Data & Analytics

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Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

gabrielmoreira Skill

Cisco Meraki monitoring and diagnostics (read-only) — device availability, loss and latency history, uplink status, live tool results, configuration change log, API request analytics via Cisco's official Meraki MCP. Use when checking Meraki device health, investigating uplink loss, or reviewing recent configuration changes

gabrielmoreira Skill

F5 BIG-IP platform operations via pyATS iControl REST — system, networking, HA/CM, auth, analytics, security, APM, live-update, ADC certs, file management. Use when checking BIG-IP system health, verifying HA sync status, auditing certificates, or inspecting F5 platform resources.

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🛒 E-commerce & Retail skill suite derived from VoltAgent/awesome-agent-skills. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.

gabrielmoreira Skill

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink.

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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.

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Publish and schedule content to 15 social platforms through one Upload-Post API call: TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, Pinterest, Bluesky, Reddit, Discord, Telegram, Mastodon, WordPress, and Google Business Profile. Use when the user wants to post or schedule a video, photo carousel, text, or document across several platforms at once, check upload status, pull analytics, or run Instagram comment-to-DM funnels.

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Load CSV and JSON files into Elasticsearch indices using the bulk API and explicit mappings when field types matter. Use when batch-importing local files, converting CSV rows or JSON arrays to NDJSON bulk format, or verifying document counts and mappings after ingest — not for Logstash pipelines, Beats, custom scripts, or index-to-index reindex.

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Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. This should trigger for requests such as Review JDBC or SQL data access in a Micronaut project; Improve transactions and parameter binding for Micronaut JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix self-invocation bypassing @Transactional in Micronaut; Review Hikari or pooled datasource usage in Micronaut JDBC. Part of Plinth Toolkit

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Use when you need data access with Micronaut Data — @MappedEntity, CrudRepository/PageableRepository, @Query with parameters, @Transactional services, projections, @Version, and @MicronautTest with TestPropertyProvider and Testcontainers. For raw java.sql access without generated repositories, use @511-frameworks-micronaut-jdbc. This should trigger for requests such as Review or implement Micronaut Data repositories and entities; Add transactions, pagination, or projections in Micronaut persistence layer; Model Micronaut Data entities and repositories; Review Micronaut Data transactions and pageable queries; Improve projections or optimistic locking with Micronaut Data. Part of Plinth Toolkit

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Detect the market regime (Bull / Bear / Sideways) for ANY asset and turn it into a tradeable signal or a risk filter. Use this whenever the user wants regime detection, a regime-aware confirmation on an existing strategy, a regime risk gate, regime-based position sizing, a Markov transition matrix, n-step regime forecasting, a stationary regime mix, or a no-lookahead walk-forward regime backtest — on a ticker (via yfinance) or on the user's own CSV price series. Composes into any existing trading agent or strategy without rewriting it. Framework by Roan (@RohOnChain).

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Spec a dbt model — its grain, sources, transformations, tests, and materialization. Use when asked to design a dbt model, plan a data transformation, write a staging/intermediate/mart model spec, or define dbt tests for a table. Produces a model spec — purpose & grain, lineage (sources → refs), the transformation logic, column definitions, dbt tests, materialization choice, and the skeleton SQL/YAML.

gabrielmoreira Skill

Load before calling data-tables or parse-file. Use for natural standalone requests like "what data tables do I have?", "show/list my tables", or "what columns are in this table?", and whenever the user asks to list, show, create, inspect, import, seed, query, update, clean up, rename columns in, or delete data tables and rows, especially from CSV/XLSX/JSON attachments. Also load before building or planning workflows that create or write to Data Tables (then load workflow-builder before build-workflow).

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Build auditable financial workbooks headless via openpyxl.

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Build M&A accretion/dilution workbooks in Excel.

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Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard usage — cases where `system.insights` only returns the current project's rows and the agent would otherwise report the data as inaccessible. Covers the synced table names for US and EU and the column-verification workflow.

