Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Design/train/audit imaging DL pipelines, splits, tuning and validation; not deployment studies.
Install Radiology Deep Learning
$ npx skills add huang-sir1/radiology-skills --skill radiology-deep-learning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install huang-sir1/radiology-skills radiology-deep-learning --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/huang-sir1/radiology-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .claude/skills/radiology-deep-learning && 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 "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .claude/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learningType 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 huang-sir1/radiology-skills --skill radiology-deep-learning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install huang-sir1/radiology-skills radiology-deep-learning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .agents/skills/radiology-deep-learning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .agents/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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 huang-sir1/radiology-skills --skill radiology-deep-learning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install huang-sir1/radiology-skills radiology-deep-learning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .cursor/skills/radiology-deep-learning && 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 "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .cursor/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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/huang-sir1/radiology-skills.git --path radiology-skills/skills/radiology-deep-learning--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 huang-sir1/radiology-skills --skill radiology-deep-learning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install huang-sir1/radiology-skills radiology-deep-learning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .gemini/skills/radiology-deep-learning && 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 "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .gemini/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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 huang-sir1/radiology-skills radiology-deep-learningInstalls 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 huang-sir1/radiology-skills --skill radiology-deep-learning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .github/skills/radiology-deep-learning && 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 "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .github/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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 huang-sir1/radiology-skills --skill radiology-deep-learning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install huang-sir1/radiology-skills radiology-deep-learning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/radiology-skills/skills/radiology-deep-learning .opencode/skills/radiology-deep-learning && 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 "radiology-deep-learning" agent skill from https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-deep-learning into .opencode/skills/radiology-deep-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "radiology-deep-learning", 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
radiology-deep-learning- GitHub stars
- 1.9k
- Token cost
- ~2.5k tokens
- SKILL.md length
- 968 words
- Files
- 12 (incl. references)
- Skills in repo
- 39
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Design/train/audit imaging DL pipelines, splits, tuning and validation; not deployment studies.
- Works in 9 steps: Confirm the design (reuse… → Choose architecture… → For foundation/trustworthy-AI designs,… → …
- Tasks that involve Deep learning
- SKILL.md covers Core stance, When to use, When to open extra files and Workflow, plus 3 more sections
- Runs Python scripts from its folder
What it does
Radiology Deep Learning is an agent skill from huang-sir1/radiology-skills. Design/train/audit imaging DL pipelines, splits, tuning and validation; not deployment studies.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including reference files (for example `README.md`, `agents/openai.yaml` and `references/architecture-choice.md`).
It sits in AI & LLM Engineering, covering Deep learning.
When your agent uses it
- Tasks that involve Deep learning
Example prompts
- “/radiology-deep-learning”
Requirements
- Python 3
Workflow steps
9 steps, taken from the first numbered list in SKILL.md.
- Confirm the design (reuse radiology-design) — task, endpoint, unit (patient-level),
- Choose architecture (architecture-choice.md) — dimension, family, task head; justify
- For foundation/trustworthy-AI designs, open foundation-models-trustworthy-ai.md and, when
- Define inputs (multimodal-inputs.md) — channels/crops/fusion; missing-modality rule;
- Set training (training-protocol.md) — transfer/SSL/scratch, patient-level splits,
- Validate — use patient-level internal evaluation and add temporal/geographic/external
- Run justified trustworthiness analyses (interpretability-uncertainty.md) — name the failure
- Freeze experiment provenance (experiment-and-model-card.md) — data/split manifest,
- Audit leakage (dl-leakage-audit.md) and write Methods to CLAIM 2024.
What it can do on your machine
Read from SKILL.md and the folder at commit aaa0fe6. 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
Ships script files (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Radiology Deep Learning loads about 2.5k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 968 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 968 words (~2,491 tokens).
“Use this skill to design (or audit) an imaging deep-learning study so it is reproducible, honestly validated, and CLAIM-compliant. DL imaging papers get torn apart for slice-level splits, patient overlap, test-set tuning, no external validation, and baselines that are too…”
- name
- radiology-deep-learning
Files
SKILL.md and 11 other files (references) in radiology-skills/skills/radiology-deep-learning of huang-sir1/radiology-skills.
