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Found 137 Skills
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.
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.
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.
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.
Analyse code changes since the last docs update and refresh the project's documentation files. Use when code has changed and documentation needs to be updated, after implementing new features, or before a milestone commit.
Add items (research objects) to existing research outline.
Use when responding to academic reviewers, planning revisions, writing rebuttals, mapping reviewer concerns, deciding concede/defend/reframe actions, or preparing camera-ready changes.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Use when developing academic research ideas, framing research questions, defining scope, novelty, contribution claims, hypotheses, operational definitions, evidence plans, or publishable positioning before SOTA, experiments, or writing.
Use when building a state-of-the-art review, literature matrix, related work section, systematic search, paper comparison, gap analysis, or citation-backed synthesis.