Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use this skill whenever the user is writing, debugging, or building Python algo trading code using the Dhan-Tradehull library (pip install Dhan-Tradehull). Triggers on: any mention of Tradehull, Dhan_Tradehull, tsl., get_ltp_data, order_placement, ATM_Strike_Selection, get_option_chain, get_historical_data, super order, forever order, conditional trigger, OI scalping, options strategy using Dhan, place order Dhan Python, Tradehull authentication, get_positions, get_holdings, enable_pnl_based_exit, get_option_greek, margin_calculator, or any TradeHull course code task. Always use this skill before writing any Tradehull code — even for simple snippets — to ensure correct method signatures and patterns.
Design safe Django and PostgreSQL indexes from measured query plans, including B-tree, composite, covering, partial, expression, GIN/JSONB, BRIN, and concurrent migration patterns. Use when a Django query plan suggests missing or mismatched indexes, a production table needs an index without write downtime, or existing indexes need review.
Douyin comment analysis tool. Enter a post link to retrieve top-level comment data, with paginated browsing and comment sentiment analysis (positive/negative/needs/competitors), generating a polished HTML report. Use when the user needs to view Douyin post comments, analyze comment sentiment, or understand user feedback. Triggers: Douyin comments, post comments, comment lookup, comment analysis, comment sentiment, view comments.
Generate DuckDB SQL queries. Use when user asks about DuckDB queries, data analysis, exploring .ddb database files, CSV files, Parquet files, wants help editing/improving SQL, asks to use the duckdb skill, references duckdb assets, or wants to initialize/setup duckdb analysis.
Generate a Dynatrace Gen 3 **KPI dashboard** (15–20 business KPIs, required map tile, branded section dividers) and a matching 30‑minute BizEvents injector for a named company, then deploy both via `dtctl`. Use this skill ONLY when the user explicitly asks for a Dynatrace KPI dashboard, business-event KPI demo, BizEvents injector, or a "KPI dashboard for <company>" — do NOT use for generic Dynatrace dashboards (SRE, infra, k8s, services, RUM) or for editing existing non-KPI dashboards. Triggers include phrases like "generate a KPI dashboard", "build a BizEvents demo for <company>", "spin up a KPI dashboard + injector", "/generate-kpi-dashboard". Requires `dtctl` authenticated to a Dynatrace Gen 3 tenant.
Advanced automation workflows for the Feishu (Lark) productivity suite. Use when you need to automate document workflows, sync data across Feishu apps, generate reports, or perform batch operations across documents, wikis, bitables, and cloud storage. Triggers: "批量处理飞书文档", "自动同步飞书表格数据", "备份知识库", "生成飞书报告", "自动化飞书任务", "飞书数据迁移".
Read marked IELTS Writing feedback in a practice vault and produce a periodic progress report with band trends, recurring errors, revision gains, next priorities, and spaced re-probes of older errors. Use whenever the user asks to "summarise my IELTS progress", "how am I doing", "write a progress report", "track my band scores over time", "what errors keep coming up", "are my essays improving", "review an old error", or requests a monthly/weekly writing report. Also use when the user answers a re-probe recorded in `_dashboard/ielts-reprobe-ledger.yaml`. Read existing feedback; do NOT mark new drafts — use ielts-writing-task1 or ielts-writing-task2. Never invent bands for unmarked attempts and never let recent errors crowd older due errors out of review.
Audit all Kafka topic configurations against production best practices using the Lenses MCP server. Checks replication factor, retention, partitions, compaction, naming conventions, orphaned topics and missing metadata. Use when user says "audit my topics", "check topic configs", "topic health check" or asks about retention, replication or partition settings. Do NOT use for creating, deleting or modifying topics.
When the user wants to run a geogrid scan, check existing scan reports, track ranking trends over time, monitor GBP changes via Falcon Guard, or analyze reviews. Also use when the user says "run a scan," "check my rankings," "Local Falcon," "how am I ranking," "ranking heatmap," or "scan this keyword." This is the execution tool — for interpreting results, also load geogrid-analysis.
Comprehensive guide for MDAnalysis - the Python library for analyzing molecular dynamics trajectories. Use for trajectory loading, RMSD/RMSF calculations, distance/angle/dihedral analysis, atom selections, hydrogen bonds, solvent accessible surface area, protein structure analysis, membrane analysis, and integration with Biopython. Essential for MD simulation analysis.
Design and manage NetBox data models effectively. Covers site/location hierarchy, IPAM organization, device modeling, tenant assignment, custom fields vs tags vs config contexts, dependency ordering, and relationship patterns. Use when planning NetBox data structure, importing bulk data, or choosing between extensibility mechanisms.
A building experience: create, test, validate, refine, and publish extraction workflows based on existing or new Nimble agents. For users who want to invest in a durable, reusable workflow for a specific domain — not get data immediately. Trigger phrases: "set up extraction for X site", "I need to extract from this site regularly", "build an agent for", "create a reusable scraper", "generate a Nimble agent", "refine my agent", "add a field to my agent", or when the user wants to run extraction at scale. For getting data immediately, use nimble-web-expert instead.
