Data & Analytics

awslabs Bundle

Use when building or modifying a Python app that uses the agent-squad Python package — async multi-agent orchestration for Python 3.11+: orchestrator, agents (BedrockLLMAgent, AnthropicAgent, OpenAIAgent, SupervisorAgent, GroundedAgent, ChainAgent, and more), classifier routing (Bedrock, Anthropic, OpenAI), storage (in-memory, DynamoDB, SQL/Turso), retrievers (Amazon KB, Dakera), tools (AgentTools, MCPToolProvider), and custom implementations.

awslabs Bundle

Use when building or modifying a Node.js / TypeScript app that uses the agent-squad npm package — multi-agent orchestration: orchestrator, agents (all built-in types + GroundedAgent), classifier routing (Bedrock / Anthropic / OpenAI), storage (in-memory / DynamoDB / SQL), retrievers (Amazon KB / Dakera), and tools (AgentTools + MCPToolProvider).

nibzard Bundle

Reconstruct what a Steel customer organization was actually doing in its browser sessions, and whether it succeeded, from warehouse records plus raw session logs in object storage. Produces an evidence-backed report: inferred tasks, per-session outcomes, what blocked them, agent-quality problems, and what Steel itself cost them. Use this skill whenever someone gives you a Steel organization id (a bare UUID counts) and asks what that org was doing, what they were building, whether their sessions worked, why they failed, why they churned, whether they hit bot walls or CAPTCHAs, what their agent was trying to automate, or asks to investigate / explore / profile / dig into / look at an org, customer, account, or user. Also use it for questions like "did this session succeed", "was a human driving this", "which sites are failing for this customer", or "why does this org look unhealthy" — even when they do not say the words "org recon" or name this skill.

ameen-alam Bundle

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

teamtinvio Bundle

Use this skill when answering ad-hoc data questions that aren't covered by download_export (canonical reports — anomaly, audit, aging, P&L, BS, GL, statement of account) or search_* tools (entity listings with structured filters). Pseudo-SQL is a read-only DSL against Jaz's curated reporting schema — single SELECT statement, ≤100 row sync preview or full async CSV export. Tools: get_pseudo_sql_schema (live catalog, optional `table` filter), get_pseudo_sql_syntax (the static syntax guide), downloadable jaz-pseudo-sql.md skill body), preview_pseudo_sql, export_pseudo_sql, get_pseudo_sql_export, run_pseudo_sql_and_download.

zenml-io Bundle

Kitaru just recipes, CLI structure and structured-output contract, analytics events, and PR-description conventions. Use when running project commands, adding CLI commands or analytics events, or writing a PR description.

zerx-lab Skill

ZKit 后端开发规约(Go + connectRPC + GORM)。当新增/修改 RPC、实现 service handler、写数据访问、用 GORM 泛型 API、自定义 querier SQL、跑 codegen 时使用。Keywords: connectRPC, handler, service, GORM, gorm.G, 泛型, Count, FirstOrCreate, nilaway, getter, querier, 自定义 SQL, deleted_at, 软删, convert, proto, task gen, buf, 代码生成, JS 保留字, 后端, backend, Go

zerx-lab Skill

ZKit 认证与安全机制(JWT/会话/刷新/验证码/防爆破/审计日志/文件上传/对象存储)。当处理登录注册、token 刷新、会话管理、登录限流、操作日志、上传、存储 driver 时使用。Keywords: JWT, access token, refresh token, 会话, session, jti, 单点登录, AUTH_SINGLE_SESSION, 验证码, captcha, 防爆破, ratelimit, 锁定, 审计, 操作日志, OperationLog, login_logs, 上传, upload, 对象存储, storage, local, s3, minio, password policy, password reset, SMTP, mailer, TOTP, 2FA, MFA, export, import, xlsx, OpenAPI, docs, cron, job, scheduler, readyz, 安全, security, 认证, auth

vo1ganin Bundle

Expert assistant for querying and analyzing on-chain blockchain data using Dune Analytics. Use whenever the user wants to: analyze blockchain/on-chain data, write SQL for Dune, find wallet activity, track DeFi protocol metrics, analyze NFT trades, monitor token transfers, investigate smart contract events, check token prices on-chain, find top holders, measure DEX volume, or explore any crypto/web3 data. Trigger even if the user doesn't mention "Dune" explicitly — if they want blockchain data insights or on-chain analysis, this skill applies. Also trigger when the user provides a contract address and wants data for it. Enforces credit budget rules (500 warn / 700 block) and free-vs-paid key separation.

