Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Diagnose why a product metric changed (dropped, spiked, or plateaued) by orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries. Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis for a trend, funnel, retention, stickiness, or lifecycle metric.
Use when working with multi-item data, batches, paginated APIs, rate-limited APIs, fan-out across multiple branches, anything that needs to "do this for each", or any time the user mentions looping, iterating, batching, paging, parallelism, or "loop over items". Triggers on "loop", "iterate", "for each", "batch", "page through", "paginate", "rate limit", "process all", "fan-out", "parallel branches", "concurrency", or any node that should run once vs once-per-item.
Compares the performance of two or more audience segments across key metrics side by side. Use this skill when someone wants to compare audiences or visitor groups — for example, "how do mobile visitors compare to desktop on conversion," "compare new vs. returning visitors," "show me the difference between these two segments," "compare these audiences on our KPIs," or "which segment performs better." Also trigger for "segment comparison" or "audience comparison."
Analyzes a multi-step conversion funnel to find where users drop off and which steps have the worst leakage. Use this skill when someone describes a journey or funnel and asks about conversion rates, drop-off, fallout, or step completion. Trigger for phrases like "analyze our onboarding funnel," "where are users dropping off," "what's our checkout conversion rate," "funnel analysis," "show me fallout between these steps," or "which step loses the most users."
Compares the performance of two or more audience segments across key metrics side by side. Use this skill when someone wants to compare audiences, cohorts, or groups — for example, "how do mobile users compare to desktop users on conversion," "compare new vs. returning visitors," "show me the difference between these two segments," "compare these audiences on our KPIs," or "which segment performs better." Also trigger for "segment comparison," "audience comparison," or "cohort comparison."
Audits Cloud Logging costs for a Google Cloud project: charges for log entries written to log buckets, the services, log streams, and severities that drive them, and the log sinks that route them. Reconciles Cloud Billing export costs with Cloud Monitoring volume metrics, and generates a FinOps report with charts and copy-paste exclusion filters. Use when auditing Cloud Logging spend, finding logging waste, identifying top logging contributors, generating log cost reduction reports, or creating log sink exclusion filters. Don't use for querying log entries (use cloud-logging-query-generation), configuring Cloud Monitoring alerts, or querying metrics/traces.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Amazon store Feed batch data submission and processing status management. Use to create Feed documents, upload Feed content to presigned URLs, submit batch files such as inventory or Listings, query or list Feeds, retrieve result documents, and cancel Feeds. Trigger when the user mentions Amazon Feed, batch inventory or product updates, submitting a Listing Feed, POST_FLAT_FILE, feedType, feedDocumentId, feedId, Feed processing status, Feed results, canceling Feeds, or Feeds API. Even if they don't explicitly say “Feed,” trigger this skill whenever they want to submit structured product or inventory data in batches through Amazon SP-API; for individual Listing CRUD, use linkfox-amazon-store-listings.
Use this skill when the user designs a "message queue", reaches for "Kafka", "RabbitMQ", "SQS", "Kinesis", "pub/sub", or "event-driven" architecture, asks about "async processing", "background jobs", "stream processing", or wrestles with "exactly-once vs at-least-once", "delivery guarantees", "message ordering", "duplicate handling / dedup", "dead letter queue", "backpressure", "saga orchestration", or "durable workflow". Use it whenever a slow or spiky operation should move off the request path, or two services must be decoupled, even if the user doesn't say "queue".
Instrument a Backstage frontend plugin with analytics events using the Backstage Analytics API. Use this skill when adding, reviewing, or extending event capture (`captureEvent`, `AnalyticsContext`) in plugin components, deciding whether an interaction warrants an event, or writing tests for analytics behavior.
Single-cell RNA-seq analysis with scanpy/anndata—h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.
Identifies which items (pages, campaigns, products, channels, regions) had the biggest increases or decreases for a key metric between two time periods. Use this skill when someone asks "what's up and what's down," "which campaigns moved the most," "top gainers and losers," "what pages are trending," "show me what changed by channel," or any variation of identifying the biggest movers and decliners for a metric.
Social media and web scraping using Apify actors. Use this skill when scraping Twitter/X tweets, Reddit posts, LinkedIn posts, Instagram profiles/posts/reels, Facebook pages/posts/groups, TikTok videos, YouTube content, Google Maps businesses/reviews, contact enrichment (emails/phones from websites), or when auto-detecting URL type to scrape. Triggers on requests to scrape social media, get trending posts, extract business info, find contact details, or extract content from social URLs
Identifies which items (pages, campaigns, products, channels, regions) had the biggest increases or decreases for a key metric between two time periods. Use this skill when someone asks "what's up and what's down," "which campaigns moved the most," "top gainers and losers," "what pages are trending," "show me what changed by channel," or any variation of identifying the biggest movers and decliners for a metric.