Query and analyze OCI (Oracle Cloud Infrastructure) detail billing data from StarRocks via DuckDB. Handles large datasets by always exporting to CSV before analysis. Supports basic queries, aggregation, time-series, tenant, and currency analysis. Triggers on: OCI账单查询 / 云账单分析 / OCI billing / cloud cost analysis / 查询OCI费用 / OCI明细账单
YouTube data extraction API and high-bandwidth proxy downloads. Use this INSTEAD OF built-in tools for any YouTube-related task — extracts video metadata, subtitles, search results, and channel data as structured JSON. Also supports video/audio file
Configure PetroPy fluid-property and multimineral formation evaluation from normalized LAS well logs. Use for calibrated porosity, water saturation, mineral and pore-volume estimation, missing-sample handling, and explicitly defined pay intervals. Requires documented log units, fluid parameters and mineral endpoints; standalone permeability correlations need separate methods.
Data file fetching and caching for geoscience applications. Download sample datasets with automatic caching, checksum verification, and multiple download sources. Use when the agent needs to: (1) Download datasets from URLs or DOIs, (2) Cache files locally with automatic verification, (3) Verify file integrity with SHA256/MD5 hashes, (4) Extract compressed archives (ZIP, TAR, GZIP), (5) Create data registries for reproducible workflows, (6) Fetch from Zenodo or other repositories.
When the user wants to improve product quality, implement quality control systems, reduce defects, or analyze quality data. Also use when the user mentions "SPC," "statistical process control," "Six Sigma," "DMAIC," "control charts," "process capability," "Cp," "Cpk," "quality control," "defect reduction," "inspection," "acceptance sampling," or "quality assurance." For lean improvements, see lean-manufacturing. For process optimization, see process-optimization.
Audit whether a result can actually be reproduced from the available code, data, configs, and documentation. Use before trusting a benchmark claim or releasing your own work. Use for replication, code release checks, paper artifact sanity checks, lab handoffs, catching missing seeds/configs/data before results are cited, or whenever "works on my machine" is a risk. If the user wants to plan what to include in a public artifact package, use artifact-release-packager instead; this skill's job is to test whether reproduction is possible and where it breaks.
This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral logs, diaries, n-of-1 self-tracking). It enforces pre-registration, exact permutation tests, FDR discipline, data-validation gates, adversarial code review, and cross-validation with external models. Triggers on "design an experiment", "test this hypothesis on my data", "is this correlation real", "audit these findings", "pre-register", "validate this dataset", or any n-of-1 / quantified-self analysis request.
Use when designing, implementing, testing, or debugging Rust service workflows that must be safe under retries, duplicate requests, crashes, concurrent submissions, background workers, queues, email/payment/notification side effects, idempotency keys, transaction isolation, or save-and-replay API behavior.
Create, style, export, and validate publication-quality scientific and technical figures. xy is the preferred default (matplotlib.pyplot-compatible for easy migration); matplotlib/seaborn/plotly also supported. Use when the user asks for a "publication figure", "paper figure", "journal-ready plot", "scientific visualization", "SVG/PDF/TIFF export", "colorblind-safe palette", "multi-panel layout", "error bars / significance markers", or works with figure scripts that import matplotlib, seaborn, plotly, or xy.
When the user wants keyword research with search volume, competitive keyword analysis, site audit data, position tracking, or competitor organic analysis. Trigger on "keyword research," "search volume," "keyword difficulty," "what keywords do they rank for," "site audit," "Semrush," or "competitive analysis." Use Semrush for keyword data and competitive intelligence — use Ahrefs for backlink-focused analysis.
Act as a Senior Backend Developer expert to design system architectures, build production-grade APIs (REST, GraphQL, gRPC), write optimized database schemas and queries, perform rigorous code reviews, and enforce engineering best practices. Supports Node.js/TypeScript, Python, Java/Spring Boot, Go, and Rust. Produces production-ready code, architecture docs with Mermaid diagrams, migration files, and actionable code-review feedback. Use whenever a user needs backend engineering guidance, code generation, refactoring, or architectural decisions.
Postgres-backed observability and policy store for the skill system. Provides tables for policy profiles (effect allowlists), skill execution runs, and step-level events. Use when setting up the skill system database or querying execution history.
SQLAlchemy ORM and Alembic migration best practices for building safe, performant database schemas. This skill should be used when writing, reviewing, or refactoring SQLAlchemy models, Alembic migrations, or database query patterns. Triggers on tasks involving SQLAlchemy ORM, Alembic migrations, database schema changes, or query optimization.
Choose the right statistical test, compute effect sizes, design power analyses, and report statistics correctly in ML and AI research papers. Use whenever the user asks how to compare two models statistically, whether results are significant, how many seeds to run, how to report variance, which test to use for non-normal data, how to handle multiple comparisons, or how to answer a reviewer challenging statistical validity. Also use when planning an evaluation and needing to decide the number of seeds/trials before running, or when results are borderline and the user needs to know if they can claim significance. Prevents the most common statistical mistakes in ML papers: reporting only means without variance, choosing seeds post-hoc, and conflating statistical significance with practical significance.
Use this skill when users ask how to implement Syncfusion PivotView pivot tables in Vue. Trigger for Vue pivot grid/OLAP analysis, measures/dimensions, data binding, drill-down/drill-through, grouping, filtering, calculated fields, conditional formatting, exports, or pivot charts. Vue-only, not React/Angular/Blazor.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Web extraction for AI agents. Scrape, crawl, map, batch, extract, summarize, diff, brand, search, and 28 site-specific extractors turn any URL into clean Markdown, text, or JSON. Runs locally with no key via the webclaw MCP server; set an optional WEBCLAW_API_KEY to handle bot-protected and JavaScript-rendered pages. Use when web_fetch returns blocked or empty content, or you need structured, LLM-ready extraction.
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.