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Generate stories about institutional outcasts given impossible mandates with minimal resources. Use when you want team dynamics in hostile institutions, David vs. Goliath within organizations, or narrative tens...
Analyze agent-user interaction transcripts to identify context network maintenance needs and guidance improvements. Use after significant agent interactions or to improve context networks.
Transform comprehensive written content into purposeful spoken guidance. Use when adapting for speech, converting to spoken format, optimizing for listening, or creating audio content from written material. Key...
Build new agent skills. Use when creating diagnostic frameworks, CLI tools, or data-driven generators that follow the established skill patterns.
Structure multi-POV stories through catalyst environments. Use when building interconnected narratives, when perspectives need meaningful intersection, or when a shared setting needs to generate distinct storyl...
Generate phonologically consistent constructed languages for fiction. Use when you need naming languages, alien speech, or fantasy tongues without deep linguistics knowledge.
Synthesize new works from extracted functional DNA documents. Use when adapting a source work to a new context, when combining multiple source extractions, or when generating variations that preserve function w...
Teaches how to discover, validate, and add llms.txt documentation sources to the blz CLI. Use when adding documentation to blz, finding llms.txt or llms-full.txt files, validating sources, refreshing content, o...
Apply timeless programming wisdom from "97 Things Every Programmer Should Know" when writing, reviewing, or refactoring code. Use for design decisions, code quality checks, professional development guidance, te...
Self-improving skill system for Pi Agent development. Features self-evolution (accumulate knowledge), self-correction (fix errors automatically), self-validation (verify accuracy), usage feedback (track pattern...
Guide for writing effective system prompts for LLM agents. Use when creating or editing system prompts for applications, agent configurations, or development tools.
Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:
Use Gemini CLI for research with Google Search grounding and 1M token context
Zero Data Retention mode for sensitive/proprietary code - no code stored on OpenAI servers
Multi-model consensus using Karpathy LLM Council pattern for critical decisions
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems,...
Comprehensive cognitive mode management skill for the VERILINGUA x VERIX x DSPy x GlobalMOO integration. Enables automatic mode selection, frame configuration, VERIX epistemic notation, and GlobalMOO optimizati...
AgentDB Reinforcement Learning Training operates on 3 fundamental principles:
AgentDB Vector Search Optimization operates on 3 fundamental principles:
Complex browser automation workflow using claude-in-chrome MCP with mandatory sequential-thinking planning. Use when automating multi-step web interactions, form filling, navigation sequences, or web scraping.
Create AI learning plugins using AgentDBs 9 reinforcement learning algorithms. Train Decision Transformer, Q-Learning, SARSA, and Actor-Critic models. Deploy these plugins to build self-learning agents, impleme...
Modular image generation - supports local SDXL Lightning, OpenAI DALL-E, Replicate, or custom providers
Implement ReasoningBank adaptive learning with AgentDBs 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning...
Autonomous decision-making engine for technical choices in technology selection, architecture patterns, and task ordering. Use when Maestro orchestrator needs to make decisions without human input for choosing...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.