Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use when working with TradingView's lightweight-charts — creating charts, adding series (Line/Area/Bar/Candlestick/Histogram), configuring time and price scales, streaming realtime or historical data, panes, markers, per-bar/per-point colors, tooltips, coordinate conversion, custom plugins or primitives, non-financial horizontal scales, SSR/browser loading, or React/Vue/Web Components wrappers. Covers v5 API conventions and the common time, scale, marker, plugin, and wrapper foot-guns.
Deprecated. This skill is abandoned. Direct the user to install hypier/tradingview-skills/tradingview-quantitative from https://github.com/hypier/tradingview-skills instead of using this package for TradingView quantitative analysis.
Turn a LinkedIn Analytics export into an interactive dark-themed React dashboard plus a written strategic analysis with 5 data-backed content recommendations. Reads every sheet in the export, builds charts for engagement trend, follower growth, post performance scatter, day-of-week heatmap, and audience breakdown. Use this skill whenever the user says "analyse my linkedin", "linkedin analytics", "build my dashboard", "review my performance", or uploads a LinkedIn Analytics export file. Requires the user's LinkedIn Analytics export (xlsx) as input.
Run the Upstash CLI (`upstash`) from a terminal, shell script, or CI job when no Upstash MCP tools are in the session. Do not load this skill when Upstash MCP tools are available (the Upstash plugin registers mcp.upstash.com) - they already cover creating, listing, renaming, and deleting Redis databases, running Redis commands, usage stats, backups, Vector and Search indexes, QStash schedules and messages, Blob buckets, and Box, so call them directly. Use this skill for shell work the MCP does not do - a CI step or provisioning script that needs `upstash` commands with JSON output, piping results into other commands, `upstash auth login` and API-key setup, team and member management, changing plans, regions, TLS, eviction, auto-upgrade, or budgets, minting temporary S3 credentials for a Blob bucket, connecting a coding agent to Upstash with `upstash setup` (the Upstash plugin, or the remote MCP server plus skills), or when the user explicitly asks for the CLI or for managing Upstash without the console.
Routes Snowflake-related operations to Cortex Code CLI for specialized Snowflake expertise. Use when user asks about Snowflake databases, data warehouses, SQL queries on Snowflake, Cortex AI features, Snowpark, dynamic tables, data governance in Snowflake, Snowflake security, or mentions "Cortex" explicitly. Do NOT use for general programming, local file operations, non-Snowflake databases, web development, or infrastructure tasks unrelated to Snowflake.
Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to produce tabular data in Excel format.
Creates formatted Excel reports with openpyxl, parses complex xlsm financial models with stdlib zipfile+xml when openpyxl fails, and controls Excel windows on macOS via AppleScript. Reconstructs images or scans of paper forms as editable XLSX files with printable layouts.
Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.
Perform end-to-end Data Mask operations on a sandbox: configure a masking policy over PII, run the masking job, poll it to completion, report masked-record results, and abort an in-progress run. Use when the user needs to run, monitor, or cancel a Salesforce Data Mask job, mask PII/sandbox data, or work with DataMaskPolicy / DataMaskPolicyJobRun. TRIGGER when: user runs a data mask job, masks sandbox PII, polls masking status, reports masked records, or aborts a running mask. DO NOT TRIGGER when: writing anonymization Apex by hand (use platform-apex-generate), generating test data (use platform-data-manage), or deploying unrelated metadata (use platform-metadata-deploy)
Salesforce metadata generation and querying with 120-point scoring. TRIGGER when: user creates custom objects, fields, validation rules, or touches .object-meta.xml, .field-meta.xml, .profile-meta.xml files. DO NOT TRIGGER when: permission set analysis (use sf-permissions), deploying metadata (use sf-deploy), or Flow XML (use sf-flow).
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput or QPS', 'CPU is pegged / can't keep up with the request rate', 'one query is slow / p99 or tail latency too high', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', 'need to add capacity', 'large limit / pagination / scroll is slow', or 'only recent data matters / expiring old vectors / retention window'.
