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Found 183 Skills
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Prepare a research artifact package for conference artifact evaluation, reproducibility review, badges, supplementary material, or post-acceptance artifact release. Use this skill whenever the user needs install instructions, reviewer-facing reproduction commands, Docker or environment checks, data/checkpoint packaging, hardware/runtime estimates, anonymized or public artifact metadata, artifact evaluation forms, or a claim-to-artifact reproducibility audit for ML/AI venues.
Turn a promising ML/AI research idea into a precise algorithm or method design before implementation. Use this skill whenever the user has an idea or project direction and wants to design the actual method, objective, architecture, inference procedure, assumptions, failure modes, ablations, implementation handoff, or method section plan before coding or experiment design.
Simulate target-conference reviewers for an ML/AI paper before submission. Use this skill whenever the user wants a reviewer-style critique, predicted scores, likely reject reasons, rebuttal risks, area-chair style meta-review, adversarial Reviewer 2 feedback, or venue-specific pre-review for conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, or similar venues. This skill should dynamically inspect reviewer guidelines, example reviews, accepted papers, and project evidence when available.
Run a pre-submission citation and reference audit for LaTeX academic papers. Use this skill whenever the user wants to verify that BibTeX entries are correct, every citation key in TeX resolves, every figure/table/equation/section reference is valid, DOI/arXiv/OpenReview/proceedings metadata matches the cited work, citation claims are supported by the cited paper, or a paper is ready for submission with clean references.
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Use when testing, reviewing, pressure-testing, refining, packaging, or validating agent skills for academic research workflows before installing or relying on them.
Use when academic research involves human subjects, public web data, platform scraping, sensitive domains, privacy risk, dataset sharing, consent, IRB, licenses, or data retention.
Use when preparing academic artifacts, reproducibility packages, artifact evaluation submissions, open science materials, code/data release, model cards, dataset cards, or replication bundles.
Verify in-text citations, references, author identities, year disambiguation, DOIs, and specified formatting rules; does not assess whether sources support claims. Use when the user asks for "check citation format", "verify in-text citations and references", "check authors with the same surname", "conduct a citation audit", or requests the rw-citation-audit workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Extract academic tone rules from the corpus provided by the user in this round or the current manuscript, and make minimal adjustments. Use when the user asks for "Extract my PhD tone", "Check if the paper sounds like me", "Preserve author fingerprint", or requests the rw-phd-tone workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Use this skill to access Reddit's full data archive via reddapi.dev API. Features semantic search, subreddit discovery, and real-time trend analysis. Perfect for market research, competitive analysis, and niche opportunity discovery.