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agent-observability
strategies for agent observability (logging, tracing, metrics). Use this to instrument agents for debugging, performance tracking, and quality assurance.
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abhishekmmgn AI Skills
GitHub ↗浏览 abhishekmmgn 维护的已发布 AI 技能，比较用途与说明，并通过来源仓库了解安装方式。
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GitHub
mcp-security-governance
security protocols for MCP agents. Use this to prevent Dynamic Capability Injection, Tool Shadowing, and Confused Deputy attacks when connecting to external servers.
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GitHub
gemini-context-security
security protocols for agent context. Use this to implement strict data isolation, redact PII, and prevent memory poisoning or prompt injection attacks.
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GitHub
gemini-prompt-construction
effective prompt structure strategies for Gemini, including Zero-shot, Few-shot, Role, and System prompting. Use this to refine queries for better accuracy and style.
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GitHub
gemini-agentic-react
strategies for implementing the ReAct (Reason and Act) paradigm. Use this to enable Gemini to use external tools, APIs, and perform multi-step research.
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GitHub
registry-architectures
strategies for managing tool and agent assets at scale. Use this to design discovery systems, implement curated lists, and facilitate cross-team reusability through Tool and Agent Registries.
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GitHub
agent-ops
operationalization strategies for agents (AgentOps). Use this to manage internal/external tools, optimize agent "brain" prompts, and handle task decomposition.
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GitHub
gemini-code-engineering
strategies for using Gemini as a coding assistant. Use this for generating new code, debugging errors, translating languages, or explaining complex logic.
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GitHub
production-pipelines
strategies for building automated CI/CD pipelines for agents. Use this to implement "shift left" testing, staged validation, and evaluation-gated deployments.
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GitHub
multi-agent-orchestration
design patterns for coordinating multiple agents. Use this to implement Hierarchical, Diamond, Peer-to-Peer, or Collaborative architectures for complex workflows.
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GitHub
mcp-interoperability
strategies for scalable agent architecture. Use this to solve the "N x M" integration problem, implement dynamic tool discovery, and decouple agents from specific tool implementations.
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GitHub
mcp-fundamentals
core architecture of the Model Context Protocol (MCP). Use this to understand Hosts, Clients, Servers, and the JSON-RPC communication layer.
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GitHub
gemini-advanced-reasoning
advanced logic and problem-solving strategies for Gemini. Use this for complex math, multi-step reasoning, or deep analysis tasks where standard prompting fails.
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GitHub
agent-interoperability-a2a
standardized protocols for agent collaboration. Use this to implement the Agent2Agent (A2A) protocol and Agent Cards to transform isolated agents into a collaborative ecosystem.
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GitHub
mcp-server-capabilities
extended MCP features beyond tools, including Resources (data access), Prompts (templates), and Sampling (server-initiated model calls). Use this to implement rich, two-way agent interactions.
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GitHub
agent-foundations
core cognitive architectures for Gemini agents. Use this to implement reasoning loops (ReAct, Chain-of-Thought) and understand the orchestration layer.
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GitHub
production-operations
managing live agents through the Observe-Act-Evolve loop. Use this to maintain performance, manage unpredictable costs, and strategically improve agents based on production data.
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GitHub
agent-evaluation
methodologies for assessing agent capabilities, tool-use trajectories, and final response quality. Use this to implement automated testing and human-in-the-loop validation for agents.
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GitHub
gemini-session-management
strategies for managing agent sessions, handling conversation history (events), and implementing compaction (summarization/truncation) to prevent context overflow.
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GitHub
agentic-rag
strategies for building Agentic RAG systems. Use this to move beyond static retrieval by using autonomous agents for adaptive source selection, query expansion, and multi-step reasoning.
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GitHub
agent-security-governance
comprehensive security layers for autonomous agents. Use this to implement system instructions as constitutions, multi-stage filtering, and continuous red-teaming.
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GitHub
gemini-structured-output
strategies for forcing Gemini to output valid structured data (JSON/XML). Use this for data extraction, API integrations, and creating machine-readable responses.
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GitHub
agent-development
implementation patterns for Gemini agents. Use this for coding ReAct loops, defining tools in Python, and using frameworks like LangChain/LangGraph.
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GitHub
gemini-memory-lifecycle
strategies for the agent memory lifecycle (Extraction, Consolidation, Retrieval). Use this to implement long-term learning and personalization beyond a single session.
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。