Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
A skill your agent uses when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue.
Install Iclr Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-topic-selection --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/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .claude/skills/iclr-topic-selection && 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 "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .claude/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selectionType 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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-topic-selection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .agents/skills/iclr-topic-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .agents/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-topic-selection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .cursor/skills/iclr-topic-selection && 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 "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .cursor/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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/brycewang-stanford/Awesome-Journal-Skills.git --path ICLR-Skills/skills/iclr-topic-selection--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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-topic-selection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .gemini/skills/iclr-topic-selection && 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 "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .gemini/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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 brycewang-stanford/Awesome-Journal-Skills iclr-topic-selectionInstalls 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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .github/skills/iclr-topic-selection && 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 "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .github/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iclr-topic-selection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/ICLR-Skills/skills/iclr-topic-selection .opencode/skills/iclr-topic-selection && 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 "iclr-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICLR-Skills/skills/iclr-topic-selection into .opencode/skills/iclr-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iclr-topic-selection", 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
iclr-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~990 tokens
- SKILL.md length
- 406 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue.
- Deciding whether a project is a strong ICLR submission
- SKILL.md covers Strong ICLR signals, Weak ICLR signals, Routing logic and Fit-versus-route decision table, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Should be reframed for ICLR
What it does
Iclr Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Deep learning. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Deciding whether a project is a strong ICLR submission
- Should be reframed for ICLR
- Should be routed to NeurIPS
- A project lacks a clear representation-learning insight
Example prompts
- “/iclr-topic-selection”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. 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
No scripts in the folder and no shell commands in SKILL.md.
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
Iclr Topic Selection loads about 990 tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 406 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 brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 406 words, ~990 tokens.
Download SKILL.mdSave it as
.claude/skills/iclr-topic-selection/SKILL.md (or your agent's skills folder).- name
- iclr-topic-selection
- description
- Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
ICLR Topic Selection
Use this when a project is still movable. ICLR is broad, but the paper should teach the learning community something about representations, objectives, models, data, optimization, evaluation, or deployment.
Strong ICLR signals
- A clear representation-learning, model-behavior, optimization, generative modeling, RL, theory, or evaluation contribution.
- Evidence that changes how researchers should build, analyze, or judge learning systems.
- A simple central claim that can be verified by focused theory, experiments, or artifacts.
- Interest beyond one dataset, product, or application vertical.
- Honest limitations and ethics treatment for high-impact model or data claims.
Weak ICLR signals
- Pure application paper with little learning insight.
- Incremental benchmark bump without mechanism, analysis, or robust evidence.
- Closed system claim that reviewers cannot inspect or reproduce.
- Dataset-only paper without a learning-representation or evaluation advance.
- Theory result disconnected from modern learning practice and not routed to a theory-focused venue.
Routing logic
- Prefer NeurIPS or ICML for broader ML method/theory work with less ICLR-specific representation framing.
- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.
- Prefer ACL, CVPR, KDD, or robotics/HCI venues when the contribution is primarily domain-specific.
- Prefer workshops when the idea is timely but under-evidenced.
Fit-versus-route decision table
ICLR's center of gravity is deep representation learning: architectures, self-supervision, generative models, foundation models, RL with deep function approximation, optimization for deep nets, interpretability, and alignment. Score the project against that center before routing.
| Project shape | ICLR fit | Better route if not ICLR |
|---|---|---|
| New self-supervised objective with analysis | Strong | — |
| Theory explaining a deep-net phenomenon | Strong | AISTATS/UAI if purely statistical |
| LLM/foundation-model behavior study | Strong | ACL if narrowly language-specific |
| Benchmark bump, no mechanism | Weak | Domain venue or workshop |
| Causal/uncertainty emphasis | Plausible | AISTATS or UAI |
| Deployed application, little learning insight | Weak | KDD, CVPR, robotics/HCI venue |
Show full SKILL.md (120 more words)Show less
Worked vignette
A team has a method that improves recommendation click-through in production. As written it is an application paper. To make it ICLR-shaped, they extract the representation-learning claim: a new contrastive objective that yields embeddings transferring across catalogs, demonstrated with an ablation and a probe on a public dataset. The product result becomes one validation point, not the contribution. If that reframing fails to surface a learning insight, the honest route is KDD.
Reviewer-pushback patterns
- "No learning insight, just engineering." Reframe around the mechanism or route to a domain venue.
- "Dataset-only paper." Add an evaluation or representation advance, or target a datasets-and- benchmarks track instead.
- "Theory disconnected from practice." Tie the result to an observed deep-learning phenomenon.
Output format
[ICLR fit] strong / plausible / weak / no
[Core learning insight] <one sentence>
[Evidence required] <theory, experiment, benchmark, artifact>
[Best venue route] ICLR / NeurIPS / ICML / AISTATS / UAI / domain venue / workshop
[Reframe] <how to make the paper more ICLR-shaped>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in ICLR-Skills/skills/iclr-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iclr Topic Selection 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 |
|---|---|---|---|---|---|---|
| Iclr Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~990 | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 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 | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Function Bodyonnx/onnx | 22k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Iclr Topic Selection
What does Iclr Topic Selection do?
A skill your agent uses when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Iclr Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue.
When should I use Iclr Topic Selection?
Iclr Topic Selection fits situations like: deciding whether a project is a strong ICLR submission; should be reframed for ICLR; should be routed to NeurIPS; A project lacks a clear representation-learning insight.
How do I install Iclr Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a claude-code`. Or copy the skill folder (ICLR-Skills/skills/iclr-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iclr-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Iclr Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a codex`. Or copy the skill folder (ICLR-Skills/skills/iclr-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iclr-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Iclr Topic Selection 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 brycewang-stanford/Awesome-Journal-Skills --skill iclr-topic-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iclr-topic-selection, .gemini/skills/iclr-topic-selection, .github/skills/iclr-topic-selection and .opencode/skills/iclr-topic-selection in your project.
What does Iclr Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Iclr Topic Selection is instructions for the agent only.
Does Iclr Topic Selection 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 Iclr Topic Selection 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 Iclr Topic Selection use?
Iclr Topic Selection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Iclr Topic Selection use?
About 990 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Iclr Topic Selection?
Skills that share tags, products or a category with Iclr Topic Selection: 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 Iclr Topic Selection?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.