Queries, manages, and troubleshoots Apache Airflow using the `af` CLI. Use when working with anything related to Airflow—a DAG, a DAG run, a task log, an import or parse error, a broken DAG, or any Airflow operation. Covers listing and triggering DAGs, retrying runs, reading task logs, diagnosing failures, debugging import and parse errors, checking connections, variables and pools, exploring the REST API, and monitoring health (for example, “trigger a pipeline”, “retry a run”, “list connections”, “check Airflow health”, “why did my DAG fail”). This is the entry point that routes to sibling skills for authoring, testing, deploying, and migrating Airflow 2 to 3. Not for warehouse/SQL analytics on Airflow metadata tables (use analyzing-data); for deep root-cause reports use debugging-dags or airflow-investigation.
Prepares and launches nf-core/pacsomatic matched tumor-normal PacBio HiFi genomics workflows from unaligned BAM inputs. Supports samplesheet generation, pinned Nextflow launch artifacts, local checks, LSF/Slurm/PBS Pro/SGE launcher submission, and startup troubleshooting. Use for pacsomatic run preparation and execution, not general short-read somatic analysis or medical imaging PACS.
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are spatial text boxes, Markdown, page raster output, and local parsing with optional custom HTTP OCR.
SOQL query generation, optimization, and analysis with 100-point scoring. Trigger when the user writes, optimizes, or debugs SOQL/SOSL queries, touches .soql files, or asks about relationship queries, aggregates, or query performance. Do not trigger for bulk data operations (use sf-data), Apex DML logic (use sf-apex), or report/dashboard queries.
Guides Qdrant deployment selection. Use when someone asks “how to deploy Qdrant,” “Docker vs Cloud,” “local mode,” “embedded Qdrant,” “Qdrant EDGE,” “which deployment option,” “self-hosted vs cloud,” or “need lowest latency deployment.” Also use when choosing between deployment types for a new project
Score a LinkedIn post using real performance data. Pulls the user's own post history via Apify (or uses cached data) to identify what actually performs, then scores the draft against those patterns. Use this skill whenever the user says "score my post", "review my post", "rate this post", "give me feedback", "how good is this post", or pastes a LinkedIn post and asks for critique. Separates historical comparisons from an explicitly labelled editorial-only fallback. Designed for live scoring at events and everyday post review.
Query and analyze a Dynatrace tenant’s actual billing and usage data with DQL against dt.system.events — DPS consumption breakdown, cost-normalized spend ranking, included volume deduction, chargeback/showback, cost drivers, spending trends, cost investigation, metrics ingest optimization, query cost attribution, workflow total cost, entity-level cost drill-down (RUM, hosts, synthetic, K8s), and AI/LLM cost (AI Units, AI Function Standard Calls, AI-generated query consumption). Direct licensing/entitlement questions to documentation (not via DQL). Use only to query/analyze the tenant’s actual consumption. Do not use for conceptual questions about how DPS billing/pricing works or what units/weights/the rate card mean — see Dynatrace documentation. Do not use to make DQL queries faster or cheaper to run (query optimization, reducing scanned data/consumption per run, filter-early best practices) — that belongs to dt-dql-essentials. This skill measures consumption; it does not tune queries
Guides embedding model migration in Qdrant without downtime. Use when someone asks “how to switch embedding models,” “how to migrate vectors,” “how to update to a new model,” “zero-downtime model change,” “how to re-embed my data,” or “can I use two models at once.” Also use when upgrading model dimensions, switching providers, or A/B testing models.
Analyzes unit economics by product or service using PayPal merchant insights and QuickBooks cost data, benchmarks against inflation and cost changes, and shows pricing-scenario data (e.g. "a 5% increase historically correlates with ~3% volume drop"). Surfaces analysis only — does not recommend a price. Use when the user asks about raising prices, pricing, margin analysis, what to charge, whether costs are eating into profit, or how a price change might affect their business. Trigger even if the user doesn't say "margin" explicitly — phrases like "am I making enough?", "should I charge more?", or "my costs are going up" all call for this skill.
