karim-bhalwani/agent-skills-collection Skills

Specialized in system architecture, API contracts, and modular black-box design. Use when creating technical specifications, designing system primitives, defining module boundaries and contracts, reviewing architecture for replaceability, or mapping system dependencies.

Explore user intent, requirements, and design before implementation. Use when turning ideas into designs, validating requirements through dialogue, exploring multiple approaches, or creating design documentation for features, components, and functionality changes.

Generate clear, actionable, and atomic checklists for coding tasks with minimal interaction and reasonable assumptions. Use when creating implementation plans, breaking down tasks into steps, generating task checklists, or planning project workflows.

Initialize project infrastructure, generate context files, and maintain the Single Source of Truth. Use when setting up new projects, auditing project environments, generating context documentation, maintaining project memory, or ensuring consistency across the codebase.

Specialist in analyzing access logs to identify unused data structures, technical debt, and legacy patterns. Use when auditing data warehouses for deprecation candidates, detecting stale tables/views, identifying legacy application dependencies, or planning safe data asset retirement.

Design and implement data pipelines using Medallion architecture, Data Vault 2.0, and Spark optimization. Use when building ETL/ELT processes, designing data schemas, optimizing Spark performance, implementing data governance, or architecting lakehouse solutions.

Specialist in hands-on ETL/ELT design, implementation, and optimization using Airflow, Spark, Kafka, dbt, and data quality frameworks. Use when building data pipelines, orchestrating data workflows, optimizing data processing, implementing streaming solutions, or ensuring data quality throughout pipelines.

Specialist in data quality validation frameworks—Great Expectations, dbt tests, data contracts. Builds comprehensive data quality gates into pipelines for reliability and trust.

Expert in dbt (data build tool) for SQL-first analytics engineering—model organization, incremental strategies, testing, documentation, and lineage management. Use when building dbt projects, designing transformation models, implementing incremental strategies, testing data quality, or managing dbt documentation.

Automate GitHub workflows with AI-powered assistance for PR reviews, issue triage, CI/CD pipelines, and Git operations. Use when setting up automated code reviews, implementing issue labeling, creating smart CI/CD workflows, building deployment validation, automating Git operations, or configuring on-demand AI assistance bots.

Specialized in quality assurance, security auditing, and performance optimization. Use when conducting code reviews, hunting bugs, scanning for vulnerabilities, profiling performance, ensuring security standards, or validating implementation quality.

Specialized in high-quality implementation, test-driven development, and clean code practices. Use when writing features, fixing bugs, implementing architectural specifications, refactoring code, or ensuring code quality.

Convert JSON data snippets into Python Pydantic data models with proper type annotations and validation. Use when converting JSON schemas to Pydantic models, generating data validation models from JSON examples, or creating Python data classes.

Production LLM application patterns, architectures, and best practices. Covers RAG pipelines, agent architectures, prompt engineering, LLMOps, and production deployment patterns.

Specialized in codebase exploration, finding file paths, tracing execution logic, and mapping dependencies. Use when exploring unfamiliar codebases, finding where code is located, tracing function calls, understanding module structure, or mapping system dependencies.

Specialized in CI/CD, Infrastructure-as-Code, release planning, and technical documentation. Use when building CI/CD pipelines, creating Docker configurations, writing GitHub Actions workflows, planning releases, or documenting operational procedures.

Specialized in managing project-wide constraints, context tiers, and coordination between specialized skills. Use when interpreting project rules, coordinating complex workflows, managing context and dependencies, or overseeing multi-skill project execution.

Strategic technical leadership in data platform design, architecture decisions, and engineering excellence. Use when making strategic architecture decisions, designing data platforms at scale, mentoring teams, or solving complex data engineering challenges.

Comprehensive toolkit for product managers including RICE prioritization, customer discovery, PRD templates, metrics frameworks, and go-to-market strategies. Use when prioritizing features, conducting customer research, defining requirements, or tracking product success.