Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Diagnoses live CockroachDB cluster performance issues by identifying long-running queries, busy sessions, and active transactions using SQL-only interfaces. Use when users report cluster slowness, high CPU, or need to find runaway queries and their source applications without DB Console access.
Solves a high-frequency practical pain point: after accumulating a large amount of traffic for a site in Yakit History, how to fetch all unique paths by domain in one go, obtain the login Cookie, consolidate those paths into a reusable Payload dictionary, and then send requests and scan/Fuzz path by path with the login state. Covers db.QueryHTTPFlowsByKeyword to fetch History, URI deduplication, Cookie extraction, db.SavePayload/YieldPayload dictionary management, and poc.HTTP request sending with login state. Use when the user wants to "extract data from historical traffic to drive subsequent scanning".
Yaklang database operations (db library). Use when the user needs to persist or query data in scripts/plugins: temporary SQLite table creation and CRUD (db.OpenTempSqliteDatabase / db.ScanResult / Exec), key-value storage for cross-script shared configuration (db.SetKey/GetKey/DelKey/SetKeyWithTTL), unified management of brute-force dictionaries (db.SavePayload/YieldPayload), and project-level configuration (db.SetProjectKey/GetProjectKey).
Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Use when the user requests analytics reporting or provides relevant inputs for this workflow.
Social-listening signals for tracked portfolio companies via Apify Actors — Reddit sentiment (fatihtahta), Twitter/X real-time mentions (kaitoeasyapi pay-per-result), Trustpilot service quality (getwally.net). Use when the user asks for sentiment, social media mentions, customer reviews, brand perception, crisis signals, OSINT, social listening, "what are people saying about X". Reads tracked companies from data/companies.json. Do NOT use for news (use apify-financial-news) or registry lookups (use apify-public-registries).
Create a RevOS Connection YAML that syncs data from a Source into the org's data warehouse. Use whenever the user asks to: add a connection, set up a sync, ingest a table, pipe data from a source, configure streams, pick which tables to ingest, or wire up a Source. The skill picks a sensible stream subset (especially important for databases — most projects don't want every table) and confirms the choice before writing YAML.
Create new dbt transformations (silver/gold models) in the RevOS dbt project. Use when asked to create a dbt model, build a transformation, add a new layer model, declare a raw source, or register a new raw table. Bronze is source-declarations only — no SQL files. Covers dbt project conventions, sources, materialization, schema.yml, and validation commands.
Guides creation of custom, interactive data visualizations with D3.js — bar/line/scatter charts, network diagrams, geographic maps, hierarchies, and real-time data updates with zoom/pan/brush interactions and animated transitions. Use when chart libraries (Highcharts, Chart.js) lack the customization needed and you require low-level control over data-driven DOM manipulation, scales, shapes, and layouts. Invoke when user mentions D3, d3.js, custom visualization, force-directed graph, or data-driven SVG.
Comprehensive data visualization skill covering visual execution and technical implementation. Includes perceptual foundations, chart selection, layout algorithms, and library guidance. Triggers on: charts, graphs, dashboards, 'visualize', 'plot', data presentation, D3, Recharts, Victory.
Charts, dashboards, and data visualizations using Recharts, Chart.js, and D3.js. Use when building charts, dashboards, or interactive data displays. Use for chart type selection, data storytelling, annotation patterns, responsive design, accessibility, and performance optimization.
Automatically discover database skills when working with SQL, PostgreSQL, MongoDB, Redis, database schema design, query optimization, migrations, connection pooling, ORMs, or database selection. Activates for database design, optimization, and implementation tasks.
Geohash encoding/decoding in TypeScript or Rust — bit interleaving, the base32 alphabet, precision and cell geometry, neighbour/adjacency computation, proximity and bounding-box queries, and geohash-backed spatial indexing. Also covers the "codebase as a navigable 2D map" pattern — projecting a codebase into a coordinate plane, geohashing it so prefixes become business-domain regions, and navigating it like Google Maps (zoom, tiles, level-of-detail, clustering, deep links). Trigger when implementing, reviewing, or debugging geohash work — even when the user does not say "geohash" but the work involves spatial hashing, Morton/Z-order codes, proximity search on lat/lon, or mapping and visualising code structure spatially. Contains 42 impact-ordered rules with TypeScript and Rust examples.
Huawei Cloud ModelArts training job fault diagnosis skill. Uses hcloud CLI to call ModelArts training job log/event APIs, analyzes training job failures/timeouts/stuck jobs, locates customer training code issues, and provides diagnosis conclusions with fix suggestions and confidence levels. Scenarios: training job failure (status.phase=Failed), timeout (Timeout), abnormal (Abnormal), stuck jobs. Triggers: training job failure, training job timeout, training job stuck, ModelArts training diagnosis, 训练任务失败排查, 训练作业异常分析.
Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mmap, async), data formats (Parquet vs CSV vs JSON, predicate pushdown), chunking & batching, spill-to-disk (external merge sort, DuckDB/Polars), pipelining (bounded queues, backpressure, checkpointing), codec selection (zstd/lz4/gzip), concurrency for I/O-bound workloads (asyncio, threads, prefetch), and observability (iowait vs CPU%, rows/sec, py-spy/strace). Trigger on "process a large file", "stream this", "out-of-core", "OOM kill", "this is slow", or code with `pd.read_csv` of multi-GB files, `requests.get(...).content` on big bodies, `BytesIO` on unbounded inputs, per-row INSERTs, sequential `requests.get` loops, falling `tqdm` rates — even if I/O or memory isn't mentioned. Complement to computer-science-algorithms.
Klaviyo marketing operations and analyst expertise. Audit flows, segments, campaigns, deliverability, and revenue attribution. Use when the user asks about Klaviyo marketing strategy, email/SMS automation, customer segmentation, flow optimization, or lifecycle marketing. For API integration, SDK, webhook, or developer questions, see the klaviyo-developer skill.
Midnight on-chain storage — ParityDB key-value backend, Patricia-Merkle trie state commitments, twoxhash storage keys, and how state transitions are persisted. Use when explaining state proofs, database layout, or trie performance on Midnight nodes.
Compatibility guide for developers moving from MySQL to MariaDB, and for developers with MySQL experience working with MariaDB. Use when migrating a MySQL application to MariaDB, when MySQL syntax or habits cause unexpected behavior in MariaDB, when asking about MySQL/MariaDB compatibility, or when adapting code written for MySQL to run on MariaDB. IMPORTANT — MariaDB diverges significantly from MySQL 8.0 and is not a simple drop-in replacement for modern MySQL. Authentication plugins, JSON handling, GTID replication, and several SQL features differ in important ways.
PostgreSQL 17/18+ performance tuning and optimization. Covers async I/O configuration, query plan forensics, index strategies, autovacuum tuning, vector search optimization, connection pooling, declarative partitioning, and practical query patterns. Use when diagnosing slow queries, configuring async I/O, tuning autovacuum, optimizing vector indexes, analyzing execution plans with EXPLAIN BUFFERS, configuring PgBouncer or connection pooling, setting up table partitioning, implementing cursor pagination, or optimizing queue processing.
Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
Design or critique an analytics or data dashboard — which metrics earn a place, how to organize and prioritize them, and which chart fits each one. Trigger when designing a dashboard, building an analytics or reporting screen, choosing chart types, or reviewing a dashboard for clarity and information overload.
Vercel KV (Redis-compatible key-value storage via Upstash). Use for Next.js caching, sessions, rate limiting, TTL data storage, or encountering KV_REST_API_URL errors, rate limit issues, JSON serialization errors. Provides strong consistency vs eventual consistency.
Generate a model-graph.png visualization of Cube.dev semantic models — render the cube graph, show relationships between cubes, draw the fact spine, or diagram the semantic layer. Use whenever the user mentions visualizing, drawing, diagramming, or graphing the semantic model / cube model / cube relationships, even if they don't explicitly ask for a PNG. Triggers: "visualize the semantic model", "draw the cube graph", "show relationships between cubes", "generate model-graph.png", "diagram the semantic layer", "render the cube model". Accepts an optional folder argument (defaults to `cubes/`).
Use when the user needs X (Twitter) data or confirmation-gated X actions through Xquik: tweet search, user lookup, follower extraction, media download, monitoring, webhooks, MCP, SDKs, posting, likes, DMs, and profile updates. Requires a Xquik API key. Never ask for X login material.
Keep local state correct against the Alpaca Broker API: idempotency keys, ID-keyed upserts, event snapshotting and deduplication, polling rails without events, windowed reconciliation/heal jobs, status state-machine mapping, and handling eventual consistency and corrections. Use when designing the data-correctness layer of any Alpaca integration in any language
Run a backtest for a saved strategy file and interpret the result. Strategy validation, backtest, hypothesis falsification, result interpretation, performance analysis, strategy test. Use when the user asks to validate an existing {data_root}/strategies/{slug}.json file, or immediately after receiving a create-strategy handoff payload (slug + hypothesis + falsification_criterion) that needs hypothesis validation.
Configures SQL audit logging on CockroachDB clusters to capture security-relevant events including authentication, privilege changes, and sensitive data access. Use when enabling audit logging for compliance, setting up role-based audit policies, or verifying audit configuration.