vo1ganin Bundle

Expert assistant for Nansen API — Smart Money analytics, wallet profiling, token intelligence across 37 blockchains (EVM + Solana + Bitcoin + Hyperliquid + more). Use this skill whenever the user wants to: find what smart money / funds are buying/selling, analyze a wallet's PnL, trade history, or counterparties, screen tokens with Nansen Score, track holders / flows / DEX trades on a specific token, research smart money on Hyperliquid perpetuals, follow Jupiter DCAs, or cross-chain institutional wallet research. Trigger even without "Nansen" keyword — any request involving "smart money", "what funds are doing", "wallet PnL analysis", "Nansen Score", "institutional flows" applies here. Enforces credit budget awareness (1 vs 5 vs 150 credit endpoints differ by 150×) and multi-key / x402 fallback.

adithya-s-k Bundle

Turns a user's plain-English description of an RL training environment into runnable code across the four target frameworks — OpenEnv, OpenReward (ORS), Verifiers, and NeMo Gym. Use whenever someone describes an environment they want to build ("I want to train an agent that does X", "make an env where the model has to Y"), asks to scaffold a new env, asks to port an existing env to one of these frameworks, or asks how to design tools/rewards/state for a new env. Use even when the user does not explicitly say "RL environment" — descriptions like "agent that browses the web", "tool-calling agent for SQL", or "game-playing agent" all qualify. Drives the full flow — clarifying interview, env-name selection, shared-domain extraction, per-framework implementation, and rollout-based smoke tests.

cookjohn Bundle

Export WoS records to Zotero, RIS, BibTeX, or Excel.

deepnote Skill

Use when reading, reviewing, creating, editing, or reordering Deepnote notebooks and blocks through the Deepnote MCP server: notebook structure, inputs, SQL, Python, and outputs, creating projects or notebooks, and adding, updating, scaffolding, or moving blocks.

deepnote Skill

Use for workspace-level questions about Deepnote: workspace summary, heartbeat, overview, inventory of projects and notebooks, scheduled or recently run notebooks, integrations, cached table and column structure, and which projects, notebooks, or SQL blocks use an integration.

archive228 Skill

Review SQL and ORM queries for correctness, safety, and performance before they ship.

cdatasoftware Skill

Use when the user wants to connect to, query, or explore a data source through a CData driver via the command line. This includes: creating or testing a connection to an enterprise system (Salesforce, NetSuite, Snowflake, SQL Server, Jira, etc.); running SQL queries against connected data; exploring schema (listing tables, columns, or stored procedures); executing source-specific stored procedures; and downloading, installing, or activating CData drivers. Acts as the prerequisite foundation for all driver-level cdata- skills (cdata-salesforce, cdata-airtable, etc.) that add source-specific guidance.

fellipeutaka Bundle

Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).

openmoss Bundle

Run SGLang auto benchmark searches with tiered server-flag sweeps, canonical dataset preparation, ShareGPT auto-download, custom-data conversion/validation, SLA or fixed-QPS benchmarking, CSV export, and optional second-stage speculative/EAGLE tuning. Use when the user wants an AI-operated benchmark workflow rather than a one-off bench_serving command.

songsunny00 Bundle

Use when converting wiki-exported requirement documents (containing docx, xlsx, md, images) into standardized templates (functional requirement docs + FAQ docs), with a gaps checklist for product managers to supplement missing information.

stagewise-io Bundle

Complete guide for the PostHog plugin — REST API access for querying analytics with HogQL, managing feature flags, inspecting events and persons, reading insights, experiments, cohorts, surveys, and more.

stagewise-io Bundle

Complete guide for the Supabase plugin — Management API access for running SQL queries, listing projects, managing edge functions, secrets, migrations, and inspecting project health.

jchaselubitz Bundle

NEVER change the seed.sql file directly. Always use the seed.ts file to make changes to the seed data.

justvinhhere Bundle

Use when writing, reviewing, or optimizing BigQuery SQL, asking about BigQuery best practices, working with .sql files targeting BigQuery, or troubleshooting slow/expensive BigQuery queries. Symptoms: high slot consumption, full table scans, expensive joins, slow queries, high bytes billed.

justvinhhere Bundle

Use when generating BigQuery SQL from natural language descriptions, converting queries from other SQL dialects to BigQuery, writing new BigQuery queries from scratch, or when the user describes what data they need and expects SQL output. Triggers on: "write me a query", "generate SQL", "how do I query", "convert this to BigQuery", "I need to get data from", "create a query".

