Generate evidence-based documentary reports by searching across all 4

Memory leak detection and analysis for Node.js, Python, and browsers

Automatically activates after significant decisions, pattern discoveries, or context that should persist across sessions. Saves decisions to org memory, patterns to pattern library, and context to user memory. Activates when important architectural choices are made, new patterns are discovered, or valuable context emerges.

Unified four-tier memory system for AI agents. Tier 1 Semantic (Serena+Forgetful

Synchronize CLAUDE.md and AGENTS.md with available agents and skills.

Merges a cover image (PNG/JPG) into an existing PDF book, ensuring the cover matches the book's page width exactly while preserving its aspect ratio.

mermaid-diagram-generator

Specialized skill for generating Mermaid diagrams with light/dark mode compatible colors. Use when creating architectural diagrams, flowcharts, ER diagrams, or sequence diagrams.

Build TX V4 meshes - agent configs, prompts, routing. Use for new meshes, agent roles, or multi-agent workflows. Triggers - mesh, routing, agents, multi-agent, config.yaml

Proactively analyzes the codebase and generates specialized subagents and skills to standardize agentic workflows.

Create language conversion skills for translating code from language A to language B. Use when building 'convert-X-Y' skills, designing type mappings between languages, establishing idiom translation patterns, or defining conversion methodologies. Provides foundational patterns that specific conversion skills extend.

Guide for translating code between programming languages. Use when converting code from one language to another, planning language migrations, understanding conversion challenges, asking about type mappings, idiom translations, or referencing pattern mappings. Covers APTV workflow, type systems, error handling, concurrency, and language-specific gotchas.

meta-prompt-framework

Comprehensive guide for creating Claude Code custom skills. Use when asked to create, design, or improve custom skills for Claude Code. Provides templates, best practices, and patterns for simple, moderate, and advanced skills.

Performs enrichment analysis (GSEA-based) for metabolic pathways across different cell groups to identify significantly enriched pathways. Uses fast gene set enrichment analysis (fgsea package) to rank pathways by their association with specific clusters, conditions, or cell states. Generates summary plots and enrichment visualizations for biological interpretation.

Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions.

Use this skill when creating or updating DAG configurations (dags.yaml), schema.yaml, and metadata.yaml files for BigQuery tables. Handles creating new DAGs when needed and coordinates test updates when queries are modified (invokes sql-test-generator as needed). Works with bigquery-etl-core, query-writer, and sql-test-generator skills.

Especialista en validación y generación de metadatos YAML (frontmatter) para archivos markdown del proyecto SyV, garantizando coherencia con estándares

Apply Bootstrapped AI Methodology Engineering (BAIME) to develop project-specific methodologies through systematic Observe-Codify-Automate cycles with dual-layer value functions (instance quality + methodology quality). Use when creating testing strategies, CI/CD pipelines, error handling patterns, observability systems, or any reusable development methodology. Provides structured framework with convergence criteria, agent coordination, and empirical validation. Validated in 8 experiments with 100% success rate, 4.9 avg iterations, 10-50x speedup vs ad-hoc. Works for testing, CI/CD, error recovery, dependency management, documentation systems, knowledge transfer, technical debt, cross-cutting concerns.

Draft publication-ready Methods sections for interview-based sociology articles. Guides pathway selection, component coverage, and calibration based on analysis of 77 Social Problems/Social Forces articles.

Use when investigating unexpected metric changes - systematically narrows root cause through 4D segmentation, intrinsic vs extrinsic factor analysis, hypothesis testing, and North Star impact assessment

Comprehensive metrics dashboard strategy including North Star Metric definition, AARRR Pirate Metrics framework, product engagement tracking, 5 role-specific dashboards, alert configuration, data infrastructure planning, and 90-day implementation roadmap for data-driven decision making

Authentic Mexican cooking expert covering moles, salsas, tacos, and regional Mexican dishes

Rapidly build and launch micro-SaaS applications with best practices, monetization, and deployment

Interact with MicroPython boards via mpy-repl-tool to push files, execute code, and test MicroPython scripts. Use when working with MicroPython development, testing board functionality, or evaluating MicroPython code on hardware.

