Agent Skills Hub

example-data-processor

A modular data processing tool for cleaning, validating, and analyzing CSV files with support for custom transformations and automated dependency management.

basic-usage

Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.

session-logs

Search, analyze, and audit GeminiClaw session logs and memory. Use to investigate past interactions, track token usage, debug tool calls, and monitor agent performance.

obsidian-bases

Create and edit Obsidian Bases (.base files) with database-like views, complex filters, custom formulas, and data summaries.

database-driver-design

Expert guidance for building production-ready Swift database client libraries, covering wire protocols, connection pooling, state machines, and NIO integration.

spark-optimization

Optimize Apache Spark jobs with partitioning strategies, memory management, shuffle tuning, and data skew mitigation for high-performance data processing pipelines.

projection-patterns

Build read models and projections from event streams for CQRS, materialized views, and optimized query performance in event-sourced systems.

code-quality

Enforce strict code quality, correctness, and Rust design patterns for the Turso database, prioritizing data integrity, performance, and maintainable, idiomatic code.

sequential-thinking

Structured reasoning tool for complex problem decomposition, step-by-step analysis, consistency verification, and evidence-based synthesis with confidence scoring.

simplemem-skill

Persistent, semantic long-term memory for AI agents. Save, query, and retrieve cross-session dialogues, decisions, and multimodal context using semantic compression.

use-railway

Operate Railway infrastructure: manage projects, services, databases, object storage, deployments, environments, variables, logs, and performance metrics.

snowflake-semantic-views

Create, alter, and validate Snowflake semantic views via the CLI. Automate the generation, documentation, and testing of semantic layer definitions to ensure model accuracy and star schema compliance.