Topic: openclaw
3,425 skills in this topic.
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biomni-research-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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bio-workflows-cnv-pipeline
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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organoid-drug-response-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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occupational-health-analyzer
分析职业健康数据、识别工作相关健康风险、评估职业健康状况、提供个性化职业健康建议。支持与睡眠、运动、心理健康等其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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gene-panel-design-agent
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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exploratory-data-analysis
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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drugbank-database
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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hipaa-compliance
Ensure HIPAA compliance when handling PHI (Protected Health Information). Use when writing code that accesses user health data, check-ins, journal entries, or any sensitive information. Activates for audit logging, data access, security events, and compliance questions.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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data-stats-analysis
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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networkx
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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lab-results
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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lit-synthesizer
Search PubMed and bioRxiv, summarise papers with LLM, build citation graphs, and generate literature review sections.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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drug-interaction-checker
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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doc-coauthoring
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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medical-research-toolkit
Query 14+ biomedical databases for drug repurposing, target discovery, clinical trials, and literature research. Access ChEMBL, PubMed, ClinicalTrials.gov, OpenTargets, OpenFDA, OMIM, Reactome, KEGG, UniProt, and more through a unified MCP endpoint. Use when researching disease targets, finding approved/investigational drugs, searching clinical evidence, discovering genetic associations, or analyzing compound bioactivity data.
FreedomIntelligence/OpenClaw-Medical-Skills 2,009
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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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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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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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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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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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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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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