simple-gemini

115

VCnoC

Collaborative documentation and test code writing workflow using zen mcp's clink to launch gemini CLI session in WSL (via 'gemini' command) where all writing operations are executed. Use this skill when the user requests "use gemini to write test files", "use gemini to write documentation", "generate related test files", "generate an explanatory document", or similar document/test writing tasks. The gemini CLI session acts as the specialist writer, working with the main Claude model for context gathering, outline approval, and final review. For test code, codex CLI (also launched via clink) validates quality after gemini completes writing.

mcp-builder

109

partme-ai

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

deal-quality-model

96

gtmagents

Scoring system for opportunity hygiene, win likelihood, and inspection prioritization.

quality-gates

96

gtmagents

Use when establishing tests, monitoring, and incident response for analytics models.

ingest-codebase

91

Michaelliv

Guide the creation of a high-quality mental model for a codebase. Use when starting a new mental model, when the model feels incomplete or unclear, or when onboarding to understand a system's architecture. Produces domains, capabilities, aspects, and architectural decisions.

google-veo

76

inference-sh

Generate videos with Google Veo models via inference.sh CLI. Models: Veo 3.1, Veo 3.1 Fast, Veo 3, Veo 3 Fast, Veo 2. Capabilities: text-to-video, cinematic output, high quality video generation. Triggers: veo, google veo, veo 3, veo 2, veo 3.1, vertex ai video, google video generation, google video ai, veo model, veo video

prompt-optimizer

74

wasintoh

Advanced prompt optimization and composition system for all prompt types (system prompts, task-specific, creative, technical, agentic). Use when users need to refine, optimize, or transform prompts to achieve better AI outputs. Triggers include requests to improve prompts, make prompts more effective, analyze prompt quality, create prompts from scratch, optimize existing prompts for specific goals, or optimize for Claude 4.x models. Also use when users share prompts that could be improved or ask for prompt engineering guidance.

MCP Builder Skill

57

jjmartres

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

testing-dbt-models

55

AltimateAI

Adds schema tests and data quality validation to dbt models. Use when working with dbt tests for: (1) Adding or modifying tests in schema.yml files (2) Task mentions "test", "validate", "data quality", "unique", "not_null", or "accepted_values" (3) Ensuring data integrity - primary keys, foreign keys, relationships (4) Debugging test failures or understanding why dbt test failed Matches existing project test patterns and YAML style before adding new tests.

mcp-builder

55

ng-forge

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services using Node/TypeScript (MCP SDK).

bim-validation-report

46

datadrivenconstruction

Generate comprehensive BIM model validation reports. Check data quality, completeness, and compliance with standards.

mcp-builder

44

Kilo-Org

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

rag-implementation

44

applied-artificial-intelligence

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.

qa_repair_geometry

43

gpt-cmdr

Automated geometry repair using RasFixit and quality validation using RasCheck. Handles blocked obstructions, generates before/after visualizations, and creates audit trails. Use when fixing geometry errors, repairing obstructions, validating models, or ensuring FEMA compliance. Triggers: fix, repair, geometry, blocked obstruction, validate, check, RasCheck, RasFixit, FEMA, quality assurance, QA, overlapping, obstruction overlap, elevation envelope, geometry error.

qa_review_triple-model

43

gpt-cmdr

Launch four independent AI code reviewers (Opus, Gemini, Codex, Kimi K2.5) to QA/QC code or notebooks. Each reviewer writes findings to separate markdown files, then orchestrator synthesizes. Use for critical code review, bug investigation, or quality assurance tasks. Triggers: triple review, quad review, four model review, independent code review, QAQC, quality assurance, multi-model analysis, cross-validation, bug investigation, critical review, kimi review, togetherai review

dev_invoke_gemini-cli

43

gpt-cmdr

Delegate QAQC and review tasks to Google Gemini CLI using markdown file handoff pattern. Write review request to REVIEW.md, Gemini analyzes, outputs findings to FINDINGS.md. Use for code review, security audits, documentation review, large context analysis. Triggers: gemini, gemini cli, delegate to gemini, gemini subagent, code review, QAQC, quality check, security audit, documentation review, large context, second opinion, architecture review, gemini-3-pro-preview, gemini-3-flash-preview Prerequisites: Gemini CLI authenticated (gemini login or GEMINI_API_KEY) Models: gemini-3-pro-preview (default), gemini-3-flash-preview (large context)

