Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use when you need systematic extraction of pain points, feature gaps, switching triggers, and opportunities from reviews (B2B review sites, app stores, forums, communities, issue trackers). Includes bias hygiene, taxonomy building, triangulation, and turning insights into experiments.
Verifies that an extracted statement is internally consistent by checking that opening_balance + sum(transactions) = closing_balance within a small tolerance, and produces a reconciliation report flagging missing rows, double-counted rows, sign errors, and rounding diffs. Use as the gate between PDF extraction and committing transactions to the data store, when reconciling a statement that does not balance, or when user mentions reconcile, balance check, or statement does not tie out.
Sample-size and statistical power calculations for planning studies. Use whenever 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 designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions 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.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
The community-developed free and open-source software package for solar physics. Provides tools for data search and download, coordinate transformations specific to solar physics, and powerful image processing through the Map object. Use when working with solar data, solar images (EUV, magnetograms, white light), solar coordinates (Helioprojective, Heliographic), Fido data search, solar time series, differential rotation, limb fitting, or multi-instrument solar analysis (AIA, HMI, GOES).
Implements Syncfusion WinUI Cartesian Charts (SfCartesianChart) for data visualization in WinUI applications. Use this when working with column, line, bar, area, or financial charts (OHLC, Candle). This skill covers axis configuration, legends, tooltips, zooming/panning, data labels, and high-performance fast series for large datasets.
Implement Syncfusion WinUI Funnel Chart (SfFunnelChart) for visualizing data across stages in a process or workflow. Use this when working with funnel charts, conversion funnels, sales pipelines, or process stage analysis in WinUI applications. This skill covers stage-based metrics, hierarchical data representation with decreasing values, and visual representation of progressive reduction in data values across sequential stages.
Implement Syncfusion WinUI Radial Gauge (SfRadialGauge) component for circular data visualization in desktop applications. Use this when working with radial gauges, speedometers, temperature monitors, or dashboard meters. This skill covers axis configuration, multiple pointer types (needle, shape, content, range), ranges with gradients, annotations, animations, and interactive dragging for WinUI/.NET desktop applications.
Build and run scoring models that tier raw company lists into campaign-ready segments, and point each scored data point at the ColdIQ endpoint that sources it. Use when building a scoring model, tiering a TAM, defining signal groups and point allocations, setting tier thresholds, cleaning a list before scoring, or running a Python scorer on an export. Triggers on "score companies", "tier the list", "scoring model", "TAM scoring", "ICP fit score", "tier thresholds", "100-point model", "qualify accounts". Do NOT use for enrichment/search to BUILD the list (see apollo-search / coldiq-search-enrich), signal sourcing (see signal-detection), or list dedup (see list-dedup).
Integrate Redis-compatible Vercel KV for caching, session management, and rate limiting in Next.js applications. Powered by Upstash with strong consistency and TTL support. Use when implementing cache strategies, storing temporary data with expiration, building rate limiters, or troubleshooting missing environment variables, serialization errors, or rate limit issues.
Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet spec', 'process capability', or 'Cpk calculation'.
Data layer reference for Android app development (Kotlin). Jetpack androidx. Room / Room 3 (androidx.room3) (Entity, Dao, Query, Migration, prepopulate, createFromAsset), DataStore (Preferences/Proto, Direct Boot), MediaStore, SharedPreferences, scoped storage, Retrofit, OkHttp.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
Use when working with @atscript/db, @atscript/db-{sqlite,postgres,mysql,mongo,memory}, @atscript/db-sql-tools, @atscript/moost-db, @atscript/db-client, or .as models with @db.* annotations. Covers DbSpace, adapter wiring, table/view CRUD, query filters, patch/field/array ops, relations, views, schema sync, engine-specific capabilities, BaseDbAdapter subclassing, moost-db REST routes, declarative actions, @InputForm structured input, URL query syntax, browser Client, client.action(), client.getActionForm(), DB validation, optimistic concurrency control via @db.column.version + $cas + withOptimisticRetry, field-level encryption at rest (@db.encrypted, key rotation, ENC_* errors), geo search (db.geoPoint, @db.index.geo, geoSearch, $geoWithin, GET /geo), token controller binding (provideDbSpace, @db.space), model manifest (atscriptModels), planSchema, assertExposed, provideTestDbSpace. Scope is DB only. Out of scope (use the moostjs/atscript skill): .as syntax, @meta.*, @expect.*, asc, unplugin, VSCode.
Strategic audience guidance -- helps users build the right audience for their business goal or improve an existing segment. Use when the user needs help choosing the right audience strategy, wants advice on segment design, or needs to improve an existing segment.
Guides privacy audit sampling methodology including statistical and non-statistical sampling, sample size determination, stratification techniques, attribute sampling for compliance testing, confidence level selection, tolerable deviation rates, and extrapolation of results to the population. Keywords: audit sampling, statistical sampling, attribute testing, sample size, confidence level, stratified sampling, privacy audit.
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
Deploy OpenSearch search applications to Amazon OpenSearch Service or Amazon OpenSearch Serverless. Use this skill when the user wants to provision an OpenSearch domain or serverless collection on AWS, deploy search configurations to AWS, set up Bedrock connectors, configure IAM roles for OpenSearch, migrate a local search setup to AWS, or manage Amazon OpenSearch infrastructure. Activate even if the user says AOS, AOSS, OpenSearch Service, serverless collection, Bedrock connector, SigV4, or AWS deployment without mentioning search.