- SKILL.md
- README.md
- agents/openai.yaml
- references/architecture-choice.md
- references/dl-leakage-audit.md
- references/experiment-and-model-card.md
- references/foundation-model-data-genealogy-and-privacy.md
- references/foundation-models-trustworthy-ai.md
- references/interpretability-uncertainty.md
- references/multimodal-inputs.md
- references/training-protocol.md
- tests/test_foundation_boundary_contracts.py
Open the folder on GitHubat commit aaa0fe6
Compare with similar skills
Radiology Deep Learning 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 |
|---|---|---|---|---|---|---|
| Radiology Deep Learning this skillhuang-sir1/radiology-skills | 1.9k | — | ~2.5k | Automated safety check: Pass | Custom licence | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Add Oponnx/onnx | 22k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Function Bodyonnx/onnx | 22k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Segment Anything Model Guide
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
Add Op
onnx/onnx
Add a new ONNX operator or update an existing operator to a new opset version.
CLIP Image-Text Matching
Orchestra-Research/AI-Research-SKILLs
Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
Add Function Body
onnx/onnx
Add a function body definition to an ONNX operator, defining how it decomposes into simpler ops.
Paddle Design Distributed
PaddlePaddle/Paddle
A skill your agent uses when working with Paddle's distributed training system: understanding parallelism strategies (DP, ZeRO, TP, PP, SP), semi-automatic parallel with ProcessMesh + shardtensor…
More from huang-sir1/radiology-skills
All 39 skills in this repoRadiology Table
huang-sir1/radiology-skills
Create/audit editable publication tables with source reconciliation; not figures or statistical inference.
Radiology Acquisition Qc
huang-sir1/radiology-skills
Audit imaging acquisition, reconstruction, series eligibility, quantitative transforms and protocol drift; not model training.
Radiology Bibliometrics
huang-sir1/radiology-skills
Build reproducible radiology bibliometrics with corpus identity, disambiguation and sensitivity; not reviews.
Radiology Clinical Domain
huang-sir1/radiology-skills
Set disease pathways, endpoints, reference standards and treatment timelines; not patient care.
Radiology Consensus Guideline
huang-sir1/radiology-skills
Design/audit consensus and evidence-to-recommendation products; not review synthesis or endorsement.
Radiology Dissemination
huang-sir1/radiology-skills
Create traceable posters, conference abstracts, summaries and media; not papers, decks or figures.
Related
Categories
Questions about Radiology Deep Learning
What does Radiology Deep Learning do?
Design/train/audit imaging DL pipelines, splits, tuning and validation; not deployment studies. Radiology Deep Learning is an agent skill from huang-sir1/radiology-skills. Design/train/audit imaging DL pipelines, splits, tuning and validation; not deployment studies.
When should I use Radiology Deep Learning?
Radiology Deep Learning fits situations like: tasks that involve Deep learning.
How do I install Radiology Deep Learning in Claude Code?
Run `npx skills add huang-sir1/radiology-skills --skill radiology-deep-learning -a claude-code`. Or copy the skill folder (radiology-skills/skills/radiology-deep-learning in huang-sir1/radiology-skills) into .claude/skills/radiology-deep-learning in your project. Claude Code loads it when a task matches its description.
How do I install Radiology Deep Learning in Codex?
Run `npx skills add huang-sir1/radiology-skills --skill radiology-deep-learning -a codex`. Or copy the skill folder (radiology-skills/skills/radiology-deep-learning in huang-sir1/radiology-skills) into .agents/skills/radiology-deep-learning in your project. Codex loads it when a task matches its description.
Can I use Radiology Deep Learning 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 huang-sir1/radiology-skills --skill radiology-deep-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/radiology-deep-learning, .gemini/skills/radiology-deep-learning, .github/skills/radiology-deep-learning and .opencode/skills/radiology-deep-learning in your project.
What does Radiology Deep Learning need to run?
Going by SKILL.md and its folder, Radiology Deep Learning needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Radiology Deep Learning access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Radiology Deep Learning 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 Radiology Deep Learning use?
Radiology Deep Learning has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Radiology Deep Learning use?
About 2.5k tokens (SKILL.md is roughly 10k 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 9k tokens, read only when the agent opens those files.
What are the alternatives to Radiology Deep Learning?
Skills that share tags, products or a category with Radiology Deep Learning: Add Uint Support (pytorch/pytorch, 104k stars), Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Op (onnx/onnx, 22k stars) and CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Radiology Deep Learning?
huang-sir1 (a GitHub user) maintains it in huang-sir1/radiology-skills, which has 1,923 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on September 21, 2026.
Source: huang-sir1/radiology-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.