SEO
Search engine optimization
serp-analyzer
by OpenClaudia
Analyze Google search results (SERP) for any keyword. Use when the user says "analyze the SERP", "what ranks for", "SERP analysis", "competitive analysis for keyword", "content brief", "what's ranking", "search results for", "who ranks for", or asks about ranking content patterns for a keyword.
keyword-research
by OpenClaudia
Perform keyword research using the SemRush API. Use when the user says "find keywords", "keyword research", "what should I rank for", "keyword ideas", "search volume", "keyword difficulty", "topic clusters", "content gaps", or asks about SEO keywords for a topic or niche.
facebook-ads
by OpenClaudia
Create Facebook and Meta ad campaigns, write ad copy, define audiences, and plan budgets. Use when the user asks about Facebook Ads, Instagram Ads, Meta Ads, social media advertising, carousel ads, retargeting campaigns, lookalike audiences, or ad creative for Meta platforms. Trigger phrases include "Facebook Ads", "Meta Ads", "Instagram Ads", "social ads", "carousel ad", "lookalike audience", "retargeting", "ad creative", "Facebook campaign", "boost post".
content-strategy
by OpenClaudia
Build content strategies, editorial calendars, topic clusters, and content plans. Map content to buyer journey stages and plan distribution. Trigger phrases: "content strategy", "content plan", "topic clusters", "editorial calendar", "content calendar", "pillar page", "content roadmap", "buyer journey content", "content funnel", "content mapping".
programmatic-seo
by OpenClaudia
Create SEO-optimized pages at scale using programmatic/template-based approaches. Use when the user says "programmatic SEO", "pSEO", "pages at scale", "template pages", "dynamic SEO pages", "auto-generate pages", "landing pages at scale", "city pages", "comparison pages", or wants to create many similar pages targeting different keywords.
competitor-analysis
by OpenClaudia
Conduct full competitor strategy breakdowns across SEO, ads, social, email, pricing, and positioning. Use when the user asks to analyze competitors, benchmark against rivals, understand competitive landscape, find competitor weaknesses, or build a competitive matrix. Trigger phrases include "competitor analysis", "competitive analysis", "who are my competitors", "competitor research", "competitive landscape", "benchmark competitors", "competitor ads", "competitor SEO", "competitor pricing", "SWOT analysis", "competitive matrix".
semrush-research
by OpenClaudia
SEO and competitive intelligence via the SemRush API. Use when asked to research competitors, analyze domains, find keyword opportunities, check backlinks, or estimate traffic. Trigger phrases: "competitor analysis", "domain overview", "keyword research", "backlink check", "traffic estimate", "SEO intelligence", "semrush", "competitive research".
read-docs
by fuma-nama
Enables efficient, accurate retrieval of content from Fumadocs-powered documentation sites.
tooluniverse-protein-structure-retrieval
by mims-harvard
Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.
tooluniverse-gwas-finemapping
by mims-harvard
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
tooluniverse-gwas-study-explorer
by mims-harvard
Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.
tooluniverse-clinical-trial-matching
by mims-harvard
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.
prd
by github
'Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.'
slack-usage
by fcakyon
This skill should be used when user asks to "search Slack for messages", "find Slack messages about X", "get channel history", "look up conversation in Slack", or "find what someone said in Slack".
tooluniverse-expression-data-retrieval
by mims-harvard
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
tooluniverse-adverse-event-detection
by mims-harvard
Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
core-actionbook
by actionbook
Rust Developer AI Assistance System — Meta-Problem-Driven Knowledge Indexing
structural-search
by 0xDarkMatter
"Search code by AST structure using ast-grep. Find semantic patterns like function calls, imports, class definitions instead of text patterns. Triggers on: find all calls to X, search for pattern, refactor usages, find where function is used, structural search, ast-grep, sg."
hivemind
by activeloopai
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
external-context
by Yeachan-Heo
Invoke parallel document-specialist agents for external web searches and documentation lookup
tooluniverse-precision-medicine-stratification
by mims-harvard
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
tooluniverse-multiomic-disease-characterization
by mims-harvard
Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.
tooluniverse-gwas-trait-to-gene
by mims-harvard
Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions). Identifies genetic risk factors, prioritizes causal genes via locus-to-gene scoring, and assesses druggability. Use when asked to find genes associated with a disease or trait, discover genetic risk factors, translate GWAS signals to gene targets, or answer questions like "What genes are associated with type 2 diabetes?"
tooluniverse-metabolomics
by mims-harvard
Comprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases. Integrates HMDB (220k+ metabolites), MetaboLights, Metabolomics Workbench, and PubChem. Use when asked to identify or annotate metabolites (HMDB IDs, chemical properties, pathways), retrieve metabolomics study information from MetaboLights (MTBLS*) or Metabolomics Workbench (ST*), search for studies by keywords or disease, or generate comprehensive metabolomics research reports.