jpmsilva1
UserA complete ecosystem for AI-assisted academic research. Features an orchestrated pipeline of 130+ ML skills, persistent state memory, and extreme token efficiency for large codebases.
Categories
Indexed Skills (8)
academic-code-replicator
Full-lifecycle skill for replicating experiments from academic papers. Guides the agent through understanding the original codebase, constructing isolated environments, making safe platform-compatibility adaptations, executing on available hardware, and producing a documented comparison against paper-reported results. Use this skill whenever the user says things like: "replicate this paper", "reproduce this experiment", "make this old code run", "run this paper's code", "I'm trying to get this GitHub repo from a paper working", "help me replicate these results", "this code is from 2019 and won't install", "I need to reproduce Table X from this paper", or any variation of scientific code replication. PROACTIVELY trigger even if the user just says "help me run this paper's experiments" without explicitly mentioning replication.
output-shaper
Dynamic token compression and verbosity controller. Forces the agent to be incredibly concise, acting as a manual output shaper. Supports three intensity profiles: lite, balanced, and ultra. Use whenever the user types "/output-shaper", complains about verbosity, asks to save tokens, or wants shorter answers.
academic-rebuttal-simulator
Simulates 'Reviewer 2' for ML papers (NeurIPS, ICLR). Critiques methodology, novelty, baselines, and related work. Outputs structured OpenReview-format reviews with sub-scores, suggests target venues with verified acceptance rates, and drafts conference rebuttals. Authored by João P. M. Silva.
distributed-gpu-engineer
Expert in scaling ML training across multiple GPUs and nodes. Masters SLURM, PyTorch Distributed Data Parallel (DDP), Ray, and CUDA OOM debugging. Authored by João P. M. Silva.
paper-code-finder
Find source code implementations and GitHub repositories for academic AI/ML research papers. Use this skill whenever a user asks for the code, repository, huggingface, or implementation of a paper. Ensure you trigger this even if they just say "is there code for this?" while looking at a paper.
research-orchestrator
Triggered when the user types /research or asks to start a research workflow. Guides the user through the full academic research lifecycle by suggesting the right skills at each stage. Opt-in only.
experiment-sweeper
Expert in ML hyperparameter orchestration. Converts scripts to use Hydra/OmegaConf and sets up Weights & Biases Sweeps. Authored by João P. M. Silva.
lint-vault
Triggered when the user types /lint or asks to health-check the Obsidian Vault. Scans the wiki/ directory to identify structural issues, orphan pages, broken wikilinks, stale index entries, and contradictions.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.