Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Design, tune, validate, and test VictoriaMetrics vmanomaly configurations for known metrics or LogsQL queries. Use when choosing static alerting versus ML, selecting a vmanomaly model, configuring Temporal Envelope, building deployment YAML, tuning anomaly sensitivity, or creating VMAlert rules. Trigger on anomaly configuration, model selection, shared autotune, seasonal anomaly detection, forecasting, or "how should I monitor this metric?" Do not use for open-ended discovery across unknown signals.
Operate VictoriaMetrics Anomaly Detection (vmanomaly) through its HTTP API. Use when checking health, versions, persisted-state compatibility, self-monitoring metrics, available models, model schemas, server queries, time-series characteristics, shared autotune, or asynchronous detection tasks. Also use for generating and validating vmanomaly configuration or alert-rule YAML. Triggers on vmanomaly API, anomaly tasks, model validation, model schema, compatibility, Temporal Envelope, time-series profiling, and vmanomaly autotune.
Use when the user says 'make my store visible to chatgpt', 'set up product feeds', or 'make my store agent-ready', or asks about Google Merchant Center, a store llms.txt, or cart links for AI agents. Sets up product feeds in six formats and registers the catalog with the major platforms.
Use when the user says 'fix my catalog for ai', 'improve my ai readiness score', 'get my catalog to 90', or 'my products keep getting rejected by google'. Runs an ordered fixlist that writes the missing alt text, descriptions, brands, GTINs, and category mappings, snapshotting before each change.
Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.
Export, list, and validate Page Designer content from B2C Commerce libraries. Use this skill whenever the user needs to export Page Designer pages, components, or content blocks, list pages in a content library, validate page JSON or metadefinitions, discover page IDs, migrate content between instances, or work with library XML offline. Also use when extracting content for review or building content deployment pipelines -- even if they just say 'export the homepage' or 'what pages are in the shared library'.
Data analysis and visualization. Query databases, generate reports, automate spreadsheets, and turn raw data into clear, actionable insights. Use when (1) you need to analyze, visualize, or explain data; (2) the user wants reports, dashboards, or metrics turned into a decision; (3) the work involves SQL, Python, spreadsheets, BI tools, or notebooks; (4) you need to compare segments, cohorts, funnels, experiments, or time periods; (5) the user explicitly installs or references the skill for the current task.
Use this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows, governance, modeling, and migration decisions. Load it when teams need workload-grounded tradeoffs, ownership and quality agreements, or a current-to-target data architecture. Do not use it for pipeline or platform operations, implementation details, interface contract semantics, SQL tuning, or statistical modeling; route those to data-engineering, platform-engineering, api-design-and-evolution, postgres, or data-scientist.
A workflow for conducting rigorous, reproducible data investigations and ad-hoc analyses. This skill should be used when investigating a business question, explaining a metric anomaly, validating a hypothesis, performing root cause analysis, or preparing a one-off investigation dashboard or memo.
Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive and consultant-style. Asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user asks to generate, create, or build a dataset for evaluation, testing, or benchmarking.
DICOM and NIfTI medical image processing pipeline. Triggers on DICOM, NIfTI, dcm2niix, de-identification, pydicom, DICOM header, conversion, anonymization, BIDS, DICOM tags, medical image format conversion, "DICOM to NIfTI", "burned-in PHI", "SeriesInstanceUID", "nibabel", "DICOM anonymization".
The Douyin traffic allocation dashboard is for content creators, operations, e-commerce, and marketing teams who want to know where Douyin traffic is flowing; it generates traffic distribution, category hierarchies, and platform direction signals usable for content planning based on the input material.
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
Validates practitioner credentials and license status against the NPI registry. Cross-references specialties, credentials, and practice addresses against official records. Returns Verified / Partially Verified / Unverified / Flagged per practitioner with mismatch details and source URLs. Triggers: "verify these doctors", "check provider credentials", "validate licenses", "verify NPI numbers", "cross-check credentials against NPI", "compliance audit on providers", "are these practitioners still licensed", "validate my provider list". Accepts CSV, Google Sheet URL, or pasted data. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for filling data gaps — use healthcare-providers-enrich instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
Reason about ML experiment design for healthcare and life sciences data. Use when the user asks to design an ML study, choose a model for clinical/biomedical data, set up cross-validation, pick evaluation metrics, audit fairness, plan a regulatory submission, or critique an ML pipeline on EHR, medical imaging, genomics, molecules, or clinical text. Triggers include "design an ML experiment", "which model for this clinical data", "how should I split", "nested CV", "class imbalance", "AUROC vs AUPRC", "calibration", "decision curve", "net benefit", "TRIPOD+AI", "PROBAST", "CLAIM", "FDA SaMD", "PCCP", "GMLP", "site generalization", "temporal leakage", "scaffold split", "foundation model evaluation", "subgroup fairness", "is this model ready for deployment".
Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes. Use this skill when the user specifically requests Python for an n8n Code node. Note — JavaScript is recommended for 95% of use cases — only use Python when the user explicitly prefers it or the task requires Python-specific standard library capabilities (regex, hashlib, statistics). EXCEPTION — for Python in the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode), use the n8n-code-tool skill instead (input is _query, return must be a string).
