claude

mlsys-skills

Twelve MLSys-specific skills for machine-learning-and-systems conference submission strategy, grounded in the official MLSys 2026 Call for Research Papers, Industrial Track CFP, Call for Artifact Evaluations, and dates/organization pages.

1,413 on GitHubby Bryce Wangv1.1.0MITupdated 1 month agoacademic
/

Plugin mlsys-skills from marketplace awesome-journal-skills in brycewang-stanford/Awesome-Journal-Skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (12)

  • mlsys-artifact-evaluation

    Use when packaging an accepted MLSys paper's code, configs, and measurement scripts for the venue's post-acceptance artifact evaluation, targeting the Availability, Functional, and Reproducible badges, writing the Artifact Appendix, handling hardware that AE reviewers cannot access, and answering an

  • mlsys-author-response

    Use when drafting MLSys author responses on OpenReview under the venue's compressed rebuttal window, prioritizing systems-reviewer objections about workload representativeness, baseline tuning, and missing measurements, deciding whether to run new experiments in days, and keeping replies anonymous a

  • mlsys-camera-ready

    Use when preparing an accepted MLSys paper for publication on proceedings.mlsys.org, covering de-anonymization of the research-track PDF, restoring company and repository identity, reconciling promised rebuttal edits, the reserved-ticket registration window for authors, and sequencing camera-ready w

  • mlsys-experiments

    Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute gains to mechanisms, and building scaling an

  • mlsys-related-work

    Use when positioning an MLSys submission against the fast-moving ML-systems literature scattered across OSDI, SOSP, NSDI, ASPLOS, and ML venues, handling arXiv-first and open-source-first prior work, comparing against production systems that have no paper, and writing the delta statement two reviewe

  • mlsys-reproducibility

    Use when hardening the reproducibility of MLSys performance claims, pinning the full system layer from driver to interconnect, separating ML randomness from systems noise, choosing repetition counts and variance reporting for throughput and latency numbers, and disclosing hardware, workloads, and co

  • mlsys-review-process

    Use when reasoning about how MLSys peer review works, covering the OpenReview workflow, the mixed ML-and-systems reviewer pool and how each half scores differently, the compressed response window, industrial-track review expectations, decision dynamics, and what the post-acceptance artifact stage me

  • mlsys-submission

    Use when auditing an MLSys submission for OpenReview readiness, covering the two-column 10-page body excluding references, the separate appendix upload, double-blind rules that still permit arXiv posting, research-versus-industrial track requirements, dual-submission exceptions, style-kit compliance

  • mlsys-supplementary

    Use when deciding what goes into an MLSys appendix versus the 10-page body, exploiting the venue's unlimited separately-uploaded appendix that reviewers are not required to read, organizing configs, traces, extended results, and anonymized code pointers, and keeping supplementary material blinded fo

  • mlsys-topic-selection

    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 st

  • mlsys-workflow

    Use when planning an MLSys submission cycle end to end, from venue-fit and track choice through evaluation freeze, the October deadline, the four-day January response, notification, camera-ready, the March artifact-evaluation submission, and the May conference, with backward planning built around sc

  • mlsys-writing-style

    Use when revising an MLSys paper's prose and structure, building the measured-bottleneck opening, naming the mechanism instead of listing optimizations, writing evaluation sections as answers to research questions, quantifying every performance claim with workload context, and fitting the argument i

View source on GitHub copaper.ai

More from brycewang-stanford/Awesome-Journal-Skills

All 300 plugins →

AI-first computer-science conference skill stack: 155 conference fit-and-submission profiles plus a CS/AI router. Covers top AI/ML, data mining, vision, NLP, robotics, HCI, systems, security, software engineering, programming languages, databases, and theory venues, with current-cycle CFP and author-kit re-check discipline.

156 skills

1,413

English natural-science / clinical / physical / formal-science journal routing and per-journal fit skills with official-submission-check discipline.

155 skills

1,413

Chinese social-science journal routing and per-journal fit skills with official-submission-check discipline.

103 skills

1,413

English economics / business journal routing and per-journal fit skills with official-submission-check discipline.

101 skills

1,413

English humanities journal routing and per-journal fit skills with argument-, source-, and style-guide discipline.

37 skills

1,413

Similar plugins

5 skills + 35 modes + Material Passport pipeline. Includes v3.6.7 cross-model audit gate and v3.6.8 generator-evaluator contract.

5 skills · 16 commands · 4 agents · hooks

Imbad0202/academic-research-skills 56,698

exa

claude

Exa AI web search, deep research, and content extraction. Provides MCP tools and research skills for comprehensive web search, people discovery, company research, academic papers, and more.

2 skills · 1 MCP server

anthropics/claude-plugins-official 47,076

Exa

claude

Exa AI web search, deep research, and content extraction. Provides MCP tools and research skills for comprehensive web search, people discovery, company research, academic papers, and more.

2 skills · 1 MCP server

anthropics/knowledge-work-plugins 35,601

Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.

6 skills · 7 commands · 2 agents

alirezarezvani/claude-skills 29,725

Contribution-first, reviewer-aware academic paper and report writing system.

2 skills

WUBING2023/PaperSpine 6,289

Semi-automated research assistant with skills for literature review, experiments, analysis, writing, and project knowledge management

45 skills · 66 commands · 6 agents · hooks

Galaxy-Dawn/claude-scholar 6,042

Maintain mlsys-skills?

This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.

claude & codex plugins300 plugins