Loading...
Loading...
Found 183 Skills
Audit whether an ML or AI paper's experimental baselines are necessary, fair, current, and reviewer-proof. Use this skill whenever the user is planning experiments, comparing methods, choosing baselines, worried about missing SOTA or unfair comparisons, preparing a reviewer-proof experiment section, or converting a literature review into must-have, should-have, optional, and not-comparable baselines.
Diagnose surprising, negative, unstable, or ambiguous ML/AI experiment results and decide whether to debug implementation, rerun experiments, change metrics or baselines, revise the algorithm, narrow the paper claim, park, or kill a direction. Use this skill whenever results do not match expectations, a method fails, metrics conflict, seeds vary, baselines beat the method, plots look suspicious, or the user asks what to do next after experimental results.
Plan, draft, and revise ML/AI limitations, scope, failure cases, ethics, broader impact, and conclusion caveats so they control claim boundaries without undermining the paper. Use when the user wants limitation wording, scope statements, failure-case interpretation, ethics/broader-impact text, or overclaim reduction.
Read research outline, launch independent agent for each item for deep research. Disable task output.
Use when normalizing BibTeX, RIS, CSL JSON, citation keys, DOI/arXiv/PMID metadata, references, unused citations, missing citations, or bibliography quality for papers and SOTA work.
Use when selecting, installing, configuring, smoke-testing, documenting, or troubleshooting MCP servers for academic search, arXiv, Semantic Scholar, OpenAlex, Crossref, PubMed, Zotero, Overleaf, Google Scholar, paper metadata, or scholarly source tooling.
Use when an academic research repository task could involve research design, sources, conversion, bibliography, SOTA, reviews, ethics, experiments, papers, reproduction, MCP tools, or project maintenance and the correct workflow is not obvious.
Use when choosing, comparing, or preparing for computer science venues, conferences, workshops, journals, tracks, deadlines, reviewer expectations, paper fit, or publication positioning.
Use when creating, repairing, refactoring, validating, or documenting an academic research repository structure, including wiki, sources, SOTA, outputs, agent docs, tests, and reproducibility folders.
Multi-source search and deduplication layer with intent-aware scoring. Integrates Brave Search (web_search), Exa, Tavily, and Grok to provide high-coverage, high-quality results. Automatically classifies query intent and adjusts search strategy, scoring weights, and result synthesis accordingly. Activated for "deep search", "multi-source search", or when high-quality research is needed.
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 and publish a research code repository for public release alongside a paper (arXiv, conference, GitHub). Use when the user wants to open-source code, create a GitHub release, package a code submission, make code public, or prepare a reproducibility release.