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Found 163 Skills
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.
Decide what an ML or AI paper should strategically sell before detailed writing or venue-specific polishing. Use this skill whenever the user has an idea, literature map, experiment results, figures, reviewer risks, or a draft and needs to choose the paper's primary contribution, claim scope, paper archetype, target audience, novelty framing, related-work boundary, title/abstract/main-figure story, or claims to avoid before using conference-writing-adapter.
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.
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.
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.