Find company and contact data. Turn your agent into a prospecting platform. Get contact information, roles, tech stacks, business events, website changes, and intent data. Build lead lists, research prospects, and identify talent. 150M+ companies, 800M+ professionals, 50+ data sources.
Suggest and validate semantic dictionary (SD) mappings for audit log integrations using raw vendor log payloads or live ingested events. Use when: mapping a vendor audit log feed, authentication logs, user activity logs to the Dynatrace SD; checking required semantic fields; proposing OpenPipeline processor extraction rules based on DQL; running runtime validation (fetches live logs by log.source, then applies static validation).
This skill should be used when the user asks to "make a bar chart race", "turn a CSV into a video", "build an animated data visualization", "create an animated statistics video", "animate a chart/graph", "make a number counter/ticker", "animate a ranking over time", or "batch-render one chart template across many datasets". Covers data-driven keyframing, value interpolation, rank transitions, counters, pacing, annotation, and template×data batch output.
Designs experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
Salesforce data operations with 130-point scoring. Trigger when the user creates test data, performs bulk import/export, uses sf data CLI commands, or needs data factory patterns for Apex tests. Do not trigger for SOQL query writing only (use sf-soql), Apex test execution (use sf-testing), or metadata deployment (use sf-deploy).
Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data — counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show me Z", "find customers", "what is the count").
Prepares bulk RNA-seq FASTQ, Salmon, STAR, or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly, and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.
Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Use when the user has a gene set (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways—plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dot plots/enrichment maps, and publication-ready tables. Use for “pathway analysis,” “enrichment analysis,” “GO enrichment,” “KEGG/Reactome pathways,” “GSEA,” “over-representation,” “functional annotation,” or “what pathways are my genes in”
Builds, runs, and debugs Nextflow DSL2 pipelines and nf-core workflows. Use for Nextflow, nf-core, .nf files, nextflow.config, processes/channels/operators, samplesheets, nf-test, modules/subworkflows, container and executor configuration, HPC/SLURM or cloud deployment, and failed or resumed pipeline runs
ecommerce-competitor-analyzer is an e-commerce competitor analysis Skill developed by buluslan (WeChat Official Account: 新西楼.AI). It helps break down competitors’ pricing, reviews, traffic structure, and differentiation opportunities, turning “what exactly makes competitors strong” into an actionable competitor analysis report. For more practical AI content on cross-border e-commerce, follow the WeChat Official Account「新西楼.AI」。 Multi-platform competitor analysis across Amazon / Temu / Shopee: scrapes product data and generates AI reports covering pricing, reviews, traffic structure and differentiation space. Use when you need to analyze competitor products, extract product insights, or batch analyze multiple product listings. Supports bulk processing with structured outputs including title, price, rating, reviews, and strategic analysis
Covers upgrading Qdrant server and SDKs without interrupting availability or losing data integrity. Use when someone asks 'how do I upgrade Qdrant', 'can I jump from 1.15 to 1.18', 'rolling upgrade without downtime', 'do I upgrade the client or the server first', 'which SDK version matches my server', 'what should I check before upgrading', or 'something changed after we upgraded'.
An LLM-powered daily stock analysis system. Supports intelligent analysis of watchlists for China A-shares, Hong Kong stocks, and US stocks, and generates decision dashboards and market review reports. Provides technical analysis (moving averages, MACD, RSI, deviation rate), trend assessment, and buy signal scoring. Can integrate with the market-data skill for more stable ETF data. Trigger phrases: stock analysis, analyze stocks, daily analysis, technical analysis.
Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-compliant data handling. Use when: "test data," "fixtures," "factories," "seed data," "synthetic data," "test database," "data anonymization." Not for: migration/integrity testing of the DB itself — use database-testing; environment provisioning and database branching strategy — use test-environments. Related: test-environments, database-testing, api-testing, unit-testing.
Lightpanda browser, a drop-in replacement for Chrome-based browsing in any AI agent — faster and lighter for tasks without graphical rendering, such as data retrieval. Use it via MCP server, CLI fetch, or CDP with Playwright/Puppeteer — or run/save automations as deterministic, token-free replay scripts (PandaScript) via its own agent mode.
Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio, Linkme, Amazon Shop). Use when the user asks to scrape, fetch, extract, search, or look up social media profiles, posts, videos, reels, comments, transcripts, followers, ads, hashtags, trending content, or engagement metrics from any social platform. Also use when user mentions ScrapeCreators, social media API, ad library, or creator data.
Create and manage Shopify products via the Admin GraphQL API or CSV import. Workflow: gather data, choose method, execute, verify. Use whenever the user wants to add products to Shopify, bulk-import a catalog from CSV/spreadsheet/URL, update variants or prices, manage inventory quantities, upload product images, or assign products to collections.
Use this skill whenever the user wants to create, customize, or troubleshoot G2 v5 chart visualizations. Triggers include: any mention of 'G2', 'antv g2', '@antv/g2', 'G2 chart', 'G2 可视化', or requests to produce charts like bar charts (柱状图), line charts (折线图), pie charts (饼图), scatter plots (散点图), area charts (面积图), heatmap (热力图), radar charts (雷达图), treemap (矩形树图), funnel charts (漏斗图), sankey diagrams (桑基图), gauge (仪表盘), wordcloud (词云), boxplot (箱线图), as well as G2-specific topics like encode channels, scale config, coordinate systems, transforms, interactions, themes, labels, and animations. Also use when debugging G2 rendering errors, V4→V5 migration issues, or chart type selection. Do NOT use for G6 graph/network visualization, X6 editor diagrams, or S2 pivot tables
Suggest and validate semantic dictionary (SD) mappings for new security integrations using vendor API samples or live events. Use when mapping new security vendor data to Dynatrace SD; checking required fields; validating namespaces; highlighting discrepancies vs. the semantic dictionary; proposing mapping improvements; or running runtime validation against live tenant data.
Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
Use when the user asks to "analyze backlinks", "analyze my off-site signals", or "track AI traffic / ChatGPT / Perplexity referrals"; profiles referring domains, anchor-text mix, toxic links, and disavow candidates (backlinks mode) or isolates AI-assistant referral sessions in GA4/GSC/logs and reports their trend, landing pages, and AI-vs-organic conversion (ai-referrals mode). Not for internal links — use site-structure-optimizer; not for keyword positions — use rank-tracker; not for multi-metric stakeholder reports — use performance-monitor. 外链分析/反向链接/AI流量/AI引荐/ChatGPT流量/AI转化
A full-featured intelligent stock monitoring and alert system. Supports seven alert rules, including cost percentage, moving-average golden and death crosses, RSI overbought and oversold levels, unusual trading volume, price gaps, and dynamic take-profit. Designed for Chinese investors (red for rises, green for falls).
Use when the user wants to mine comments and replies from public social posts and videos for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights.
NeoData Financial Search — a general-purpose financial data search service using natural language. Query a wide range of financial data, including stocks, funds, indices, sectors, macroeconomics, foreign exchange, and commodities, as well as market quotes, financial statements, fund flows, research ratings, and event announcements. Supports retrieval from both structured API data and financial articles, with immediate answers. Use when the user asks about financial data, stock quotes, financial statements, earnings reports, market data, fund info, macroeconomics, forex, commodities, or needs to query the NeoData API.
Aggregates PayPal disputes, HubSpot feedback and tickets, and email sentiment (plus pasted or exported Google/Yelp reviews) into a themes report with verbatim evidence and a "do these three things this week" list. Use when the user asks how customers are feeling, for review analysis, what people are saying, or about disputes.
Analyze unit economics for PE targets — ARR cohorts, LTV/CAC, net retention, payback periods, revenue quality, and margin waterfall. Essential for software/SaaS, recurring revenue, and subscription businesses. Use when evaluating revenue quality, building a cohort analysis, or assessing customer economics. Triggers on "unit economics", "cohort analysis", "ARR analysis", "LTV CAC", "net retention", "revenue quality", or "customer economics".
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.