llm-ai
16
enactdocsguide
LLM guide for creating, publishing, and running Enact tools
EnactProtocol
data-ai
sorting
stars
current ordering strategy
query
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llm-ai
16
checking-skill-best-practices
Evaluates Claude skills against official best practices from Anthropic documentation. Use when reviewing skill quality, ensuring compliance with guidelines, or improving existing skills.
onukura
data-ai
llm-ai
16
grey-haven-memory-systems
Design and implement long-term memory systems for AI agents using vector stores, knowledge graphs, and hybrid approaches. Includes benchmarks and decision frameworks. Use when building persistent agent memory, implementing RAG, designing knowledge bases, or when user mentions 'memory', 'RAG', 'vector store', 'knowledge graph', 'long-term memory', 'retrieval', or 'embeddings'.
greyhaven-ai
data-ai
llm-ai
16
design-synthesizer
Auto-activates when synthesizing outputs from multiple parallel sub-agents to create cohesive, integrated design with consistency checking and conflict resolution.
matteocervelli
data-ai
llm-ai
16
nucleo-matcher
High-performance fuzzy matching library for filtering choices in prompts
johnlindquist
data-ai
llm-ai
16
agent-inbox
Check and process messages from autonomous AILANG agents. Use when starting a session, after agent handoffs, or when checking for completion notifications.
sunholo-data
data-ai
machine-learning
16
mlops-best-practices
MLOps best practices for model versioning, experiment tracking, deployment, monitoring, and retraining workflows. Covers reproducibility, CI/CD for ML, model registry, and production ML system design.
ilyasibrahim
data-ai
machine-learning
16
model-evaluation-framework
Model evaluation metrics, testing protocols, and performance assessment for Somali dialect classification. Covers accuracy, F1-score, confusion matrix analysis, per-dialect performance, and evaluation best practices for multi-class classification tasks.
ilyasibrahim
data-ai
data-analysis
15
matlab-uihtml-app-builder
Build interactive web applications using HTML/JavaScript interfaces with MATLAB computational backends via the uihtml component. Use when creating HTML-based MATLAB apps, JavaScript MATLAB interfaces, web UIs with MATLAB, interactive MATLAB GUIs, or when user mentions uihtml, HTML, JavaScript, web apps, or web interfaces.
matlab
data-ai
data-analysis
15
moai-lang-r
R 4.4+ development specialist covering tidyverse, ggplot2, Shiny, and data science patterns. Use when developing data analysis pipelines, visualizations, or Shiny applications.
modu-ai
data-ai
data-engineering
15
servicenow-table-api
Manages ServiceNow tables. Use for CRUD on any table. Triggers - generic data ops.
Knuckles-Team
data-ai
llm-ai
15
critical-perspective
Engage in critical thinking by questioning assumptions, exploring alternative perspectives, and uncovering latent topics in conversations. Use when discussions could benefit from deeper exploration, when identifying blind spots, or when broadening understanding through respectful challenge and curiosity-driven inquiry.
infranodus
data-ai
llm-ai
15
writing-for-llms
Use when writing prompts, agent instructions, SKILL.md, commands, system prompts, Task tool prompts, prompt engineering, or LLM-to-LLM content
TechNickAI
data-ai
llm-ai
15
grok-skill
Search and analyze X (Twitter) using xAI Grok 4 via OpenRouter with Live Search. Trigger on prompts that explicitly or implicitly ask to "search Twitter/X", "what's trending", "tweets from @handle", "hashtag #…", "what are people saying", or that require tweet-level activity/engagement from X.
mikedemarais
data-ai
llm-ai
15
subagent-prompt-construction
Systematic methodology for constructing compact (<150 lines), expressive, Claude Code-integrated subagent prompts using lambda contracts and symbolic logic. Use when creating new specialized subagents for Claude Code with agent composition, MCP tool integration, or skill references. Validated with phase-planner-executor (V_instance=0.895).
yaleh
data-ai
machine-learning
15
replicate-cli
This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.
rawveg
data-ai
llm-ai
15
moai-foundation-core
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
modu-ai
data-ai
llm-ai
15
agent-creator
Generate a new sub-agent configuration file from a user's description. Use when user says "let's create a new agent", "let's create a new subagent", "create a subagent for...", or asks to build/make a new agent/sub-agent.
cristianoliveira
data-ai
machine-learning
15
sap-hana-ml
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
secondsky
data-ai
machine-learning
15
agent-prompt-evolution
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
yaleh
data-ai
machine-learning
15
retrospective-validation
Validate methodology effectiveness using historical data without live deployment. Use when rich historical data exists (100+ instances), methodology targets observable patterns (error prevention, test strategy, performance optimization), pattern matching is feasible with clear detection rules, and live deployment has high friction (CI/CD integration effort, user study time, deployment risk). Enables 40-60% time reduction vs prospective validation, 60-80% cost reduction. Confidence calculation model provides statistical rigor. Validated in error recovery (1,336 errors, 23.7% prevention, 0.79 confidence).
yaleh
data-ai