Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Per-feature NaN-safe Spearman/Pearson correlation across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail, correct pairwise deletion, degenerate input filtering, and large-dataset performance. Use statistical-analysis for test choice; shap-model-explainability for interpretability.
Provides comprehensive guidance for Navicat database management tool including database connection, query execution, data management, and database administration. Use when the user asks about Navicat, needs to manage databases with Navicat, execute SQL queries, or work with Navicat features.
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".
Generate publication-quality matplotlib figures by selecting a pre-built paper style and substituting user data. Use when: user provides data (numbers, arrays, or CSV) and wants a chart in a specific academic style; user asks to "plot this data", "make a bar chart", "draw a radar chart", "用这个风格画图", "把我的数据画出来"; or user selects a style name from the style catalog (bar_paired_delta, bar_grouped_hatch, line_confidence_band, line_training_curve, line_loss_with_inset, scatter_tsne_cluster, scatter_broken_axis, radar_dual_series).
Invoke whenever spatial SQL or its execution backend is the decision: PostGIS, DuckDB Spatial, SpatiaLite, ST_* functions, recurring spatial joins, concurrent/growing workloads, or large GeoParquet queries. Covers backend selection, schemas, GiST/BRIN indexes, KNN, geometry versus geography, correctness benchmarks, and EXPLAIN optimization. Use PostGIS for managed concurrent services and embedded engines for bounded local analytics when evidence supports that choice. Use geo-data-engineering for acquisition, conversion, and file-based ETL without spatial SQL.
Set up and maintain a research-experiment harness so experiments run fast and every paper number traces to one command. Use when starting a new project, running a sweep, aggregating runs into a paper table, or cleaning up a codebase that has become hard to navigate. Trigger on "how should I structure my code", "set up my project", "organize my experiments", "starting a new paper", "run all these configurations", "collect my results into a table", "my code is getting messy", "which run produced this number", or "I inherited this codebase". Built for researchers who are strong in the mathematics and weak in software: it fixes a pipeline contract up front so adding an experiment is one line in a list, never an edit to shared code. Not for designing the training loop itself (pytorch-training-recipe / jax-training-recipe), reproducing one paper's method (paper-to-code), choosing which experiment to run or diagnosing overfitting (experiment-design), or auditing a released artifact (reproducibility-audit).
Identifies root causes behind declining user retention with likely causes, supporting evidence, and fix experiments. Use when you observe a retention drop and need to systematically diagnose why users are leaving.
Create and audit truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning
Generate one unified performance report that brings together SEO rankings, AI-search visibility (SE Ranking) and social engagement (Planable) — delivered as a short text summary in chat plus a self-contained interactive HTML report. Use this skill whenever the user wants a combined cross-channel report, or says things like "give me a full report across SEO and social", "how did we do this month across search and social", "combined SEO + AI + social report for [client]", "build a cross-channel dashboard", or "one report that covers rankings, AI visibility and social". Always activate when the request spans both search (SE Ranking) and social (Planable) in a single report.
Best practices for Snowpark Python (DataFrames, UDFs, UDTFs, stored procedures) and dbt with the dbt-snowflake adapter. Use when writing server-side Snowpark pipelines, registering UDFs or stored procedures, choosing dbt materializations, configuring incremental models, or setting up sources and tests for a Snowflake-backed dbt project.
Use when running or explaining SpacetimeDB CLI commands and standalone configuration. Covers `spacetime init`, `build`, `publish`, `generate`, `call`, `sql`, `logs`, `describe`, `dev`, `server`, `start`, login/logout, and `config.toml`. Triggers on: spacetime CLI, spacetime publish, spacetime generate, spacetime sql, spacetime logs, spacetime server.
Implements Syncfusion JavaScript chart controls (Line, Area, Bar, Column, Pie, Polar, Radar, Waterfall, Stock). Use when building interactive data visualizations, dashboards, or real-time charts. Covers series and axes configuration, styling, animations, exporting, and technical indicators. Works with TypeScript (webpack/modules) and JavaScript (CDN/ES5).
Subsurface well data analysis toolkit for loading, processing, and analyzing well logs, projects, and formation tops. Built on lasio with enhanced curve processing. Use when the agent needs to: (1) Load wells from LAS files with metadata, (2) Work with multi-well Projects, (3) Process curves (despike, smooth, resample, normalize), (4) Manage formation tops, (5) Export well data to DataFrame/LAS/CSV, (6) Perform cross-well analysis and QC.
