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Found 107 Skills
Guide a focused CS or AI literature review sprint that turns a topic, idea, claim, or project direction into a ranked paper map, closest-work risk assessment, method taxonomy, novelty implications, baseline implications, and next actions. Use this skill whenever the user needs to survey a topic, check novelty, map related work, prepare a project, find canonical or recent papers, decide read/skim/ignore priority, or turn papers into a research direction.
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.
Create a new Git branch or code worktree for experiments, features, baselines, rebuttal fixes, or method revisions. Use when starting an isolated code direction, creating a branch, creating a project-aware code worktree under a project control root, or setting up a worktree with UV sync, IDE config copying, linked assets, and worktree memory.
Pre-submission checklist for LaTeX academic papers. Use when the user wants to submit a paper, check submission readiness, prepare camera-ready, switch to final mode, or verify a paper is ready for a conference deadline.
Write decision-oriented advisor, mentor, lab meeting, or research progress updates from project memory, experiment reports, papers, code changes, logs, and notes. Use this skill whenever the user needs a weekly update, advisor email, meeting note, progress memo, decision request, blocker summary, project status report, or concise research update that connects evidence, risks, options, asks, and next actions.
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.
Write structured experiment report documents from ML/research experiment notes, configs, logs, metrics, tables, and figures. Use this skill whenever the user asks to write an experiment report, research update, mentor update, weekly experiment summary, result analysis document, or presentation-ready experiment writeup, especially when the output should explain motivation, setup, algorithms, metrics, results, figures, interpretation, conclusions, limitations, and next steps.
Initialize, inspect, and maintain a hierarchical memory system for an ML research project across paper, code, worktrees, slides, reviewer simulation, rebuttal, experiments, claims, evidence, risks, and actions. Use this skill whenever the user wants cross-session project memory, project bootstrapping context, feedback-loop tracking, claim-evidence-risk-action alignment, worktree memory, or consistency between code results, paper writing, slides, reviews, and rebuttal.
Perform common Git operations safely with sandbox-aware failure handling. Use whenever the user wants to inspect or modify git state, especially for cherry-pick, merge, rebase, commit, branch, stash, or worktree workflows. Always use this skill when the user mentions a Git failure, conflict, cherry-pick, merge issue, worktree, branch checkout problem, lock file, permission denied, operation not permitted, or any case where a sandboxed agent might confuse an environment restriction with a real code conflict. Be proactive: if the task smells like Git state or Git write behavior, use this skill even if the user did not explicitly ask for a 'Git' workflow.
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.
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.