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Debug a customer's data warehouse source, schema, or table from a support ticket, using PostHog's own production data. Use when a ticket says a warehouse table is stale, empty, stuck, duplicated, missing rows, or failing to sync, and you need the real state of the sync rather than the customer's description. The customer is on a team your MCP session cannot reach, so every answer comes from execute-sql with a connectionId set to a direct-connect source over PostHog's live production databases, which hold all customers' data. Covers region detection (US vs EU), the Production Postgres connection (externaldatasource / externaldataschema / externaldatajob / datawarehousetable), the Production ClickHouse connection (log_entries, app_metrics2), cross-region access through posthog-connection-call, why the external-data-* product tools silently answer from your own project instead, and how to end with one recommended action plus who can run it. Internal only: every query returns another customer's data.

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Create a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says "create an endpoint", "expose this query as an API", "turn this insight into an endpoint", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or compare mode, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.

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Expert blueprint for player mortality loops: progress-index checkpoints, soul graves, ghost/spirit layers, I-frame restitution, world-progress persistence, and death analytics. Use when implementing respawn, shrine checkpoints, corpse-run retrieval, or second-chance mechanics. Keywords revival, respawn, checkpoint, soul grave, ghost mode, I-frames, death analytics, progress index.

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Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

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Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

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智能数据库查询技能。通过SSH隧道连接线上数据库，支持自然语言转SQL、执行查询、表结构探索。当用户需要查询数据库、问数据、看表结构时使用此技能。

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Orchestrate the live-ops team for post-launch content planning: coordinates live-ops-designer, economy-designer, analytics-engineer, community-manager, writer, and narrative-director to design and plan a season, event, or live content update.

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Write hyper-personalized cold emails to local businesses using their Google Maps data (CSV). Generates AIDA sequences with personalization from ratings, reviews, website, and industry context.

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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

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交易策略分析专家 - 深度解析交割单和交易复盘数据，提供胜率、盈亏比、策略一致性评估及改进建议。支持 CSV、Excel、文本格式输入，输出专业 Markdown 报告。

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Van Westendorp price sensitivity, computed from real survey answers — crossings found by interpolation, not read off a chart by eye. Use when someone has (or plans) the four-question pricing survey (too cheap / cheap / expensive / too expensive) and needs the optimal price point, the acceptable range, and a defensible readout. Produces OPP/IPP and the PMC–PME range, the four cumulative curves as data, and a real .xlsx with a live revenue what-if — via the bundled zero-dependency script.

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万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。

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Build a new dashboard, or update an existing one, from a set of insights — the same job the in-app assistant does with its upsert-dashboard tool, but over MCP. Use when a user asks to create a dashboard, put several metrics/charts together on one page, assemble a dashboard for a topic (product analytics, retention, revenue, activation, etc.), or add/remove/replace insights on a dashboard they already have. Covers deciding create vs update, reusing existing insights vs creating new ones, and using PostHog's vetted dashboard templates as reference for what a strong dashboard on a topic looks like.

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SQL query expert for optimization, schema design, and data analysis

gabrielmoreira Skill

Generate Word (.docx), Excel (.xlsx) and PowerPoint (.pptx) documents and fill existing PDF forms, from real NetClaw data, with per-element provenance and no fabrication. Use when someone needs a deliverable rather than an answer — a change record to attach to a CR, an audit workbook for a compliance reviewer, a summary deck for a director, or a required PDF form filled from real device and ticket data.

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Optimize slow queries, analyze SQL performance, and collect evidence for expensive workloads.

gabrielmoreira Skill

Build a reviewed crosswalk (RequirementMappingSet YAML library + review xlsx/csv) between two CISO Assistant framework YAML files using Claude itself as the reasoning engine. Zero infrastructure — stdlib + pyyaml only, no embedders, no LM Studio, no Qdrant. Use when the user asks to map / crosswalk / generate a mapping between two frameworks (e.g. ccb-cff-2023-03-01.yaml ↔ cyfun2025.yaml), wants to contribute a community mapping to backend/library/libraries/, or says things like "build a mapping between framework X and Y", "create a crosswalk YAML", "generate requirement_mapping_set". Output matches the schema in backend/library/libraries/mapping-*.yaml exactly so the result is PR-able.

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Build comparable-company valuation workbooks in Excel.

gabrielmoreira Skill

Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics. TRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create. DO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.

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Data analysis expert for statistics, visualization, pandas, and exploration