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Found 4,138 Skills
Multi-agent review-and-improve loop for a GitHub PR you have checked out — posts a "starting" PR comment cc'ing the original author, runs requested rounds plus any adaptive continuation, applies fix commits to the local branch after each round, pushes everything back to the PR, then edits the starting comment in-place with the synthesized report (or a failure summary). Auto-detects the PR from the currently checked-out branch when no locator is supplied. Use when the user wants to "improve a PR", "review and commit fixes", "iterate on my PR", or "review and push back" against a checked-out PR branch. Requires `gh`, `uuidgen`, `jq`, and `uv` or `python3` on PATH. Activates the `review-anvil` engine in per_fix mode.
Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.
Check whether a tenant's DEPLOYED Sumsub config actually satisfies a regulation/policy document — tracing each requirement to where it is collected, scored, and ENFORCED, and flagging "collected-but-not-enforced" gaps. TRIGGER when the user has a regulation/policy/requirements doc (PDF or text) and wants to verify the live config matches it, audit a client's setup against compliance rules, "does my config satisfy this regulation", "check conformance / gap analysis", or close the loop after configuring with the create-* skills. SKIP for building config (sumsub-create-*) or for generating a config plan from a regulation (sumsub-analyze-regulation). For pure hygiene linting with no regulation, run this skill's bundled lint_config.py sub-pass directly.
Analyze a regulation document (PDF or text) and produce a Sumsub configuration plan — mapping regulatory requirements to Sumsub entities (levels, questionnaires, PoA presets, TM rules, workflows). TRIGGER when the user provides a regulation PDF, legal act, or compliance requirement document and wants to know what to configure in Sumsub. Acts as the entry point before invoking sumsub-create-level, sumsub-create-questionnaire, sumsub-create-poa-preset, sumsub-create-workflow, and other skills. SKIP for direct entity creation requests (no regulatory context) or Sumsub API calls.
Create or update a Sumsub Proof-of-Address (POA) preset. POST `/resources/api/agent/poaStepSettings` to create new, PATCH same path to update (id in body), GET `/resources/api/agent/poaStepSettings/{id}` to read one back. TRIGGER when the user asks to "create / add / build / configure / update / edit a POA preset" or "PoA step settings", configure which proof-of-address document types are accepted (utility bills, bank statements, tax bills, etc.), set validity periods per provider type, enable POI-as-POA (accept identity doc as proof of address), tune the cross-validator (name/address fuzzy match between POI and POA), or add per-country POA overrides. SKIP for attaching a preset to a level (set `poaStepSettingsId` on the level instead, via `sumsub-create-level`), or for non-POA presets (cross-check presets, permission presets, etc.).
Use this skill to validate code changes against real Kubernetes microservice dependencies with Signadot signals such as local sandboxes, cluster reachability, logs, endpoints, and routing-key isolation.
Connects to Oxylabs remote headless browsers via Chrome DevTools Protocol (CDP) using Playwright or Puppeteer. Provides anti-detection, CAPTCHA handling, residential proxies, and geo-targeting built in. Use when browser automation needs remote execution, stealth capabilities, rendered pages, screenshots, PDFs, or complex JavaScript interaction.
One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, DeepSeek), or avoid juggling separate provider API keys and accounts — especially when they already use Neon and want AI requests to branch with their project. Works with the OpenAI SDK, Anthropic SDK, google-genai, the Vercel AI SDK, and Mastra by changing only the base URL. Triggers include "call an LLM", "add AI to my app", "chat completion", "model routing", "LLM proxy/gateway", "one API for all models", "use Claude/GPT/Gemini", "AI SDK", "Mastra agent", "Neon AI Gateway", and "log/rate-limit AI calls".