Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
Assesses lawful basis for AI training data processing per EDPB April 2025 report on LLMs and general-purpose AI. Covers legitimate interest balancing tests, consent challenges for ML training, public dataset assessment, and web scraping lawfulness. Keywords: AI training data, lawful basis, EDPB LLM, legitimate interest, consent, web scraping.
ALGOPACK access, free and paid API keys, subscription, API key authorization, entitlement, and troubleshooting guidance for DataShop setup, bearer tokens, moexalgo .env session.TOKEN setup, direct REST headers, free or delayed access, 401, 403, 429, rate limits, and product boundaries.
Direct ALGOPACK and MOEX ISS market-data workflows for browser URLs, curl, JSON, CSV, simple HTML output, quotes, candles, trades, order books, securities metadata, EQ/FO/FX endpoint selection, iss.only filters, ISS columns/data normalization, start pagination, and no-library access.
This skill should be used when the user asks about "social media analytics", "platform analytics", "audience insights", "engagement rate", "social media strategy report", "competitive benchmarking", "act as a social media analyst", "social analyst mode", "social media metrics", "content performance", "follower growth", "social media audit", "social listening", or needs expertise in analyzing social media data and translating it into actionable strategy reports. Part of the AlterLab FC Skills collection (New Media & Communication department).
This skill should be used when the user asks about "content analysis", "coding scheme", "media content analysis", "quantitative content analysis", "qualitative content analysis", "discourse analysis", "framing analysis", "media representation", "intercoder reliability", "Krippendorff's alpha", "Cohen's kappa", "coding manual", "code book", "content coding", "act as a content analyst", "content analyst mode", "thematic coding", "manifest content", "latent content", "unit of analysis", "coding categories", "media framing", "representation studies", "textual analysis", "narrative coding", "frequency analysis", "Semetko frames", "Entman framing", "Fairclough discourse", "Mayring", "coding sheet", or needs expertise in systematic content analysis methodology and media coding frameworks. Part of the AlterLab FC Skills collection (Research Methods & Academic Writing department).
This skill should be used when the user asks about "statistics", "statistical analysis", "SPSS", "R statistics", "hypothesis testing", "t-test", "ANOVA", "chi-square", "regression analysis", "correlation", "p-value", "effect size", "confidence interval", "descriptive statistics", "inferential statistics", "data visualization", "act as a statistics guide", "statistics guide mode", "statistical significance", "research statistics", "quantitative analysis", "normal distribution", "sample size", "power analysis", "non-parametric tests", "Mann-Whitney", "Kruskal-Wallis", "Likert scale analysis", "survey data analysis", "Excel statistics", "JASP", "factor analysis", "reliability analysis", "Cronbach's alpha", "research data analysis", or needs expertise in applying statistical methods to academic research data. Part of the AlterLab FC Skills collection (Research Methods & Academic Writing department).
Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.
ByteHouse hybrid search Skill supports full-text search + vector search, combining them with the RRF reranking algorithm for more precise results. Use this Skill when the user needs full-text search + vector search in a ByteHouse database, combined with RRF reranking for more precise results.
A ByteHouse cluster load analysis and performance monitoring tool for analyzing cluster load, monitoring resource usage, analyzing query throughput, and identifying performance bottlenecks. Use this Skill when users need to analyze ByteHouse cluster load, monitor resource usage, analyze query throughput, or identify performance bottlenecks.
A skill for starting the ByteHouse MCP Server locally and invoking its tools. Use it to connect to a ByteHouse database and query data, interact with ByteHouse via the MCP protocol, and generate data asset catalogs and lineage analyses. Use this skill when users need to connect to a ByteHouse database, query data, interact with ByteHouse via MCP, or generate data asset catalogs and lineage analyses.
ByteHouse slow query analysis and performance optimization tool for identifying and analyzing slow queries, query performance optimization suggestions, viewing query execution plans, and analyzing query history trends. Use this Skill when users need to identify and analyze slow queries in ByteHouse databases, get query performance optimization suggestions, view query execution plans, or analyze query history trends.
