494 skills · sorted by relevance
Generate Python code for Dataverse SDK workflows, including retries, bulk data changes, efficient queries, table metadata, and large file uploads. The skill also covers pandas integration and asks for type hints, docstrings, and official API references.
Generate Python code for Dataverse integrations with error handling, retries, logging, and shared connection management. Include type hints, usage examples, and queries that retrieve only the records and columns needed for the task.
Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.
Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.
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Provision a PostgreSQL database with Neon Instagres and save connection settings for application use and migrations. Includes setup examples for Drizzle and Prisma, initial data loading, and claiming a temporary database to keep using it.
Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.
Use when reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit
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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).
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Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).
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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.
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Plan database schema and data changes for production systems. Use staged column replacements, concurrent index creation, and batched data updates to limit disruption, with migration workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate.
Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.
Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.
Look up a music production by its ID to retrieve metadata and an audio URL. Check whether stem separation, which splits audio into parts, MIDI extraction, or mastering has finished using the production’s latest status.
Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.
Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.
Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.
Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
Debug "API returns data that doesn't exist in database" when multiple Kubernetes clusters exist (production, staging, POC). Use when: (1) REST API returns records that direct database queries can't find, (2) Data counts match approximately but specific records don't overlap, (3) kubectl context points to a different cluster than the one serving the public domain, (4) ClickHouse/Postgres queries return stale or different data than the API, (5) "completely different datasets" despite same table names and schemas. Root cause: querying the wrong cluster's database.
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You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.
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Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.
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Assess whether an application is ready for production using local files and authorized evidence. Reviews authentication, data, payments, and operations, then reports release blockers, prioritized fixes, and a readiness score without uploading repository contents to an external audit service.
Add logs, metrics, traces that follow requests across services, and actionable alerts to production features. This skill guides signal design, request identification, response instructions, and checks that the collected data can explain failures.