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Found 337 Skills
Plans, creates, and optimizes marketing campaigns including content strategy, social media, email, and analytics. Helps develop go-to-market strategies, campaign messaging, and performance measurement.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
Manage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes). NOT for deleting services - use railway-environment skill with isDeleted for that.
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Add, view, or remove domains for Railway services. Use when user wants to add a domain, generate a railway domain, check current domains, get the URL for a service, or remove a domain.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
Deep web scraping, screenshots, PDF parsing, and website crawling using Firecrawl API
バックエンドコード規約とパターン集。APIルート、Repository、バリデーション等の実装パターン。 使用タイミング: (1) 新しいAPIルート作成時 (2) Repository実装時 (3) バリデーション設計時 (4) エラーハンドリング実装時 (5) バックエンドのベストプラクティス確認時。 トリガー例: 「APIルートを作って」「Repositoryパターンで」「バックエンドのパターン」 「Zodでバリデーション」「エラーハンドリングのパターン」
プロジェクト共通のコーディング規約。TypeScript、命名規則、関数設計、React/Next.js等。 使用タイミング: (1) コードレビュー時 (2) 新規ファイル作成時 (3) リファクタリング時 (4) コーディングスタイルの確認時 (5) チーム開発のルール確認時。 トリガー例: 「コーディング規約」「命名規則は？」「TypeScriptのルール」 「インポート順序」「コミットメッセージの書き方」