Creates interactive educational MicroSims using the best-matched JavaScript library (p5.js, Chart.js, Plotly, Mermaid, vis-network, vis-timeline, Leaflet, Venn.js). Analyzes user requirements to route to the appropriate visualization type and generates complete MicroSim packages with HTML, JavaScript, CSS, documentation, and metadata.

This skill analyzes diagram, chart, or simulation specifications and returns a ranked list of the most suitable MicroSim generator skills to use. It compares the specification against capabilities of all available microsim generators (p5.js, ChartJS, Plotly, Mermaid, vis-network, timeline, map, Venn, bubble) and provides match scores (0-100) with detailed reasoning for each recommendation. Use this skill when a user has a diagram specification and needs guidance on which MicroSim generator skill to use.

Utility tools for MicroSim management including quality validation, screenshot capture, icon management, and index page generation. Routes to the appropriate utility based on the task needed.

Expert guidance for Microsoft Fabric development using the Fabric MCP Server. Access Fabric public APIs, OpenAPI specs, item schemas, best practices, and OneLake file management. Use when working with Fabric workloads, Lakehouses, pipelines, semantic models, notebooks, or building Fabric integrations.

Generate MJ V7 prompts for all images in a page.

Migrate Python modules from SDR_stochastic research code to vrp-toolkit architecture. Use when migrating files from the old codebase structure to the new three-layer architecture (Problem/Algorithm/Data layers), refactoring paper-specific code into generic implementations, or converting research notebooks into educational tutorials.

Complete migration guide from Zod v3 to v4 covering all breaking changes and upgrade patterns

Specialized in database migrations and data seeding. Trigger this when creating tables, modifying schemas, or preparing initial data.

Expert guidance for writing database migrations using golang-migrate for the mediaz SQLite database. Covers migration creation, testing, rollback capability, data preservation, and mediaz-specific patterns. Activates when users mention migrations, schema changes, database alterations, or golang-migrate.

Debug and troubleshoot Mini-Apps when they fail to load, build, or run. Covers build checks, browser console inspection, bridge issues, and asset routing fixes.

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Setup and configure Minikube for local Kubernetes development

Manages local Kubernetes clusters using Minikube for development and testing. This skill should be used when setting up local K8s environments, enabling addons, configuring networking, and deploying applications locally. Use this skill for Phase IV local Kubernetes deployments before cloud deployment.

Mise development environment manager (asdf + direnv + make replacement). Capabilities: tool version management (node, python, go, ruby, rust), environment variables, task runners, project-local configs. Actions: install, manage, configure, run tools/tasks with mise. Keywords: mise, mise.toml, tool version, runtime version, node, python, go, ruby, rust, asdf, direnv, task runner, environment variables, version manager, .tool-versions, mise install, mise use, mise run, mise tasks, project config, global config. Use when: installing runtime versions, managing tool versions, setting up dev environments, creating task runners, replacing asdf/direnv/make, configuring project-local tools.

This skill implements the miso feature-to-code workflow. When feature markdown files change, it automatically propagates those changes through the implementation chain: pseudocode → platform-specific

Convert PDFs to Markdown using Mistral OCR API with image extraction. Use when you need to extract structured text and images from PDFs, especially for scanned documents or documents with complex formatting. Outputs Markdown with embedded images.

MkDocs Material documentation management. This skill should be used when writing, formatting, or validating documentation in docs/. Covers admonitions, Mermaid diagrams, code blocks with annotations, content tabs, navigation setup, and mkdocs testing. Always check project-specific docs at docs/dev/ai/skills/ and docs/dev/ai/agents/ for project-specific Claude skill and Claude agent documentation when available.

Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.

Serve models with A/B testing, monitoring, retraining.

Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.

ml-pipeline-orchestrator

Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.

ML research for RAN with reinforcement learning, causal inference, and cognitive consciousness integration. Use when researching ML algorithms for RAN optimization, implementing reinforcement learning agents, developing causal models, or enabling AI-driven RAN innovation.

End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deployment and monitoring.

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