Use this skill when the user wants to configure how long Nebula Logger keeps log records before deleting or archiving them. Covers retention date semantics on `Log__c`, the `LogBatchPurger` batch job, `LogBatchPurgeScheduler`, purge action values, and how to tune retention per user, profile, or scenario.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
Compatibility guide for developers migrating from Oracle Database to MariaDB. Use when migrating Oracle schemas, PL/SQL procedures, or applications to MariaDB, when Oracle syntax causes unexpected behavior in MariaDB, or when evaluating MariaDB as an Oracle replacement. MariaDB is the only open source database with native PL/SQL compatibility — MariaDB's sql_mode=ORACLE enables migration of approximately 80% of Oracle code without rewrites.
This skill should be used when the user needs to consume the Pier Cloud (Lighthouse) API for cloud cost management — including JWT authentication, listing contexts, workspaces, workspace groups, and FinOps data views. Trigger whenever there is a need to integrate, automate, or debug calls to the Pier Cloud platform via Python, Node.js, or cURL.
Extract structured data from Office documents (DOCX, PPTX, XLSX, HWP, HWPX) using the Polaris AI DataInsight Doc Extract API. Use when the user wants to parse, analyze, or extract text, tables, charts, images, or shapes from document files. Invoke this skill whenever the user mentions extracting content from Word, PowerPoint, Excel, HWP, or HWPX files, wants to parse document structure, needs to convert document data for RAG pipelines, or asks about reading tables, charts, or text from Office-format documents — even if they don't explicitly mention "DataInsight" or "Polaris".
Guides PostgreSQL development including table design, indexing, constraints, PL/pgSQL, JSONB, full-text search, window functions, CTEs, EXPLAIN ANALYZE tuning, backup/restore, replication, and extensions like pgvector. Use when the user needs to write or optimize PostgreSQL queries, design schemas, or manage PostgreSQL databases.
Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
This skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English.
Detect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis.
Routes Tilebox tasks and implementation-oriented Earth observation or geospatial processing. Use for satellite, aerial, SAR, hyperspectral, or raster imagery; STAC or provider-catalog ingestion; AOI and time-series analysis; mosaics, spectral indices, classification, segmentation, change detection, and object detection; or environmental, agricultural, disaster, maritime, urban, and infrastructure monitoring—even when Tilebox is not named. For new processing projects, orchestrates workflow initialization, Python authoring, deployment, and job execution.
Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP). Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points. Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, or Stage 2 momentum stock scanning.
Audit an existing VictoriaMetrics vmanomaly configuration against runtime capabilities and real data. Use when reviewing model-data fit, scheduler cadence, persisted-state compatibility, cold start, excessive detections, missing anomalies, output cardinality, or upgrade readiness. Trigger on vmanomaly config review, false-positive investigation, detection quality, model effectiveness, compatibility, and "is my anomaly detection working?" Use vmanomaly-config instead when building a new configuration from scratch.
Implement web page content extraction capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to scrape web pages, extract article content, retrieve page metadata, or build applications that process web content. Supports automatic content extraction with title, HTML, and publication time retrieval.
Use when the user needs Excel file manipulation — reading, writing, formulas, charts, conditional formatting, data validation, pivot tables, or large file handling. Trigger conditions: create Excel reports programmatically, read spreadsheet data, add formulas or charts, apply conditional formatting, perform data validation, generate pivot tables, handle CSV import/export, process large datasets in Excel format.
Chains estimation, decision analysis, and storytelling to transform uncertain choices into clear, stakeholder-ready recommendations. Quantifies uncertain variables, applies expected value analysis to identify the best option, then packages the analysis into a persuasive narrative. Use when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs, justifying investments, pitching to decision-makers, or when user mentions ROI analysis, expected value, business case, cost-benefit, or needs to combine estimation with persuasive communication.
Reason about clinical data terminology standards — MedDRA hierarchy (LLT→PT→HLT→HLGT→SOC), ICD-10 code structure and grouping, SNOMED CT concept model, LOINC panel relationships, and mapping decisions between systems. Use when the user asks to code adverse events, map diagnoses to ICD-10, choose a coding granularity level, group AEs by SOC or PT, interpret SNOMED CT relationships, select LOINC codes for lab panels, convert between terminology systems, or decide when to aggregate at HLT vs PT level. Triggers include "MedDRA coding", "ICD-10 grouping", "SNOMED CT", "LOINC panel", "adverse event coding", "terminology mapping", "SOC table", "preferred term", "code hierarchy", "clinical coding", "AE frequency table", "diagnosis grouping", "lab code selection", "cross-walk between terminologies".
Generates structured scaffolds (frameworks, checklists, templates) for technical work — TDD test suites, exploratory data analysis plans, statistical analysis designs, causal vs predictive modeling objectives, and validation checklists. Use when starting technical work that needs systematic planning before execution. Invoke when user mentions "write tests for", "explore this dataset", "analyze", "model", "validate", "design an A/B test", or when technical work needs scaffolding before execution.
Craft CMS 5 content modeling — sections, entry types, fields, Matrix, relations, project config, and content architecture strategy. Covers choosing section types, designing entry types and field layouts, selecting field types, configuring Matrix and nested entries, relations and eager loading, and multi-site propagation.
DAX performance optimization for semantic models. Automatically invoke when the user asks to “optimize DAX,” “fix slow DAX,” “DAX performance,” “tune a measure,” “debug a measure,” “DAX anti-patterns,” or mentions slow queries, server timings, or DAX authoring.
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.
Use when adding or auditing ECS entity.* fields (entity inventory / entity store) on Elastic integrations — classifying a data stream as entity vs event, mapping vendor fields to entity.attributes.*, entity.lifecycle.*, and entity.relationships.*, or running an entity-coverage gap analysis on an existing package. Invoke manually with /entity-mappings.
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.