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
Design and audit billing reconciliation across invoices, payments, ledgers, entitlements, app-store receipts, subscriptions, IAP, payouts, refunds, chargebacks, taxes, credits, adjustments, revenue recognition inputs, and support evidence. Use when money movement, customer access, or marketplace balances must match across systems.
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
Use when the user has the Bmob MCP server configured (http://mcp.bmobapp.com/mcp) and wants to perform LIVE operations against their Bmob backend cloud project from the IDE. Triggers: 'list bmob tables', 'show bmob schema', 'create bmob table', 'add a row to bmob', 'update bmob record', 'delete bmob data', '生成 bmob curl', 'design bmob schema', '上传云函数', '部署云函数', '发布云函数', '同步云函数', '同步函数', '拉取云函数', '下载云函数', '调用云函数', '执行云函数', 'invoke cloud function', 'run cloud function', '一键部署网站', '静态托管', '部署静态站点', 'deploy static site', 'deploy website'. Provides 12 tools: get_project_tables (ALWAYS call first to discover schema before any write), create_table, add_single_data, update_single_data, delete_single_data, generate_code, deploy_cloud_function, invoke_cloud_function, list_cloud_functions, get_cloud_function, deploy_static_site, mcp_endpoint_mcp_post. NOT for writing client/SDK code that will ship in the user's app — for that use bmob-database-{javascript,android,ios,swift,flutter,restful}. NOT for ACL / role design (use bmob-acl-and-roles) or cloud function development (use bmob-cloud-function-development).
Browserless cloud browser automation service. Run headless Chrome at scale for scraping, screenshots, and PDF generation. Use for cloud browser automation, scalable scraping, or headless Chrome as a service. Triggers on browserless, headless chrome, browser service, cloud scraping, screenshot api, pdf generation, chrome as a service.
Augur — a local-first investment research memory system: 18 investor personas analyze independently and form a weighted consensus. Evidence is time-aware, and every run is reviewable. CLI / Dashboard / MCP. A local-first research memory system with 18 investor personas.
ByteHouse data asset and lineage analysis Skill. Directly queries system.tables / system.columns via clickhouse-connect to pull table structures, fields, engines and other metadata for a given database, generate a data asset catalog, auto-tag it, and identify lineage relationships and column similarity between Distributed/Local. Use this Skill when the user mentions "ByteHouse data assets", "data asset inventory", "table structure analysis", "lineage analysis", "field analysis", "table engine distribution", "metadata catalog", and the like.
ByteHouse multimodal search skill, supporting vectorized storage and multimodal hybrid retrieval of text, images and video (vector search, text-to-image search, image-to-text search, hybrid keyword + vector search). Use this skill when the user mentions "ByteHouse vector search", "multimodal search", "hybrid search", "text-to-image search", "image-to-image search", "text-to-video search", "multimodal knowledge base", "HNSW / vector index" and similar needs.
Guides developers through integrating and using Viking SDK, covering installation, authentication, API calls, and troubleshooting for the Viking vector database, knowledge base, and memory store. Use when users need to develop business code with Viking SDK or ask questions about Viking.
Entrypoint for managing Volcengine Flink (based on Apache Flink) workloads. Use it for developing and deploying streaming and batch jobs, diagnosing real-time issues (e.g., backpressure, checkpoint failures, OOM), troubleshooting resources, and performing SRE actions (stop/start/restart/scale/config changes). It intelligently routes requests to four specialized sub-skills and leverages flink-mcp for tool execution.
Produce a long-form, shareable markdown writeup on whether Claude has regressed on this user's work. A bundled Python script scans `~/.claude/projects/`, computes every metric, and renders a markdown skeleton with tables already filled — in ~2.5s. Claude fills a dozen short narrative placeholders and saves. Writes `./cc-canary-<YYYY-MM-DD>.md` suitable for pasting into a GitHub issue or gist.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.
Build an interactive competitive-traffic report for any company and its rivals—monthly visits (SimilarWeb), organic search traffic and Domain Rating (Ahrefs)—as one self-contained HTML page with ranked charts, a three-month trend, a multi-year overlay and a data table. Use when asked to benchmark a product against competitors, size up a market, answer “how much traffic does X get compared to Y”, build a competitive landscape, or produce a traffic/authority comparison deck or page.
Build and deploy the Goldsky Compose corporate-actions / dividend-distribution example under the user's own account — a durable, idempotent distributor that pays N token holders pro-rata for a tokenized corporate action (dividend, coupon, rebate, airdrop) with an on-chain audit trail. The interesting bit: Compose orchestrates a Goldsky Turbo job-mode pipeline as an ephemeral subroutine — declaring a campaign spawns a one-shot pipeline that snapshots share-token holders at a record block, waits for it, pays each holder via a gas-sponsored wallet, then deletes the pipeline. Triggers on: 'dividend distribution', 'pay dividends onchain', 'distribute dividends to shareholders', 'corporate actions distributor', 'pro-rata payout to token holders', 'airdrop pro-rata by balance', 'cap table distribution', 'set up / deploy the dividend / corporate-actions example'. Ships pointed at shared permissionless demo contracts on Base Sepolia so there's nothing to deploy. Deploys fully in-app or via the CLI; the only runtime requirement is a `GOLDSKY_PROJECT_KEY` secret, set as the last step. For a custom/novel Compose app, use /compose. For debugging a deployed app, use /compose-doctor. For manifest/CLI/API field lookups, use /compose-reference.
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
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.