84 tagged machine learning, measured the same way as everything else here.
Browse within: data-science 21forecasting 15mlforecast 15neuralforecast 15deep-learning 14prompt-engineering 9Data Processing 7cpp 7data-pipeline 7jupyterlab 7Apple Silicon 6MLOps 6agent-skill 6benchmarking 6
AnastasiyaW/codex-claude-code-config
Agent
Independent reviewer of OBJECT INTERACTION physics in pixel-art scenes (gravity, occlusion order, surface support, light direction consistency, anchor points, scale plausibility). The 4th specialized reviewer in the pixel-art-quality-board orchestrator. Use when the user asks "do objects interact correctly", "is…
AnastasiyaW/codex-claude-code-config
Agent
Orchestrator agent that runs 4 specialized pixel-art reviewers in parallel (style + animation + composition + interaction), then synthesizes their verdicts into a single PASS/NEEDSWORK/REJECT decision with prioritized fixes. Use when the user asks to "review my pixel art quality", "score this animation"…
Agent
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.
vlasenkoalexey/tpu_performance_autoresearch_wiki
Agent Claude Code
Launch a single TPU training workload on a GKE cluster via XPK, poll until completion or hang, capture xprof + HLO dumps to GCS, and report structured verdict signals back to the master agent. Stateless one-shot worker — does NOT write wiki pages, decide experiment verdicts, or update the model page. Use for every…
vlasenkoalexey/tpu_performance_autoresearch_wiki
Agent Claude Code
Independent verifier for kernel-family experiments (the Roles section's verifier for the pallas lane). Given a final candidate kernel + the naive baseline, it independently re-benchmarks both in a fresh process, re-runs numerical parity, captures traces/LLO dumps with the canonical flag set, runs the hypothesis-firing…
vlasenkoalexey/tpu_performance_autoresearch_wiki
Agent Claude Code
Analyze a single completed experiment's xprof trace + HLO dump and return structured ## Profile + ## HLO Dump markdown sections that slot directly into the experiment page. Phases: Phase 1 walks xprof (bucket attribution, dominant ops, memory profile); Phase 2 walks HLO (module sizes, fusion verification, regression…
Agent
Expert in Google Colab for cloud-based ML/DL development with free GPU/TPU access. Specializes in Colab 2025 features (Gemini AI integration, google.colab.ai library), production workflows, session management, GitHub integration, Drive persistence, BigQuery/GCS integration, and optimizing for runtime limits. Use for…
Agent
Expert in Jupyter Notebook and JupyterLab for interactive computing, data analysis, machine learning experimentation, and reproducible research. Specializes in production-ready notebooks, version control, CI/CD integration, parameterization with Papermill, MLOps workflows, and JupyterLab 4.4+ modern features including…
Agent
AGILAB repo-aware implementation agent.
Agent
AGILAB review-only agent.
Agent
Human-maintained inventory of .cursor/rules and skills. Updated by the agent during agent-brain-refresh — not generated by a script.
Agent
Status (2026-06-30): Tide gate PASS — scratch iter175 / champion v8. Do not start new tide iter runs unless reproducing. Next AR work: gate-10song-smoke (EXPERIMENTLOG.md § Current phase).
Agent
Iterative improvements to agent behavior, process, and conventions on this project. Not a substitute for EXPERIMENTLOG.md or DISCUSSIONNOTES.md — those hold research findings; this holds how we work better.
jeremylongshore/plugins-nixtla
Agent
Contract: This agent evaluates the impact of proposed schema changes on systems, queries, and costs.
jeremylongshore/plugins-nixtla
Agent
Contract: This agent runs automated verification scripts to validate Phase 2-3 conclusions with empirical data.
jeremylongshore/plugins-nixtla
Agent
Contract: This agent synthesizes all phase outputs into actionable recommendations with implementation plans.
Agent
Look for blind spots, unsupported architecture families, missing validation gates, and overclaimed optimizations.
Agent
Find MLX or Apple-supported APIs, runtime constraints, packaging rules, and validation gates that should constrain porting guidance.
Agent
Track packages and release metadata that change install, conversion, runtime, or optional dependency guidance.
Agent
Fast structural profiling sweep for a dataset — shape, dtypes, missingness, cardinality, duplicate keys. Use during /ds-data or /ds-explore for a quick first-pass profile. Not for deep statistical analysis or judgment calls about what the findings mean — that's the calling skill's job.
Agent
Runs the ds-method discipline checklist against a notebook or pipeline before /ds-report — baseline present, validation strategy sound, slice performance checked, metric matches the problem. Use before final reporting/handoff, or when asked to sanity-check a DS pipeline end to end. Not for hunting leakage specifically…
Agent
Adversarially hunts for target leakage across a feature pipeline — features that encode the target directly, temporal leakage where future information reaches training data, and validation-split leakage. Use before /ds-model or /ds-report when a metric looks implausibly good, or as a final check before a pipeline…
Agent
This chat mode is designed for analyzing market trends, providing insights on financial markets, and assisting with investment strategies. The AI should respond in a professional and analytical manner, focusing on data-driven insights and market analysis.