python-telegram-bot
python-telegram-bot library for building Telegram bots. Use for handlers, callbacks, inline keyboards, conversations, and bot commands.
python-telegram-bot library for building Telegram bots. Use for handlers, callbacks, inline keyboards, conversations, and bot commands.
Create or update the feature specification from a natural language feature description. Only invoke when user explicitly requests to create a specification, spec, or feature specification.
Use when "DSPy", "declarative prompting", "automatic prompt optimization", "Stanford NLP", or asking about "optimizing prompts", "prompt compilation", "modular LLM programming", "chain of thought", "few-shot learning"
Amazon Bedrock Prompt Management for creating, versioning, and managing prompt templates with variables, multi-variant A/B testing, and flow integration. Use when creating reusable prompt templates, managing prompt versions, implementing A/B testing for prompts, integrating prompts with Bedrock Flows, optimizing prompt engineering, or building production prompt catalogs.
Standardizing and formatting datasets for Unsloth, including chat template conversion and synthetic data generation (triggers: chat templates, ShareGPT, Alpaca, conversation_extension, add_new_tokens, standardize_sharegpt, formatting_prompts_func).
A simple skill that should be used to respond to a user when the enter the phrase "hello world".
Create a new AFI skill in afi-skills from a canonical template, including SKILL.md and initial eval stubs, while keeping domains, risk levels, and determinism aligned with existing patterns.
Use when you notice a pattern that should be in the config. Analyzes current setup, researches best practices, and proposes specific config changes. Also use when asked to improve agent behavior or workflows.
Assembles relevant context for agent spawns with prioritized ranking. Ranks packages by relevance, enforces token budgets with graduated zones, captures error patterns for learning, and supports configurable per-agent retrieval limits.
Build new agent skills. Use when creating diagnostic frameworks, CLI tools, or data-driven generators that follow the established skill patterns.
Orchestrate adversarial player-coach loop for high-quality code synthesis. Based on dialectical autocoding methodology.
Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.
Enterprise-grade Context7 MCP integration patterns for language-specific documentation access with real-time library resolution and intelligent caching
Creates and maintains Agent Skills with effective triggers and progressive disclosure. Use when user requests to create a skill, generate a SKILL.md, build custom capabilities, or mentions "create skill", "new skill", or "skill configuration".
Use this skill for requests related to LangGraph; fetch relevant documentation first to provide accurate, up-to-date guidance.
Google Gemini API for AI/ML applications. Use for text generation, image understanding, video processing, function calling, embeddings, and multimodal AI capabilities.
Implement code using sonnet model with full main context access
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.
Browser automation and inspection for AI agents via WebSocket