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Topic: openclaw

3,425 skills in this topic.

distributed-training Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
aiming-lab/AutoResearchClaw 11,027
mixed-precision Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab/AutoResearchClaw 11,027
pytorch-training Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
aiming-lab/AutoResearchClaw 11,027
chemistry-rdkit Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
aiming-lab/AutoResearchClaw 11,027
cv-classification Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
aiming-lab/AutoResearchClaw 11,027
cv-detection Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
aiming-lab/AutoResearchClaw 11,027
nlp-alignment Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
aiming-lab/AutoResearchClaw 11,027
nlp-pretraining Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
aiming-lab/AutoResearchClaw 11,027
rl-policy-optimization Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
aiming-lab/AutoResearchClaw 11,027
experimental-design Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
aiming-lab/AutoResearchClaw 11,027
hypothesis-formulation Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab/AutoResearchClaw 11,027
literature-search Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.
aiming-lab/AutoResearchClaw 11,027
meta-analysis Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
aiming-lab/AutoResearchClaw 11,027
scientific-visualization Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
aiming-lab/AutoResearchClaw 11,027
scientific-writing Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
aiming-lab/AutoResearchClaw 11,027
statistical-reporting Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
aiming-lab/AutoResearchClaw 11,027
systematic-review Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
aiming-lab/AutoResearchClaw 11,027
biology-biopython Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
aiming-lab/AutoResearchClaw 11,027
statistical-reporting Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.
aiming-lab/AutoResearchClaw 11,027
scientific-writing Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.
aiming-lab/AutoResearchClaw 11,027
scientific-visualization Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
aiming-lab/AutoResearchClaw 11,027
skillshare-changelog Generate CHANGELOG.md entry from recent commits in conventional format. Also syncs the website changelog page. Use this skill whenever the user asks to: generate a changelog, document what changed between tags, or create a new CHANGELOG entry. If you see requests like "write the changelog for v0.17", "what changed since last release", this is the skill to use. Do NOT manually edit CHANGELOG.md without this skill — it ensures proper formatting, user-perspective writing, and website changelog sync. For full release workflows (tests, changelog, release notes, version bump, announcements), use /release instead.
runkids/skillshare 1,424
skillshare-cli-e2e-test Run isolated E2E tests in devcontainer from ai_docs/tests runbooks. Use this skill whenever the user asks to: run an E2E test, execute a test runbook, validate a feature end-to-end, create a new runbook, or test CLI behavior in isolation. If you need to run a multi-step CLI validation sequence (init → install → sync → verify), this is the skill — it handles ssenv isolation, flag verification, and structured reporting. Prefer this over ad-hoc docker exec sequences for any test that follows a runbook or needs reproducible isolation.
runkids/skillshare 1,424
skillshare-codebase-audit Cross-validate CLI flags, docs, tests, and targets for consistency across the codebase. Use this skill whenever the user asks to: audit the codebase, check for consistency issues, find undocumented flags, verify test coverage, validate targets.yaml, check handler split conventions, or verify oplog instrumentation. This is a read-only audit — it reports issues but never modifies files. Use after large refactors, before releases, or whenever you suspect docs/code/tests have drifted out of sync.
runkids/skillshare 1,424