Total 31,333 skills, AI & Machine Learning has 5078 skills
Showing 12 of 5078 skills
Semantic skill discovery and routing using GraphRAG, vector embeddings, and multi-tool search. Automatically matches user intent to the most relevant skills from 144+ available options using ck semantic search, LEANN RAG, and knowledge graph relationships. Triggers on /meta queries, complex multi-domain tasks, explicit skill requests, or when task complexity exceeds threshold (files>20, domains>2, complexity>=0.7).
Example skill demonstrating the Skills-as-Containers pattern with workflows, assets, and natural language routing. This is a teaching tool showing the complete PAI v1.2.0 architecture. USE WHEN user says 'show me an example', 'demonstrate the pattern', 'how do skills work', 'example skill'
Routes tasks to skills in skill-db and skill-library using semantic discovery. Triggers on specialized skill requirements, domain-specific tasks, or explicit skill requests. Uses skill-discovery, mcp-skillset, and skill-rag-router for semantic matching.
Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.
Manage long-running agent sessions. Use for tracking progress in extended tasks, maintaining context across long sessions, and managing multi-step workflows.
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
Guide for creating MCP servers that enhance LLM reasoning through structured processes, persistence, and workflow guidance. Use when building MCP servers for structured thinking, journaling, memory systems, or other cognitive enhancement patterns.
Use when making predictions or judgments under uncertainty and need to explicitly update beliefs with new evidence. Invoke when forecasting outcomes, evaluating probabilities, testing hypotheses, calibrating confidence, assessing risks with uncertain data, or avoiding overconfidence bias. Use when user mentions priors, likelihoods, Bayes theorem, probability updates, forecasting, calibration, or belief revision.
Setting up Model Context Protocol (MCP) integration between Blockbench and Claude AI for AI-assisted 3D modeling. Use when configuring BlockbenchMCP, connecting Claude to Blockbench, troubleshooting MCP connection issues, or enabling AI-powered model creation and manipulation.
Generate images using AI when user wants to create pictures, draw, paint, or generate artwork. Supports text-to-image and image-to-image generation.
Use when executing implementation plans with independent tasks in the current session - dispatches fresh subagent for each task, reviews once per phase, loads phases just-in-time to minimize context usage
Use when writing instructions that guide Claude behavior - skills, CLAUDE.md files, agent prompts, system prompts. Covers token efficiency, compliance techniques, and discovery optimization.