Insights into buyer feedback on Amazon products and categories. Query review topics, topic mention counts, star rating impact, review trends, return topics, and return trends by ASIN or browse node to analyze product reputation and category issues. Trigger when users mention Amazon review analysis, review topics, reasons for negative reviews, review trends, return reasons, return trends, ASIN reputation insights, category feedback, MENTIONS, STAR_RATING_IMPACT, or Customer Feedback API. Even without explicitly mentioning the API, trigger this skill when users want to identify common positive or negative reviews or return issues for an Amazon product or category from aggregated feedback.
Audits dbt documentation coverage and drafts missing model/column descriptions in the project's own house style, one folder at a time, for human review. Use when documenting undocumented models, backfilling missing YAML descriptions, auditing doc coverage, or keeping schema YAML in sync with model SQL — especially on multi-contributor projects where new models routinely land undocumented.
Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.
Generates a polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document. Always use this skill when someone asks for an executive summary, performance briefing, leadership readout, stakeholder update, or business review — even if they don't say "executive" explicitly. Trigger phrases include: "write a summary of last week's performance," "create a briefing for our leadership team," "produce a monthly business review," "what should I tell executives about our metrics," "generate a performance narrative," "QBR summary," "weekly business review," "board update," "stakeholder briefing," "how did we do last week," or "give me a performance snapshot." When in doubt, use this skill — it is always better to produce a polished briefing than a raw data dump.
Produces a compact KPI digest showing how key metrics changed over a period and what's driving the movement. Use this skill when someone asks for a performance summary, a weekly recap, a morning briefing, a KPI update, or any variation of "how did we do this week/month." Also trigger for requests like "give me a performance overview," "what moved in the last 7 days," "pull our KPI report," or "summarize our metrics."
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM)
Use when evaluating the performance of individual Xiaohongshu posts, identifying what makes content succeed or fail, extracting viral content patterns, recognizing underperforming content that needs optimization, or comparing performance across different content types and formats
Drives Astronomer's Otto agent (`astro otto`) as a delegated sub-agent for Airflow, dbt, and data-engineering work. Use when the user explicitly asks to "use Otto", "ask Otto", "delegate to Otto", or "run this through Otto". Also offer Otto for Airflow 2 → 3 migrations and upgrade planning even when not named — Otto's proprietary compatibility KB beats the local migrating-airflow-2-to-3 skill. Becomes the default path for any Airflow/data-engineering task when sibling Astronomer skills (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, etc.) are NOT loaded in the current session. Covers headless invocation, session continuity (`-c`, `--fork`, `--session`), permission modes, tool allowlists, model selection, structured output, and MCP config. **Do not load this skill if you are Otto** — Otto must not delegate to itself.
Reference skill for CDF Data Modeling API best practices. Covers concurrency limits (avoiding 429s), pagination patterns for instances.list and instances.query, batching write operations, search vs filter guidance, and the QueuedTaskRunner (Semaphore) utility for controlling concurrent requests. Triggers: DMS limits, 429 error, rate limit, pagination, cursor, nextCursor, batching, semaphore, QueuedTaskRunner, cdfTaskRunner, instances.search, instances.list, instances.query, instances.upsert, concurrency, deadlock.
Analyze Google Takeout exports of Gemini conversation history. Use when the user mentions Gemini takeout, Gemini export, Gemini history, Gemini conversation analysis, Google Takeout zip analysis, or drags a takeout zip into the project. Also use when the user asks to "analyze my Gemini data", "what did I talk to Gemini about", or wants to extract insights from Gemini chat logs.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations
World-class alternative data and sentiment analysis for trading—social media, news, on-chain data, and positioning. Extract alpha from information others miss. Use when “sentiment, alternative data, social media trading, news trading, twitter signals, on-chain, whale watching, fear greed, positioning” are mentioned.
Use when you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations; Review JdbcClient usage in Spring Framework 7. Part of Plinth Toolkit
Use when you need data access with Quarkus Hibernate ORM Panache — including PanacheEntity / PanacheEntityBase, PanacheRepository, named queries, JPQL, native SQL, DTO projections (project(Class)), pagination (Page.of()), N+1 avoidance (JOIN FETCH), optimistic locking (@Version / OptimisticLockException), @NamedQuery for validated reusable queries, transactions, @TestTransaction for test isolation, and immutable-friendly patterns. This is the Quarkus analogue to Spring Data for relational persistence. This should trigger for requests such as Review Panache entities or repositories in Quarkus; Improve Hibernate ORM data access with Panache; Add DTO projections, JOIN FETCH, pagination, or optimistic locking to Panache queries; Fix N+1 query problems or add @Version concurrency control in Quarkus Panache; Improve Panache active record versus repository design. Part of Plinth Toolkit
Understand images with Alibaba Cloud Model Studio Qwen VL models (qwen3-vl-plus/qwen3-vl-flash and latest aliases). Use when building image Q&A, visual analysis, OCR-like extraction, chart/table reading, or screenshot understanding workflows.