Run a corpus-scale, STATS-ONLY PII audit over a folder of session transcripts LOCALLY and produce an aggregate report — counts by type and by layer, the per-session redaction-rate distribution, document lengths, and a coarse residual proxy. Use when the user says "audit my sessions", "scan folder for PII", "how much PII across these transcripts", "PII stats for my corpus", "is my redaction holding at scale", or points at a directory of transcripts and asks how much personal data it contains. Fully local — raw text never leaves the machine; the report carries ZERO PII values, transcript substrings, or filenames (only anonymized own-NN ids and counts), so the aggregates are safe to surface. Run it on a RED (raw) corpus to size the PII, or on a GREEN (already-redacted) corpus to check residual leakage.
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
Create interactive data visualizations using Vega-Lite declarative JSON grammar. Supports 20+ chart types (bar, line, scatter, histogram, boxplot, grouped/stacked variations, etc.) via templates and programmatic builders. Use when users upload data for charting, request specific chart types, or mention visualizations. Produces portable JSON specs with inline data islands that work in Claude artifacts and can be adapted for production.
Optimize document chunking for RAG performance and retrieval quality. Use this skill when splitting documents, choosing chunk sizes, implementing semantic chunking, or improving RAG retrieval accuracy. Activate when: chunking, split documents, chunk size, text splitting, document processing, RAG performance, semantic chunking, overlap.
Migration companion for moving to Contentstack from another CMS. Guides content and application migration through a structured, step-by-step workflow. Currently supports migrations from Contentful, with additional source platforms planned. Use when users want to migrate, move, switch, port, or re-platform to Contentstack, including content models, content, assets, locales, application integrations, or website code. Trigger on requests such as "migrate to Contentstack", "move to Contentstack", "switch to Contentstack", "migrate from Contentful", "move my Contentful space", "port my CMS", or similar migration-related requests. The skill validates prerequisites, guides the migration process, provides progress checkpoints, and delivers a completion summary. Prefer this skill whenever a Contentstack migration is requested.
Expert CRO advisor that analyzes landing pages, product funnels, UI/UX friction, and provides data-driven A/B test ideas to maximize conversions, sign-ups, trials, and retention. Use when optimizing conversion rates, analyzing funnels, designing experiments, improving CTAs, reducing drop-offs, or when user mentions conversion rate, CRO, landing page optimization, A/B testing, or funnel analysis.
Create, bulk-create, list, update, test, and delete Bolna dispositions for post-call structured extraction. Use when the user wants typed outcomes (lead_qualified, appointment_time, sentiment, churn_risk, consent_captured) pulled from every transcript and exposed in `extracted_data` on executions and webhook payloads. Covers Free Text vs Pre-defined answer types, validation (timestamp, numeric, boolean, email, regex), confidence scoring, copy-on-write semantics for shared dispositions, and the per-agent test endpoint.
Design charts, metrics, and data tables that help users interpret and compare data. Use when the user asks to visualize data, choose chart types, improve dashboard analytics, or make data presentation accessible. Dashboard navigation and shell layout alone belong to add-ui or arrange.
MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries.
Build type-safe D1 databases with Drizzle ORM for Cloudflare Workers. Includes schema definition, migrations with Drizzle Kit, relations, and D1 batch API patterns. Prevents 12 errors including SQL BEGIN failures. Use when: defining D1 schemas, managing migrations, writing type-safe queries, implementing relations or prepared statements, using batch API for transactions, or troubleshooting D1_ERROR, BEGIN TRANSACTION, foreign keys, migration apply, or schema inference errors. Prevents 12 documented issues: D1 transaction errors (SQL BEGIN not supported), foreign key constraint failures during migrations, module import errors with Wrangler, D1 binding not found, migration apply failures, schema TypeScript inference errors, prepared statement caching issues, transaction rollback patterns, TypeScript strict mode errors, drizzle.config.ts not found, remote vs local database confusion, and wrangler.toml vs wrangler.jsonc mixing. Keywords: drizzle orm, drizzle d1, type-safe sql, drizzle schema, drizzle migrations, drizzle kit, orm cloudflare, d1 orm, drizzle typescript, drizzle relations, drizzle transactions, drizzle query builder, schema definition, prepared statements, drizzle batch, migration management, relational queries, drizzle joins, D1_ERROR, BEGIN TRANSACTION d1, foreign key constraint, migration failed, schema not found, d1 binding error
Entity Framework Core with DbContext, migrations, LINQ queries, relationships, and performance optimization. Covers EF Core 8+ patterns. USE WHEN: user mentions "Entity Framework", "EF Core", "DbContext", "migrations", "LINQ", "EF relationships", "database first", "code first" DO NOT USE FOR: Prisma - use `prisma`, Drizzle - use `drizzle`, Spring Data JPA - use `spring-data-jpa`, Dapper (raw SQL)
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
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.