session-management
Context preservation, tiered summarization, resumability
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Context preservation, tiered summarization, resumability
OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration
AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
Microsoft Teams bots and AI agents - Claude/OpenAI, Adaptive Cards, Graph API
Guide for creating effective agent skills. Use when you want to create a new skill, improve an existing skill, or learn best practices for skill development. Helps codify learned patterns into reusable, discove...
Always-on rule-oriented guidance for claude-plugin agents. Use to align behavior, tool usage, and model-specific defaults (GPT-5.2-code vs Opus 4.5) while avoiding deprecated bd/cass references.
Multi-agent coordination patterns for OpenCode swarm workflows. Use when working on complex tasks that benefit from parallelization, when coordinating multiple agents, or when managing task decomposition. Do NO...
Multi-agent coordination patterns for OpenCode swarm workflows. Use when working on complex tasks that benefit from parallelization, when coordinating multiple agents, or when managing task decomposition. Do NO...
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or to...
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Execute plan files by launching multiple parallel subagents to complete tasks simultaneously. Triggers on explicit "/parallel-task" commands.
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
Create comprehensive, phased implementation plans with sprints and atomic tasks. Use when user says: "make a plan", "create a plan", "plan this out", "plan the implementation", "help me plan", "design a plan",...
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge in...
Add a new language model to the Giselle codebase. Use when the user wants to add, register, or integrate a new LLM model (OpenAI, Anthropic, Google) into the system.
Use when starting any iOS/Swift conversation - establishes how to find and use Axiom skills, requiring Skill tool invocation before ANY response including clarifying questions
Use when deploying custom ML models on-device, converting PyTorch models, compressing models, implementing LLM inference, or optimizing CoreML performance. Covers model conversion, compression, stateful models,...
Reference — Complete Foundation Models framework guide covering LanguageModelSession, @Generable, @Guide, Tool protocol, streaming, dynamic schemas, built-in use cases, and all WWDC 2025 code examples
Use when deploying ANY machine learning model on-device, converting models to CoreML, compressing models, or implementing speech-to-text. Covers CoreML conversion, MLTensor, model compression (quantization/pale...
Use when first installing Axiom, unsure which skill to use, want an overview of available skills, or need help finding the right skill for your situation — interactive onboarding that recommends skills based on...
Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models, @Generable, LanguageModelSession, structured output, Tool protocol, iOS 26 AI integration.
CoreML API reference - MLModel lifecycle, MLTensor operations, coremltools conversion, compression APIs, state management, compute device availability, performance profiling.
Use when implementing on-device AI with Apple's Foundation Models framework — prevents context overflow, blocking UI, wrong model use cases, and manual JSON parsing when @Generable should be used. iOS 26+, macO...
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.