Seederive unstructured data labeling platform, using LLM to batch-analyze and process text, audio and image data. Use this Skill whenever the user mentions any of the following scenarios: data labeling, annotation, sentiment analysis, tag classification, opinion extraction, translation, comment analysis, astroturfing detection, content scoring, tag library management, prompt optimization. Even if the user does not directly mention "Seederive", trigger this Skill whenever it involves classifying/labeling/analyzing/translating/scoring a batch of texts, or mentions phrases such as "help me analyze these comments", "what is the sentiment of this data", "identify astroturfing", "extract opinions", "translate this content", "build a tag system", "optimize it for me because results are poor". It also applies when the user provides a CSV/Excel file and requests batch processing.
Apply causal inference whenever the user is interpreting metrics, debugging system behavior, reading A/B test results, or trying to understand whether an observed change was caused by an action or by something else. Triggers on phrases like "X caused Y", "since we deployed this, metrics changed", "the A/B test showed a lift", "why did this metric move?", "is this correlation or causation?", "we changed X and Y improved", "how do we know this worked?", "the data shows…", or any situation where conclusions are being drawn from observational data. Also trigger before any decision based on metric interpretation — confusing correlation with causation leads to interventions that don't work and misattribution of credit. Never assume causation without applying this skill.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
INVOKE THIS SKILL IMMEDIATELY when user asks to: write/create/build a Nextflow pipeline or workflow, create any bioinformatics pipeline (RNA-seq, DNA-seq, variant calling, ChIP-seq, etc.), or compose/chain Nextflow modules (process or workflow modules) from the Nextflow Registry. This skill handles all Nextflow workflow creation tasks.
Design and audit creator, listing, plugin, skill, app, marketplace, content, or community ranking quality across relevance, quality signals, freshness, fairness, fraud resistance, diversity, monetization, review abuse, creator trust, and user outcomes. Use when marketplaces need discovery/ranking systems that reward quality without becoming pay-to-win or spam-prone.
Query documented public database APIs with explicit endpoints, filters, pagination, and provenance. Use when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.
Type-safe SQL ORM for TypeScript with zero runtime overhead. Use when code imports drizzle-orm, drizzle-kit, or drizzle-orm/pg-core, user asks about Drizzle schema design, queries, relations, migrations, or database management with Drizzle ORM. Covers PostgreSQL focus with pgTable, pgEnum, pgSchema, pgView, and drizzle-kit CLI.
Tracks ML experiments with SwanLab, an open-source tool covering swanlab.init, config and metric logging, scalar charts, and media logging (images, audio, text, GIFs, point clouds, molecules), in cloud, local (mode="local" with swanlab[dashboard]), or self-hosted setups. Includes integrations for PyTorch, Transformers, PyTorch Lightning, and Fastai. Use when logging metrics and hyperparameters for training runs, comparing runs across seeds, checkpoints, or hyperparameters, running an offline or self-hosted dashboard instead of managed SaaS, logging media alongside scalars, or adding tracking callbacks to a Transformers, Lightning, or Fastai training loop. Not for other trackers such as Weights & Biases or MLflow.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Financial analysis and research workflows for market research, equity research, comparable companies, precedent transactions, and DCF valuation. Use when asked to build or critique DCF models, comps/precedents tables, competitor or strategy analysis, market sizing, or to deliver analyst-ready outputs in Excel, PowerPoint, or Markdown from CSV/Excel/SQL/API/web data.
Queries 20+ bioinformatics databases and analysis services through the gget CLI and Python package, covering Ensembl gene search, info and sequences (ref, search, info, seq), BLAST, BLAT, MUSCLE, DIAMOND, PDB, AlphaFold, ELM, ARCHS4, CELLxGENE, Enrichr, Bgee, OpenTargets, cBioPortal, COSMIC, viral sequence downloads (virus), and 8cube mouse specificity and expression data. Use when looking up gene or transcript details, running a quick BLAST/BLAT search, fetching AlphaFold or PDB structures, running enrichment analysis on a gene list, downloading viral sequences with filters, or exploring disease and drug associations interactively. Not for batch processing or fine-grained BLAST control (use biopython) or multi-database Python pipelines (use bioservices).
Apply rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.
Guide developers in using Hebrew NLP models and tools, including DictaLM, DictaBERT, AlephBERT, and ivrit.ai. Use when the user asks about Hebrew text processing, Hebrew NLP, “ivrit,” Hebrew tokenization, Hebrew NER, Hebrew sentiment analysis, Hebrew speech-to-text, or needs to process Hebrew text programmatically. Covers model selection, preprocessing, and Hebrew-specific NLP challenges. Do NOT use for Arabic NLP (different tools) or general English NLP tasks.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.