data-pipeline-engineer

43

curiositech

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

dev_invoke_kimi-cli

43

gpt-cmdr

Delegate testing, QA, and code review tasks to Opencode CLI using Kimi K2.5 model via markdown file handoff. Write test request to TASK.md, Opencode with Kimi K2.5 generates tests/reviews, outputs to OUTPUT.md. Use for test generation, QA verification, edge case detection, code coverage analysis, security reviews. Triggers: kimi, kimi k2.5, kimi cli, opencode kimi, test generation, QA, quality assurance, code review, unit tests, integration tests, edge cases, test coverage, testing, kimisubagent, togetherai kimi, opencode/kimi-k2.5-free, togetherai/moonshotai/Kimi-K2.5 Prerequisites: Opencode CLI installed, Together.ai API key (if using togetherai provider) Models: opencode/kimi-k2.5-free (recommended), togetherai/moonshotai/Kimi-K2.5 (alternative)

data_transform

39

vuralserhat86

Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

rails-audit-thoughtbot

37

thoughtbot

Perform comprehensive code audits of Ruby on Rails applications based on thoughtbot best practices. Use this skill when the user requests a code audit, code review, quality assessment, or analysis of a Rails application. The skill analyzes the entire codebase focusing on testing practices (RSpec), security vulnerabilities, code design (skinny controllers, domain models, PORO with ActiveModel), Rails conventions, database optimization, and Ruby best practices. Outputs a detailed markdown audit report grouped by category (Testing, Security, Models, Controllers, Code Design, Views) with severity levels (Critical, High, Medium, Low) within each category.

mcp-builder

37

nj19257

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

data-analytics-engineering

34

vasilyu1983

Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.

model-selection

31

melodic-software

Choose appropriate model for custom agent tasks. Use when selecting between Haiku, Sonnet, and Opus for agents, optimizing cost vs quality tradeoffs, or matching model capability to task complexity.

rag-architecture

31

melodic-software

Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.

semantic-search

29

omer-metin

Build production-ready semantic search systems using vector databases, embeddings, and retrieval-augmented generation (RAG). Covers vector DB selection (Pinecone/Qdrant/Weaviate), embedding models (OpenAI/Voyage/Cohere), chunking strategies, hybrid search, and reranking for high-quality retrieval. Use when ", vector-search, embeddings, rag, pinecone, qdrant, weaviate, llama-index, langchain, hybrid-search, reranking" mentioned.

protein-structure

29

omer-metin

Patterns for protein structure prediction using AlphaFold2/ColabFold, structural analysis, model quality assessment, and integration with experimental data. Covers best practices and critical interpretation of prediction confidence metrics. Use when ", " mentioned.

dbt-artifacts

28

sfc-gh-dflippo

Monitor dbt execution using the dbt Artifacts package. Use this skill when you need to track test and model execution history, analyze run patterns over time, monitor data quality metrics, or enable programmatic access to dbt execution metadata across any dbt version or platform.

dbt-migration-validation

28

sfc-gh-dflippo

Comprehensive validation skill for dbt models and schema YAML files. Defines validation rules, common anti-patterns to detect, and auto-fix suggestions. Integrates with Claude Code hooks to enforce quality standards during migration.

senior-data-engineer

28

hainamchung

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.

documentation-agent

27

Agile-V

Generates standards-based repository documentation for GitHub or any project. Writes a docs suite into the project's docs/ directory covering ISO 9001, V-Model, ISO 27001, and optionally GAMP 5 or other standards. Use when the user asks for repo documentation, compliance docs, quality docs, or to create/refresh the docs/ suite.

midjourney-replicate-flux

25

rawveg

Generate highly detailed, Midjourney-style image prompts optimized for the FLUX 1.1 Pro model on Replicate. Transform basic user descriptions into rich, cinematic prompts with professional photography qualities, dramatic lighting, and editorial-quality aesthetics. Use when users request image generation, need prompt enhancement, or want Midjourney-quality outputs via FLUX 1.1 Pro.

model-quantization

25

Martinholovsky Claude Skills Generator Model Quantization

Expert capability for AI model quantization and optimization. Covers 4-bit and 8-bit quantization, GGUF conversion, memory optimization, and quality–performance tradeoffs for deploying large language models in resource-constrained JARVIS environments.