lamm-mit Bundle

Run simple dimensionless 1D Euler-Bernoulli beam mechanics with selectable boundary conditions, point forces, distributed loads, point moments, springs, plots, CSV/JSON results, and deformation GIFs. Use when asked to analyze a simple beam, cantilever, fixed-fixed beam, overhang, support reactions, shear/moment/deflection plots, or a small educational beam mechanics movie/artifact.

lamm-mit Bundle

Run parameterized 2D lattice fracture simulations from the Fracture and Mechanics notebook as offline artifacts. Use when asked to simulate a pre-cracked triangular lattice, Mode I tension, Mode II shear, Morse/Lennard-Jones/MLIP pair potentials, temperature effects, strain-rate effects, crack propagation, stress-strain curves, or fracture movies. Produces an animated movie, stress-strain plot, final lattice image, CSV data, JSON run metrics, and README by running a bundled Python script.

oceanbase Bundle

Use seekdb-cli to interact with seekdb/OceanBase databases via shell commands. Use when: (1) querying databases with SQL, (2) exploring table schemas and structure, (3) profiling table data distributions, (4) inferring table relationships, (5) managing vector collections and semantic search, (6) adding/exporting collection data, (7) managing AI models , (8) checking database connection status, or (9) performing any database operation via command line.

oceanbase Bundle

seekdb database documentation lookup. Use when users ask about seekdb features, SQL syntax, vector search, hybrid search, integrations, deployment, or any seekdb-related topics. Automatically locates relevant docs via catalog-based semantic search.

oceanbase Bundle

Query and export data from seekdb vector database. Supports two search modes: (1) Scalar search - metadata filtering only, (2) Hybrid search - fulltext + semantic search combined. The --query-text parameter is used for BOTH fulltext ($contains) and semantic (query_texts) search simultaneously. Can export results to CSV/Excel.

oceanbase Bundle

Import CSV or Excel files into seekdb vector database and manage collections. Supports automatic vectorization of specified columns using embedding functions. When users need to: (1) Read and preview Excel files, (2) Import CSV/Excel data into seekdb, (3) Create vector collections from tabular data, (4) Vectorize specific text columns for semantic search, (5) Batch insert product/document data with embeddings, (6) Delete collections, or (7) Access sample data files (sample_products.csv/xlsx) for testing - IMPORTANT: sample files are located in this skill's example-data/ directory, you MUST read this skill file first to get the correct path.

otrf Bundle

Define a focused hunt hypothesis by synthesizing completed system internals and adversary tradecraft research. Use this skill after research has been completed to narrow a high-level hunt topic into a single, concrete attack pattern with clear investigative intent. This skill produces a structured, testable hypothesis and should be used before selecting data sources, defining environment scope, or developing analytics.

otrf Bundle

Generate query-agnostic analytics that model adversary behavior by translating hunt investigative intent into analytic definitions grounded in schema semantics. This skill is used to define how behavior should manifest in data before query execution or validation, and works best when informed by system internals, adversary tradecraft, a structured hunt focus, and suggested data sources.

otrf Bundle

Assemble a complete hunt blueprint by consolidating outputs from prior hunt planning skills into a single, structured plan for execution. Use this skill after system and tradecraft research, hunt focus definition, data source identification, and analytics generation have been completed. This skill is synthesis and packaging only and must not introduce new research, assumptions, or analytics.

otrf Bundle

Identify relevant security data sources that could capture the behavior defined in a structured hunt hypothesis. Use this skill after the hunt focus has been defined to translate investigative intent into candidate telemetry sources using existing platform catalogs. This skill supports hunt planning by reasoning over available schemas and metadata before analytics development or query execution.

rerun-io Skill

Use when the user is writing datafusion-python (Apache DataFusion Python bindings) DataFrame or SQL code. Covers imports, data loading, DataFrame operations, expression building, SQL-to-DataFrame mappings, idiomatic patterns, and common pitfalls.

owid Skill

Access Our World In Data from Python with the owid-catalog library: load chart data, catalog tables or individual indicators as pandas DataFrames that carry their own units, descriptions, sources and citations. Use this skill whenever the work happens in Python or a notebook (pandas, a uv script, matplotlib, parquet); whenever you need an indicator's metadata, units or codebook; whenever you need dimensions that published charts flatten away, such as sex, age group or projection variant; or whenever you need to search OWID's full catalog of indicators and tables, including semantic search by meaning, rather than only its published charts. Prefer it over the HTTP-based search-charts and fetch-chart-data skills for any Python-based analysis.