Cultural tourism Bilibili information source — search popular Bilibili videos related to cultural tourism, filter the hottest content by likes, and generate a polished HTML report. Use when the user needs cultural tourism on Bilibili, travel guides, scenic spot popularity, cultural tourism news, or viral cultural tourism content. Triggers: 文旅B站, 文旅信息源, 文旅资讯, 旅游B站, 景区视频, 文旅爆款.
Use when checking a radiology or medical AI study design before drafting or submission. Reviews the analysis unit, cohort logic, leakage risks, comparator, validation strategy and reporting-guideline fit. AI-vs-expert benchmarks are /design-ai-benchmarking.
Analyze dragon-tiger list data (institutional seats, brokerage branches, buy/sell flows, per-stock appearance statistics) to identify capital preferences and abnormal trading risk. Use when the user asks about the dragon-tiger list, institutional seat activity, hot-money brokerage branches, reasons for appearing on the list, or needs a dragon-tiger list review.
Community sentiment via Gate-News MCP, X/Twitter-first. Use for social discussion, KOL takes, or opinion on a coin or topic. Triggers: what does the community think about ETH, Twitter or X sentiment, what are people saying, KOL opinions. Reddit, Discord, Telegram when search_ugc is available; until then label output as X/Twitter-only. Tools: news_feed_search_x, news_feed_get_social_sentiment.
Manage Huawei Cloud ModelArts training jobs and related resources through full lifecycle operations via hcloud CLI. Covers 52 API interfaces across 8 functional domains: training job management, algorithm management, training job tags, training experiments, training job events, model import, auto search (hyperparameter tuning), and training image save. All write operations require user confirmation before execution. Triggers include: "ModelArts training", "训练作业", "模型训练", "创建训练作业", "查询训练作业", "停止训练作业", "删除训练作业", "算法管理", "超参配置", "training job", "training management", "create training", "ModelArts 训练", "训练实验", "自动搜索", "超参调优".
Knowledge base architecture selection, curation, and governance. Use when choosing between document-based (RAG), entity-based (graph), or hybrid KB architectures, establishing content curation workflows, implementing versioning and governance, or evaluating quality metrics. For building retrieval pipelines, use the rag-implementer skill. For building knowledge graphs, use the knowledge-graph-builder skill.
Analyze logs in OpenSearch using PPL and Query DSL. Use this skill when the user wants to query logs, analyze error patterns, discover log patterns, check error rates, perform anomaly detection on logs, or investigate application issues through log data. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, PPL, error rate, anomaly detection, log patterns, or log analytics without mentioning OpenSearch.
Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
Caching strategies in front of AWS OpenSearch (Elasticsearch) or AWS Personalize — search, recommenders, multi-recommender pages, anon vs logged-in traffic. Covers ROI decision (TPS/minProvisionedTPS, Zipf, amplification), key design (canonicalisation, cohort vs user, solution-version pinning, bucketing), personalisation boundary (anon/logged split, fan-out coalescing), strategies (cache-aside, refresh-ahead, write-through, batch precompute, L1+L2), TTL (volatility, soft/hard, jitter, event-driven invalidation), stampede protection (single-flight, XFetch, stale-while-revalidate, circuit breaker), observability (hit-rate, cost-per-1k, cardinality drift, log-replay), defensive caching (negative, Bloom filter), and tier composition (LRU, ElastiCache Redis, CloudFront, OpenSearch request/filter cache). Triggers on cache hit rate, Personalize throttling, stampede, single-flight, L1/L2, ElastiCache sizing. Complements opensearch-function-scoring-algorithms.
Manages planned cluster maintenance across all tiers. Self-Hosted covers node drain procedures for OS patching, hardware changes, and configuration updates. Advanced/BYOC covers maintenance window configuration, patch scheduling, deferral policies, and monitoring during CRL-managed maintenance. Standard and Basic maintenance is fully managed with no customer action. Use when planning maintenance, configuring maintenance windows, or preparing applications for maintenance events.
Writes fast, idiomatic Polars in Python using the lazy API, and answers natural-language questions about data in one shot. Use this skill whenever a task involves loading, querying, transforming, aggregating, joining, or analyzing tabular data (CSV, Parquet, NDJSON, or DataFrames); answering questions about top-k, trends, period-over-period, distributions, time series, or share-of-total; querying DataFrames or files with SQL; writing, debugging, or speeding up Polars code; or translating pandas to Polars. Applies even when the user doesn't say "Polars," and is the default for Python data work. Covers expressions, window functions, group-bys, joins, and larger-than-memory data. Not for Polars Cloud, On-Prem, distributed, or GPU workloads.
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.