quality-assurance-auditor

23

yushui2022

Enforce auditing of paper generation quality to prevent model substitution, logical breaks, and hollow content. Invoke when the user requests inspection/audit/acceptance/assurance/verify/QA, or when a gate is needed before merging.

mcp-builder

22

mjunaidca

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

nano-banana

22

leegonzales

Generate and edit high-quality AI images using Google's Gemini 3 Pro Image model (Nano Banana Pro) via MCP. Use when user wants to create images, edit photos, generate graphics, or needs visual content with text rendering.

building-mcp-servers

22

mjunaidca

Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).

validation-plan-artifacts

21

deepeshBodh

This skill MUST be invoked when the user says "review research", "review data model", "review contracts", "plan quality", "phase review", or "design gaps". SHOULD also invoke when user mentions "artifact review" or "planning validation".

nano-banana

20

nilecui

Generate and edit high-quality AI images using Google's Gemini 3 Pro Image model (Nano Banana Pro) via MCP. Use when user wants to create images, edit photos, generate graphics, or needs visual content with text rendering.

mcp-builder

20

basher83

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

crt-validating-solution

20

BellaBe

Validates the Solution section of strategy/canvas.md against quality criteria. Checks growth model selection, problem-feature mapping, and MVP scope. Use when reviewing solution design, checking feature prioritization, or auditing canvas.

prompt-optimizer

20

ckanner

This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models can better understand and execute. This skill applies comprehensive prompt engineering best practices to enhance prompt quality, clarity, and effectiveness.

grey-haven-evaluation

20

greyhaven-ai

Evaluate LLM outputs with multi-dimensional rubrics, handle non-determinism, and implement LLM-as-judge patterns. Essential for production LLM systems. Use when testing prompts, validating outputs, comparing models, or when user mentions 'evaluation', 'testing LLM', 'rubric', 'LLM-as-judge', 'output quality', 'prompt testing', or 'model comparison'.

pw-danger-gemini-web

20

plugins-world

Text and image generation using a reverse-engineered Gemini Web API. Core capabilities: - Text generation: Generate text responses using Gemini models - Image generation: Generate images from text prompts and save them locally - Visual input: Support reference images for image-to-image or vision-guided text generation - Multi-turn conversation: Maintain conversational context via sessionId When to use: - When you need to generate high-quality AI images (cover art, in-article images, illustrations, etc.) - When you need text generation using Gemini models - When you need to generate variants or descriptions based on reference images - As an image generation backend for other skills (pw-cover-image, pw-redbook-image) Not suitable for: - Production environments that require official API support and guaranteed stability - Critical business processes sensitive to API changes - High-volume or high-frequency batch calls (may trigger rate limiting) Important reminders: - First use requires the user to accept a disclaimer - Google account authentication is required (automatically opens the browser to sign in) - Access from mainland China requires configuring a proxy - This is an unofficial API and may stop working at any time

ai-app-performance-optimization

19

samarv

Shift focus from AI hype (latest models, vector DBs, agentic frameworks) to high-leverage activities that actually improve product quality. Use this when an AI feature is underperforming, when the team is stuck in a "research loop," or when planning the roadmap for a new AI application.

defining-ai-objective-functions

19

samarv

A framework for defining high-taste "Objective Functions" to train AI models. Use this when setting quality standards for RLAIF/RLHF, designing data labeling rubrics, or deciding how a model should prioritize trade-offs (e.g., brevity vs. depth).

interaction-design

18

petekp

Design intuitive, meaningful interactions grounded in user goals and cognitive principles. Use when designing component behaviors, user flows, feedback systems, error handling, loading states, transitions, accessibility, keyboard navigation, touch/gesture interactions, or when evaluating interaction quality. Also use for modal vs modeless decisions, direct manipulation patterns, input device considerations, emotional/dramatic aspects of UX, or when asked about making interfaces feel responsive, humane, and goal-directed.

agentic-development

17

Exploration-labs

Conversational guidance for building software with AI agents, covering workflows, tool selection, prompt strategies, parallel agent management, and best practices based on real-world high-volume agentic development experience. Use this skill when users ask about setting up agentic workflows, choosing models, optimizing prompts, managing parallel agents, or improving agent output quality.

Rapid Convergence

16

yaleh

Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).