Topic: openclaw
3,425 skills in this topic.
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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
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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
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experimental-design
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
aiming-lab/AutoResearchClaw 11,027
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hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab/AutoResearchClaw 11,027
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mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab/AutoResearchClaw 11,027
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researchclaw
Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.
aiming-lab/AutoResearchClaw 11,027
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pytorch-training
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
aiming-lab/AutoResearchClaw 11,027
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systematic-review
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
aiming-lab/AutoResearchClaw 11,027
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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
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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
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nlp-pretraining
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
aiming-lab/AutoResearchClaw 11,027
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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
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a-evolve
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.
aiming-lab/AutoResearchClaw 11,027
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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
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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
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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
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distributed-training
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
aiming-lab/AutoResearchClaw 11,027
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hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab/AutoResearchClaw 11,027
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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
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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
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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
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skillshare-update-docs
Update website docs to match recent code changes, cross-validating every flag against source. Use this skill whenever the user asks to: update documentation, sync docs with code, document a new flag or command, fix stale docs, or update the README. This skill covers all website/docs/ categories (commands, reference, understand, how-to, troubleshooting, getting-started) plus the built-in skill description and README. If you just implemented a feature and need to update docs, this is the skill to use. Never manually edit website docs without cross-validating flags against Go source first.
runkids/skillshare 1,424
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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
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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