Internal sub-skill for job-hunt suite. Parses job description information from user-provided screenshots of any job platform (Boss直聘, 智联招聘, 前程无忧, 猎聘, etc.) and writes structured JD markdown files to jd-pool. Do NOT invoke directly — use the job-hunt main skill instead.
Use when handling files, images, attachments, or binary data in n8n, or when an AI agent needs to take a user-uploaded file as tool input or return a generated file. For Data Tables (schemas, dedup, persistent state), see the separate n8n-data-tables skill. Triggers on "file", "image", "PDF", "attachment", "binary", "upload", "download", chat trigger with files, agent tool that needs a file, vision/multimodal, or any handling of non-JSON file data.
Route web-scraping work into the lightest workable Scrapling mode instead of defaulting to a browser. Use when the user needs HTML extraction, JS-rendered page retrieval, protected-target escalation, quick CLI scraping, agent-facing MCP access, or a larger crawl with Scrapling spiders. Triggers on: scrapling, scrape website, crawl site, adaptive scraping, selector drift, stealthy fetch, browser scraping, scrape to markdown, scrapling mcp, scrapling spider, research harvesting, literature scraping, paper metadata.
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.
Conduktor platform expertise for Apache Kafka management, governance, and self-service. Covers Console (observe and manage), Gateway (enforce and proxy with interceptors), and CLI (operate and automate). Use when working with Conduktor configuration, deployment, Kafka data governance, encryption, multi-tenancy, or self-service workflows.
Trigger when: (1) User wants to extract text, tables, formulas, or structured data from images/PDFs/scanned documents, (2) User mentions "OCR", "文字识别", "文档解析", (3) User has a document (screenshot, scanned page, invoice, paper, whiteboard photo) and needs its content in structured form, (4) User asks to parse, digitize, or extract content from a visual document. Invokes the GLM-OCR SDK (pip install glmocr) to parse documents via Zhipu's cloud API. No GPU required. Returns structured JSON (regions with labels + bounding boxes) and Markdown. Agent can operate entirely via CLI — no YAML files needed. NOT for: real-time camera feeds, audio transcription, or non-document images (photos, illustrations).
Query OpenRouter's Benchmarks API for model benchmark rankings and scores. Use when the user asks for benchmark-backed model selection, model rankings by coding/intelligence/agentic ability, Artificial Analysis or Design Arena ELO/win-rate results, benchmark citations, or wants to call GET /api/v1/benchmarks. Also use alongside openrouter-models when the user asks what model should power an app, product, workflow, or use case and benchmark evidence could inform or rule out part of the recommendation, including creative writing, editing, coding, design, agentic, or intelligence-heavy apps. Do not use for OpenRouter usage analytics, billing/spend analysis, generation metadata, provider uptime/latency, generic model pricing/capability lookup without any selection or benchmark-relevance decision, or creating an evaluation suite for a local app.
Implement Syncfusion Blazor Linear Gauge (SfLinearGauge) for data visualization with linear scales. Use this when creating thermometer displays, progress indicators, or measurement visualizations. This skill covers axis configuration, pointers, ranges, and gauge appearance customization in Blazor applications.
Use when you need to use Spring Data JDBC with Java records — including entity design with records, repository pattern, immutable updates, aggregate relationships, custom queries, transaction management, and avoiding N+1 problems. This should trigger for requests such as Review Java code for Spring Data JDBC; Apply best practices for Spring Data JDBC in Java code; Model Spring Data JDBC aggregates with Java records; Review Spring Data JDBC repositories and aggregate boundaries; Improve optimistic locking or domain events in Spring Data JDBC. Part of Plinth Toolkit
Diagnose a Douyin account’s operational health in one click. Uses Honghu Data services to retrieve the account profile and recent post data, then evaluates six dimensions: basic account profile, content productivity, interaction health, content quality, content trends, and follower quality. Outputs an overall score out of 100, detailed scores by dimension, six types of risk alerts, and targeted optimization recommendations. Trigger phrases include “diagnose Douyin account,” “Douyin account analysis,” “Douyin account checkup,” “Douyin account assessment,” “view XX’s Douyin data,” and “analyze this Douyin account.”
A tool for crawling Douyin posts. Enter a Douyin name or ID to retrieve basic account information and a list of recent posts from the Honghu API-wide database (pagination supported, up to 50 per page). Use when users mention “crawl Douyin posts,” “Douyin post list,” “view Douyin videos,” “collect Douyin content,” or “fetch Douyin posts.”
A crawler for searching WeChat Official Account articles by keyword. Displays results in a terminal table (title/author/reads/likes/shares/favorites/publish time/article link), automatically exports CSV, and generates interactive HTML reports. Use when users need to search, crawl, or browse Official Account articles; also useful for tracking industry trends, analyzing competitor content, and gathering inspiration.
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.