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Found 163 Skills
Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.
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.
USE FOR web search, research, RAG, grounding, browse, find, lookups, fact-checking, documentation, agentic AI. All-in-one, optimized for AI agents. Pre-extracted, token-budgeted web content, deep research, news, images, videos, places, custom ranking
Persistent project-scoped store for deep research on large topics. Use for substantive questions - comparing libraries, evaluating tools, surveying solutions to hard problems. Not for plan notes, not for small facts, not for code-level decisions, not for ideas.
Use this skill for "review this paper", "review this manuscript", "peer review", "review my paper", "critique this manuscript", "review this submission", "give me feedback on my paper", "check my methods", "review my statistics", "review as a peer reviewer", "evaluate this manuscript", "review this PDF", or mentions manuscript review, peer review, paper critique, or methodological review.
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
This skill should be used when executing the epic-dev workflow, creating epic branches, managing sprint phases, working with git worktrees for phased feature development, or when the user mentions "epic workflow", "sprint phases", "phased development", or "git worktree workflow".
Always use this skill to search the web, research any topic, scrape information, find the latest data, or compare options. Delivers high-quality multi-source research with anti-bot resilience, browser scraping, parallel discovery, deep synthesis, and files with outputs.
Sync verified experiment results from the code repo or a code worktree into the paper's daily experiments log and project memory. Use when results in code/docs/results, code/docs/reports, code/docs/runs, worktree docs, logs, or user-confirmed metrics should be promoted into paper-facing evidence.
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.