Topics
ZimoLiao/scholaraio
A skill your agent uses when the user asks about research themes, topic distribution, BERTopic clustering, topic overview, topic papers, topic merges, or HTML topic visualizations.
A skill your agent uses when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing…
MITAuto-check passed
Install Mlsys Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .claude/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .claude/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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/MLSys-Skills/skills/mlsys-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 mlsys-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .agents/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .agents/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .cursor/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .cursor/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 MLSys-Skills/skills/mlsys-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 mlsys-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .gemini/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .gemini/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-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 mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .github/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .github/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-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 mlsys-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/MLSys-Skills/skills/mlsys-topic-selection .opencode/skills/mlsys-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 "mlsys-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-topic-selection into .opencode/skills/mlsys-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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
mlsys-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 807 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 belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing…
- Works in 2 steps: Does an ML property (model structure,… → Does a systems property (memory…
- Deciding whether a project belongs at MLSys rather than OSDI
- SKILL.md covers The co-design test, Routing table, Signals your project is… and Track choice within MLSys…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mlsys Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing between the research and industrial tracks, and sharpening the systems-for-ML or ML-for-systems framing before writing starts.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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 belongs at MLSys rather than OSDI
- The ML conferences
- Testing for genuine ML-systems co-design
- Choosing between the research and industrial tracks
Example prompts
- “/mlsys-topic-selection”
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Does an ML property (model structure, training dynamics, tolerance to approximation,
- Does a systems property (memory hierarchy, interconnect, scheduling, cost) shape the
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
Mlsys Topic Selection loads about 1.8k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 807 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). 807 words, ~1,809 tokens.
Download SKILL.mdSave it as
.claude/skills/mlsys-topic-selection/SKILL.md (or your agent's skills folder).- name
- mlsys-topic-selection
- description
- Use when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing between the research and industrial tracks, and sharpening the systems-for-ML or ML-for-systems framing before writing starts.
MLSys Topic Selection
Use this before writing begins. MLSys exists for work where machine learning and computer systems constrain each other — the CFP spans systems for ML (training, inference, serving, compilers, runtimes, specialized hardware, hardware-efficient methods, benchmarks and tooling) and ML for systems (learned scheduling, LLM-driven hardware design and system optimization), plus adjacent lanes like federated learning, compound AI and agent systems, privacy/security for ML, and data preparation (2026 topics list, verified 2026-07-08). Fit failures here are usually routing failures: strong work aimed at the wrong reviewer pool.
The co-design test
Ask two questions; MLSys wants both answers to be yes.
- Does an ML property (model structure, training dynamics, tolerance to approximation, request patterns of ML workloads) shape the system design?
- Does a systems property (memory hierarchy, interconnect, scheduling, cost) shape the ML method or its evaluation?
One yes is a re-routing signal: pure systems novelty exercised on an ML workload belongs at a general systems venue; pure ML novelty measured for speed belongs at an ML venue.
Routing table
| Project's center of gravity | Better venue | Why |
|---|---|---|
| New OS/storage/scheduling abstraction, ML is one workload among several | OSDI / SOSP / ATC / EuroSys | Their reviewers reward general-purpose systems depth |
| Network protocol or datacenter fabric for training traffic | NSDI / SIGCOMM | Networking-first reviewer pool |
| Microarchitecture or accelerator design | ASPLOS / ISCA / MICRO | Hardware evaluation norms (simulators, RTL) differ |
| New learning algorithm; efficiency is one experiment | NeurIPS / ICML / ICLR | Algorithmic novelty is the contribution |
| HPC-scale training on supercomputers | SC / HPDC | HPC evaluation culture |
| Mechanism where ML and system constraints genuinely interlock | MLSys | This is the house genre |
| Production ML system's design + deep benchmarks, limited novelty | MLSys industrial track (2026+) | Novelty explicitly not required there |
| Early idea, workshop-scale evidence | An MLSys-adjacent workshop first | Expanded workshop papers can return with chair approval |
Signals your project is MLSys-shaped
- The abstract's key number is a systems metric (goodput, p99, memory, $/token) achieved because of an ML-aware design decision — not incidentally.
- The natural reviewer nightmare is "is this workload representative?" rather than "is this theorem tight?" or "is this abstraction general?"
- The related work splits across systems venues and ML venues — no single community owns
the problem (see
mlsys-related-work). - You can promise an artifact whose evaluation is meaningful on hardware a committee can access — badge culture is part of this venue's identity.
- The contribution survives this compression: "We observed [ML property]; therefore we built [mechanism]; which yields [measured system payoff] at [stated cost]."
Track choice within MLSys (2026 structure)
- Research track: previously unpublished ideas, full double-blind, novelty judged against both literatures.
- Industrial track: built-at-scale systems, design methodology and detailed benchmarks required, novelty not required, company identity may remain visible. Choosing it for a paper whose real asset is a clever new mechanism undersells the mechanism; choosing research track for a deployment report gets it rejected for novelty. Route by the paper's strongest honest asset.
Show full SKILL.md (333 more words)Show less
Sharpening moves before committing
Fill these four lines; blanks are routing information.
ML property exploited: ______________________
System constraint engaged: ______________________
Named mechanism: ______________________
Headline systems metric: ______ on workload ______ vs baseline ______
If "named mechanism" is blank -> industrial track or a workshop.
If "ML property" is blank -> general systems venue.
If "system constraint" is blank -> ML venue.
If the metric line is blank -> not ready for any venue; measure first.- Check the current cycle's topics list before finalizing — the venue's emphasis has drifted fast (the 2026 list added compound AI/agent systems and LLM-driven hardware design; assume continued drift).
