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Found 14,001 Skills
Guidance on how to use Claude Code effectively — covering context management, verification strategies, the explore-plan-implement workflow, prompting techniques, session management, parallel sessions, and common failure patterns. Use this skill whenever the user asks how to get the most out of Claude Code, how to write better prompts, how to manage context, when to use plan mode, how to automate tasks, or when they describe a frustrating pattern like Claude repeating mistakes or losing track of instructions.
Recall past Parallel Task, Monitor, and FindAll runs when they may help; evict runs or clear memory when asked.
EditorConfig (エディタ設定統一フォーマット) リファレンス。 .editorconfig ファイル、glob パターン、root、 indent_style / indent_size / tab_width、end_of_line, charset、 trim_trailing_whitespace, insert_final_newline, max_line_length。
Lefthook (高速並列 Git hooks マネージャー) リファレンス。 lefthook.yml 設定、pre-commit / pre-push / commit-msg 等のフック、 parallel / piped 実行、glob / run / tags フィルタ、 CI スキップ、staged_files、Husky からの移行。
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots.
Quick wizard to create a new Pine Script from a description. Just describe what you want and get working code.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
Add Sumsub Device Intelligence (the Fisherman JS module) to your own web pages where there is NO Sumsub verification widget — login, signup, password reset, 2FA, checkout, or any high-value action you want fraud-screened. TRIGGER when the user asks to "add device intelligence to login / signup", "fingerprint the device on a custom page", "use Fisherman standalone / self-hosted", "ongoing monitoring of platform events", "pre-KYC device check", "detect multi-accounting at signup", or "send a financial transaction with the captured device". Covers the whole loop — behavior access token, @sumsub/fisherman init + fingerprint, confirming the event (platform event / financial transaction / create-applicant), reading results, sandbox, go-live. SKIP when the page already embeds the Sumsub WebSDK — Device Intelligence rides along inside it, use `sumsub-integrate-dint-websdk`.
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.