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Found 163 Skills
Initialize Python Project (New or Fork). Use when the user wants to create a new production-ready Python/ML project structure, or fork and enhance an existing project. Uses uv for environment management.
Initialize LaTeX Academic Project with standard structure, macros, and writing guide. Use when user wants to create a new LaTeX paper project for any conference or journal.
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.
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.
Audit a skill repository or installed skill collection for global consistency, lifecycle coverage, routing quality, documentation drift, memory writeback coverage, stale future-skill references, broken helper paths, and validation readiness. Use this skill whenever the user asks for a global consistency audit, skill taxonomy review, lifecycle audit, cross-skill routing audit, README or AGENTS inventory consistency check, or maintenance pass over a collection of agent skills.
Control a remote SSH server project from a local git repo with persistent project memory. Use when the user develops locally but runs remotely, wants the agent to understand remote repo mappings across sessions, needs safe local/remote git sync via GitHub, wants to inspect remote state, submit jobs, start interactive sessions, monitor logs, or recover project context at the start of a new coding session.
Finalize an accepted ML or AI paper for camera-ready submission after reviews, rebuttal, and acceptance. Use this skill whenever the user has an accepted paper, camera-ready deadline, final revision, acceptance email, meta-review, rebuttal promises, author-response commitments, de-anonymization tasks, supplement updates, code links, acknowledgements, final LaTeX checks, or needs to ensure the accepted paper's claims, figures, references, and artifacts are consistent before final submission.
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.
Initialize a full ML research project control root with independent paper, code, and optional slide repositories, shared project memory, root-level agent guidance, code-owned worktree policy, and component handoffs. Use when starting a new research project, setting up a project root for agents, connecting paper/code/slides repos, or replacing a simple paper+code workspace with a lifecycle-aware research project structure.
Maintain a paper-facing evidence board that aligns claims, experiments, figures, tables, sections, reviewer risks, and next actions during ML/AI paper writing. Use this skill whenever writing exposes missing experiments, new results require paper changes, reviewer simulation reveals evidence gaps, claims need support checks, figures/tables need mapping to claims, or the user wants a live paper evidence board before submission.
Review ML or AI experiment figures, tables, plots, captions, result narratives, and paper visual style before they are shown in a paper, advisor meeting, report, slide deck, rebuttal, or submission. Use this skill whenever the user has experimental results, plots, tables, metrics, screenshots, captions, draft result sections, or wants to audit figure style choices such as color, typography, markers, symbols, line widths, sizing, and venue-consistent visual conventions.
Build a retrospective or forward-looking work timeline from git commits, project docs, user notes, or chat records, then output a Markdown and/or HTML report with a Gantt chart or timeline visualization. Use when the user wants to review past work across one or more projects, explain time allocation to a mentor, summarize what was done in a period, or plan the next phase with a timeline.