOT_USER='<user>'
export MINIO_ROOT_PASSWORD='<strong-password>'
export EMBEDDING_MODEL_NAME='CLIP/clip-vit-b-32'
source ./setup.sh --nosetup
./build.sh
docker compose -f docker/compose.yaml up -d --buildOther supported setup actions are --conf, --down, --build [custom-tag],
and --nd (foreground docker compose ... up --build).
For Milvus, use docker/compose-milvus.yaml. For local media storage, layer
docker/compose.storage-local.yaml after the default compose file.
Current architecture
src/main.py creates the FastAPI app at /v1/dataprep, starts the optional
Metrics Manager publisher, preloads the embedding client and YOLOX detector,
and asks the active vector store to update its index during shutdown.
Requests flow through src/endpoints/ into
src/core/embedding/embedding_orchestrator.py. Video work is executed by the
threaded/shared-memory pipeline in embedding_helper.py; client.py wraps the
in-process model from the sibling embedding package and persists vectors
through src/core/vectorstores/. Media bytes and metadata go through
src/core/storage/.
Show full SKILL.md (211 more words)Show less
Change rules
- Preserve backend neutrality. Use
get_vector_store()andget_storage()rather than importing a concrete backend in endpoint or orchestration code. - Keep search/query behavior out of this service;
BaseVectorStorecovers ingestion-time add, delete, health, and index-update operations. - Add endpoint schemas in
src/common/schema.pyand include routers insrc/main.py. - Keep media routes under
/media; supported inputs include MP4 video and common image formats, plus text summaries at/summary. - Mock external storage, model, and vector-store calls in unit tests. The
Milvus integration test is opt-in through
MILVUS_IT_URI. - Add the repository SPDX header to every new source, config, test, or documentation file.
- Never commit credentials.
Important configuration facts
| Fact | Impact |
|---|---|
All application settings use Pydantic's MM_DATAPREP_ prefix | Set container variables such as MM_DATAPREP_VECTORDB_BACKEND, MM_DATAPREP_STORAGE_BACKEND, and MM_DATAPREP_EMBEDDING_MODEL_NAME |
setup.sh sets INDEX_NAME=video-rag; default compose maps it to MM_DATAPREP_DB_COLLECTION | For a one-off VDMS collection, set INDEX_NAME after sourcing and before Compose |
| Milvus collection names cannot contain hyphens | compose-milvus.yaml uses MILVUS_INDEX_NAME with default video_rag |
| Changing embedding dimensions is incompatible with an existing collection | Choose a fresh collection or obtain confirmation before deleting data |
| YOLOX weights are downloaded on first use | An offline first run can leave object detection unavailable while other ingestion continues |
| Metrics Manager publishing is optional | It is enabled only when MM_DATAPREP_METRICS_MANAGER_URL is non-empty and must not delay ingestion |
© 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 5 other files (references) in microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev of open-edge-platform/edge-ai-libraries.
- SKILL.md
- evals/evals.json
- example-prompts/onboard-embedding-model.md
- example-prompts/update-test-cases.md
- references/source-map.md
- references/testing-and-build.md
Open the folder on GitHubat commit 960d2e4
Compare with similar skills
Multimodal Dataprep 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Multimodal Dataprep Dev this skillopen-edge-platform/edge-ai-libraries | 168 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Deepstream SopNVIDIA/skills | 3.5k | — | ~4.7k | Automated safety check: Notes | Apache-2.0 | |
| Flowfile Debugging PlaybookEdwardvaneechoud/Flowfile | 370 | — | ~6.3k | Automated safety check: Pass | MIT | |
| Python API Designjohnku2011/boilerplates-with-ai-skills | 240 | — | ~449 | Automated safety check: Pass | MIT | |
| Fastapi Patternsaffaan-m/ECC | 274k | 1 repos | ~3.9k | Automated safety check: Notes | MIT | |
| Python Devdoccker/cc-use-exp | 1.1k | — | ~790 | Automated safety check: Pass | Custom licence |
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Questions about Multimodal Dataprep Dev
What does Multimodal Dataprep Dev do?
Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and…. Multimodal Dataprep Dev is an agent skill from open-edge-platform/edge-ai-libraries. Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and pluggable VDMS/Milvus vector stores plus MinIO/local storage.
When should I use Multimodal Dataprep Dev?
Multimodal Dataprep Dev fits situations like: changing source; adding a backend; running pytest/coverage/format checks; building the service image.
How do I install Multimodal Dataprep Dev in Claude Code?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a claude-code`. Or copy the skill folder (microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev in open-edge-platform/edge-ai-libraries) into .claude/skills/multimodal-dataprep-dev in your project. Claude Code loads it when a task matches its description.
How do I install Multimodal Dataprep Dev in Codex?
Run `npx skills add open-edge-platform/edge-ai-libraries --skill multimodal-dataprep-dev -a codex`. Or copy the skill folder (microservices/visual-data-preparation-for-retrieval/multimodal-dataprep/.github/skills/multimodal-dataprep-dev in open-edge-platform/edge-ai-libraries) into .agents/skills/multimodal-dataprep-dev in your project. Codex loads it when a task matches its description.
Can I use Multimodal Dataprep 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 multimodal-dataprep-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/multimodal-dataprep-dev, .gemini/skills/multimodal-dataprep-dev, .github/skills/multimodal-dataprep-dev and .opencode/skills/multimodal-dataprep-dev in your project.
What does Multimodal Dataprep Dev need to run?
Going by SKILL.md and its folder, Multimodal Dataprep Dev needs the command-line tools its instructions call (poetry and docker) and credentials named MINIO_ROOT_PASSWORD. Our summary lists: Python 3; Docker.
Does Multimodal Dataprep Dev access the network?
SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Multimodal Dataprep 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 Multimodal Dataprep Dev use?
Multimodal Dataprep 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 Multimodal Dataprep Dev use?
About 1.3k tokens (SKILL.md is roughly 5.3k 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 2.2k tokens, read only when the agent opens those files.
What are the alternatives to Multimodal Dataprep Dev?
Skills that share tags, products or a category with Multimodal Dataprep Dev: Deepstream Sop (NVIDIA/skills, 3.5k stars), Flowfile Debugging Playbook (Edwardvaneechoud/Flowfile, 370 stars), Python API Design (johnku2011/boilerplates-with-ai-skills, 240 stars) and Fastapi Patterns (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Multimodal Dataprep Dev?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/edge-ai-libraries, which has 168 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 7, 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.