- Consider the calendar honestly: MLSys is annual (October deadline in the 2026 cycle). If the project peaks in March, a systems venue with a nearer deadline may serve the work better than a seven-month hold.
Vignette: three borderline projects, routed
- A learned cost model inside a query optimizer, evaluated on analytics workloads. ML-for-systems is in scope, but the reviewer pool that can judge query optimization depth sits at database venues. Route to MLSys only if the contribution is the learning-systems machinery (training-data collection, drift handling, inference latency budget); route to a DB venue if it is optimizer quality.
- An INT4 quantization method with strong accuracy and a fast custom kernel. If the
paper's spine is the accuracy-preservation insight, ML venues fit; it becomes
MLSys-shaped when the kernel/runtime co-design carries equal weight and the
evaluation leads with latency-quality tradeoffs on real serving stacks — the AWQ
pattern in
../../resources/exemplars/library.md. - A cluster scheduler for mixed training/inference jobs, evaluated in simulation only. Topic fits; evidence does not — simulation-only evaluation of a systems claim is a recognized weak pattern here. Either add a real-cluster deployment at modest scale or target a venue where simulation is the norm.
Anti-patterns that fail the fit test
- "We ported X to GPUs and it is faster" — engineering without an insight that travels.
- "Our new attention variant, with a latency table" — ML paper wearing a systems hat.
- "A survey of serving systems" — MLSys publishes measured contributions, not surveys.
- "Our company's stack, described" without benchmarks — even the industrial track demands measurement depth.
- "Faster than a two-year-old baseline" — in a field whose systems improve monthly, a stale comparison fails the fit test before the novelty question is even reached.
- "We propose a benchmark" with no run rules or quality targets — the venue that published MLPerf holds benchmark papers to benchmark-committee standards.
Output format
[Fit] strong MLSys / MLSys-possible / route elsewhere
[Co-design test] ML->system: yes/no ; system->ML: yes/no
[Track] research / industrial / workshop-first
[Best alternative venue] <venue + reason>
[Contribution compression] observed -> built -> measured -> cost
[Next action] <measurement, mechanism naming, track decision, or reroute>© 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 MLSys-Skills/skills/mlsys-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Mlsys 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 |
|---|---|---|---|---|---|---|
| Mlsys Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| TopicsZimoLiao/scholaraio | 576 | — | ~294 | Automated safety check: Pass | MIT | |
| Topic Modelingbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~3.7k | Automated safety check: Pass | Custom licence | |
| Bestblogs Topicginobefun/BestBlogs | 4k | — | ~670 | Automated safety check: Pass | None | |
| Zsxq Topicitwanger/toBeBetterJavaer | 18k | — | ~564 | Automated safety check: Pass | None | |
| Pubmed Topic Recommendaipoch/medical-research-skills | 2k | — | ~1.9k | Automated safety check: Pass | MIT |
Similar skills
Topic Modeling
brycewang-stanford/Auto-Empirical-Research-Skills
Structural topic modeling: STM spec, topic count, coherence-exclusivity.
Bestblogs Topic
ginobefun/BestBlogs
A skill your agent uses when the user asks about a specific topic, subject area, or wants to explore curated topic pages on BestBlogs.
Zsxq Topic
itwanger/toBeBetterJavaer
知识星球主题管理：搜索主题、查看主题详情、发布帖子、编辑主题、发表评论、回复某条评论（楼中楼）、回答提问、删除主题；通过 api call 查看主题评论列表、设置精华、设置标签、查看自己提的问题与已回答记录。当用户需要查找内容、发帖、编辑主题、评论、回复评论、回答问题、删除主题、查看主题评论、查看自己的提问记录、或管理主题精华和标签时使用。
Pubmed Topic Recommend
aipoch/medical-research-skills
Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.
TiXL UI Topic Doc Writer
tixl3d/tixl
Fills in empty embedded help text for TiXL's UI topics by distilling the maintainer's own video explanations into short, user-facing doc entries.
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Questions about Mlsys Topic Selection
What does Mlsys Topic Selection do?
A skill your agent uses when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing…. Mlsys Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at MLSys rather than OSDI, SOSP, NSDI, ASPLOS, ATC, EuroSys, or the ML conferences, testing for genuine ML-systems co-design, choosing between the research and industrial tracks, and sharpening the systems-for-ML or ML-for-systems framing before writing starts.
When should I use Mlsys Topic Selection?
Mlsys Topic Selection fits situations like: deciding whether a project belongs at MLSys rather than OSDI; the ML conferences; testing for genuine ML-systems co-design; choosing between the research and industrial tracks.
How do I install Mlsys Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-topic-selection -a claude-code`. Or copy the skill folder (MLSys-Skills/skills/mlsys-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mlsys-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Mlsys Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-topic-selection -a codex`. Or copy the skill folder (MLSys-Skills/skills/mlsys-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mlsys-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Mlsys 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 mlsys-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/mlsys-topic-selection, .gemini/skills/mlsys-topic-selection, .github/skills/mlsys-topic-selection and .opencode/skills/mlsys-topic-selection in your project.
What does Mlsys Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlsys Topic Selection is instructions for the agent only.
Does Mlsys 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 Mlsys 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 Mlsys Topic Selection use?
Mlsys 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 Mlsys Topic Selection use?
About 1.8k tokens (SKILL.md is roughly 7.2k 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 Mlsys Topic Selection?
Skills that share tags, products or a category with Mlsys Topic Selection: Topics (ZimoLiao/scholaraio, 576 stars), Topic Modeling (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Bestblogs Topic (ginobefun/BestBlogs, 4k stars) and Zsxq Topic (itwanger/toBeBetterJavaer, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mlsys 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.