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Agent skills
machine-learning

Topic: machine-learning

247 skills in this topic.

formula-derivation Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof.
wanshuiyin/Auto-claude-code-research-in-sleep 6,306
grant-proposal Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says "write grant", "grant proposal", "申請書", "write KAKENHI", "科研費", "基金申请", "写基金", "NSF proposal", or wants to turn research ideas into a funding application.
wanshuiyin/Auto-claude-code-research-in-sleep 6,306
ml-ralph REQUIRED first step for ANY ML task. When user describes an ML problem, goal, experiment, or model improvement — ALWAYS invoke this skill BEFORE exploring code or planning. Triggers: ml-ralph, create prd, ml project, kaggle, implement model, improve model, train model, better model, new approach, experiment.
pentoai/ml-ralph 33
ralph-file-schemas Schema reference for ml-ralph state files (prd.json, kanban.json). Use when reading or writing these files.
pentoai/ml-ralph 33
ralph-log-events Event schemas for ml-ralph log.jsonl. Use when logging any event.
pentoai/ml-ralph 33
ml-ralph Create ML project PRDs. Triggers: ml-ralph, create prd, ml project, kaggle.
pentoai/ml-ralph 33
auto-experiment Launch an autonomous THINK→EXECUTE→REFLECT experiment loop on a GPU project
Xiangyue-Zhang/auto-deep-researcher-24x7 302
conf-search Search papers from top AI/ML conferences
Xiangyue-Zhang/auto-deep-researcher-24x7 302
daily-papers Daily arXiv paper recommendations with automatic deduplication
Xiangyue-Zhang/auto-deep-researcher-24x7 302
experiment-status Check status of running autonomous experiment loops
Xiangyue-Zhang/auto-deep-researcher-24x7 302
gpu-monitor Check GPU status, running experiments, and available resources
Xiangyue-Zhang/auto-deep-researcher-24x7 302
obsidian-sync Refresh Obsidian dashboard and daily notes from current experiment state
Xiangyue-Zhang/auto-deep-researcher-24x7 302
paper-analyze Deep analysis of a single paper with figure extraction from arXiv source
Xiangyue-Zhang/auto-deep-researcher-24x7 302
progress-report Generate structured research progress reports
Xiangyue-Zhang/auto-deep-researcher-24x7 302
customer_support_agent
Leeroo-AI/leeroopedia-mcp 10
llm_post_training
Leeroo-AI/leeroopedia-mcp 10
ml_inference_optimization
Leeroo-AI/leeroopedia-mcp 10
self_evolve_rag
Leeroo-AI/leeroopedia-mcp 10
ml-debug Use when something is failing in ML/AI work — OOM, NaN, divergence, crashes, bad throughput, wrong outputs, dependency conflicts
Leeroo-AI/superml 165
ml-experiment Use when starting, logging, or reviewing ML experiments — maintains a persistent experiment journal with hypotheses, results, and learnings across sessions
Leeroo-AI/superml 165
ml-iterate Use when the user is stuck, needs ranked next steps, or wants alternatives after initial experiments — "I tried X and got Y, what next?"
Leeroo-AI/superml 165
ml-plan Use when the user wants an implementation plan, architecture design, or multi-step ML pipeline — "build X", "implement X", "design X", "set up X"
Leeroo-AI/superml 165
ml-research Use when the user wants to understand an ML/AI topic, compare approaches, or survey framework capabilities — "how does X work?", "compare X vs Y"
Leeroo-AI/superml 165
ml-verify Use when the user wants to verify code, config, or math before running — or proactively before any expensive training job or deployment
Leeroo-AI/superml 165