Copilot CLIใซๅฆ็ใๅง่ญฒใใในใญใซใใฆใผใถใผใใCopilotใซ่ใใฆใใCopilotใง็ขบ่ชใใฆใใCopilotใซไปปใใฆใใชใฉใจๆ็คบ็ใซไพ้ ผใใใจใใซไฝฟ็จใใใGitHub Copilot CLIใฎๆฉ่ฝใๆดป็จใใฆใใณใใณใใฎๆๆกใGitๆไฝใฎ่ชฌๆใชใฉใๅๅพใใใ
AI governance and responsible AI planning including EU AI Act classification, NIST AI RMF, and AI ethics frameworks
Expert Python developer specializing in Python 3.11+ features, type annotations, and async programming patterns. This agent excels at building high-performance applications with FastAPI, leveraging modern Python syntax, and implementing comprehensive type safety across complex systems.
Implement vector search for knowledge retrieval. Use when adding RAG, semantic search, knowledge base features, or context building for the agent.
Write effective prompts for Task tool sub-agents, slash commands, and system prompts. Covers Claude 4.x prompting patterns, context engineering, output format specification, and parallel delegation. Use when spawning sub-agents, creating slash commands, or writing system prompts.
Create and manage Decision (DEC-NNN) and Assumption (ASM-NNN) records. Use when: documenting non-obvious choices, tracking risky assumptions, explaining rationale. Triggers: "document decision", "create DEC", "track assumption", "@rationale", "why did we choose"
Auto-detect and load minimal context from Skills folders using progressive disclosure, trimming 1400 tokens from system prompt through metadata-first loading. Use when optimizing token usage, managing multiple skills, or preventing context bloat. Load YAML metadata first, then conditionally load full SKILL.md only for matched skills based on task triggers. Achieves 10% accuracy improvement via targeted context. Triggers on "optimize skills", "reduce tokens", "smart loading", "skill efficiency".
DO NOT invoke unless explicitly instructed. Core guidelines for orchestrating tasks with subagents.
[72] CLOSE. Save and restore session context between conversations. Use when ending a session to preserve progress, or starting a new session to restore context. Triggers on "save session", "end session", "preserve context", "handoff", "continue from last time", or when context window is running low.
Create MCP (Model Context Protocol) servers using FastMCP Python SDK. Define tools that AI agents can call to perform task operations. Use when building MCP servers for Phase 3 AI chatbot integration.
ไผ่ฉฑๅฑฅๆญดใไฝๆฅญๅ ๅฎนใใใๅๅฉ็จๅฏ่ฝใชใจใผใธใงใณใในใญใซใ็ๆใพใใฏ่ฟฝ่จใใ
Continuous learning system that monitors all user requests and interactions to identify learning opportunities. Active during: (1) Every user request and task, (2) All coding sessions and problem-solving activities, (3) When discovering solutions, patterns, or techniques, (4) During /retrospective sessions. Automatically evaluates whether current work contains valuable, reusable knowledge and creates new Claude Code skills when appropriate.
Techniques for ensuring LLM responses adhere to strict JSON schemas, utilizing Pydantic models, JSON mode, and schema-based refusals. Triggers: structured-output, pydantic, json-schema, json-mode, llm-response-parsing.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Build AI agents with OpenAI's API. Use when creating tool-calling agents, multi-step reasoning systems, function calling implementations, or autonomous AI workflows. Triggers include "OpenAI agent", "function calling", "tool use", "agent loop", "autonomous agent", or "AI assistant with tools".
Expert TypeScript developer specializing in advanced type system features, generic programming, and type-safe application architecture. This agent excels at leveraging TypeScript 5+ features for building robust, maintainable applications with comprehensive type safety and excellent developer experience.
Reinforcement learning training for CTF-AI. Use when training DQN agents, adjusting hyperparameters, debugging training issues, analyzing rewards, or improving AI performance.
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and self-assessments without modifying their static .md configuration.
