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Found 5,584 Skills
Use when designing module boundaries, planning refactors, or reviewing architecture in Python codebases. Also use when facing tangled dependencies, god classes, deep inheritance hierarchies, unclear ownership, or risky structural changes.
Universal watermark removal with ML-based inpainting and automatic detection. Works on ANY watermark type (Google SynthID, Midjourney, DALL-E, stock photos, logos). Four methods: inpaint (ML, best quality), aggressive (fast), crop (fastest), paint (basic). Auto-detects watermark location in any corner. Use when: (1) Removing ANY type of watermark, (2) Google AI/Imagen/Gemini watermarks, (3) Stock photo watermarks, (4) Logo overlays, (5) Cleaning images for production, (6) Batch processing, or (7) User mentions 'watermark', 'remove watermark', 'clean image', 'SynthID'
Smart Disk Cleaner for Mac, a user-friendly wrapper based on Mole (https://github.com/tw93/Mole)
Security scanner for vibe-coded projects. AUTO-INVOKE this skill before any git commit, git push, or when user says "commit", "push", "ship it", "deploy", "is this safe?", "check for security issues", or "goodvibesonly". Also invoke after generating code that handles user input, authentication, database queries, or file operations.
This skill generates text-based UI wireframes using .kui file syntax. Use when creating wireframes, mockups, or UI layout diagrams. The skill converts .kui files to SVG/PNG images via the katsuragi CLI tool.
Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).
Find technical debt patterns in codebases. Use when asked to find duplicated code, inconsistent patterns, or refactoring opportunities.
Choose optimal Next.js rendering strategy (SSR, SSG, ISR, CSR) based on content type, update frequency, and performance requirements. Use when deciding how to render pages, optimizing performance, or implementing data fetching. Trigger words include "rendering", "SSR", "SSG", "ISR", "static", "server-side".
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Modern file and content search using fd, ripgrep (rg), and fzf. Triggers on: fd, ripgrep, rg, find files, search code, fzf, fuzzy find, search codebase.
AWS cost optimization and FinOps workflows. Use for finding unused resources, analyzing Reserved Instance opportunities, detecting cost anomalies, rightsizing instances, evaluating Spot instances, migrating to newer generation instances, implementing FinOps best practices, optimizing storage/network/database costs, and managing cloud financial operations. Includes automated analysis scripts and comprehensive reference documentation.