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Found 13 Skills
Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.
Generate a resumable handoff document from an in-progress conversation, review, debugging session, or investigation. Dispatches co-located subagents to extract original instructions and Q&A context, capture evidence-backed insights, optionally validate claims from tracking files, and assemble a cold-start-ready handoff file plus structured working artifacts. Use when the user says "create a handoff doc", "save this for later", "document what we found", "update the resumption file", or wants a fresh agent to resume later without relying on chat history.
Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.
AWS CloudFormation patterns for Lambda functions, layers, event sources, and integrations. Use when creating Lambda functions with CloudFormation, configuring API Gateway, Step Functions, EventBridge, SQS, SNS triggers, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for cold start optimization.
Provides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications. Triggers include "create lambda python", "deploy python lambda", "chalice lambda aws", "python lambda cold start", "aws lambda python performance", "python serverless framework".
Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Use when deploying PHP/Symfony applications to AWS Lambda, optimizing cold starts, configuring API Gateway integration, or implementing serverless PHP applications with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".
Provides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when deploying TypeScript functions to AWS Lambda, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications. Triggers include "create lambda typescript", "deploy typescript lambda", "nestjs lambda aws", "raw typescript lambda", "aws lambda typescript performance".
AWS Lambda, Vercel Edge Functions, Cloudflare Workers, cold starts, deployment patterns, and infrastructure as code (SST, Serverless Framework). Use when building serverless applications or optimizing function-based architectures.
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.
Design hybrid recommendation systems combining multiple strategies for improved accuracy. Use this skill when the user needs to overcome single-method limitations, combine collaborative and content-based filtering, or build a production recommendation pipeline — even if they say 'combine recommendation approaches', 'best recommendation architecture', or 'cold start plus personalization'.
Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.