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Audit codebases, infrastructure, AND agentic AI systems for security issues. Covers traditional security (dependencies, secrets, OWASP web top 10, SSL/TLS, file permissions) PLUS agentic security (prompt injection scanning, identity spoofing detection, memory poisoning checks, multi-agent communication audit, OWASP Agentic Top 10). Use when scanning for vulnerabilities, detecting hardcoded secrets, reviewing agent workspace configuration, checking prompt injection vectors, or auditing agent permissions and boundaries.

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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

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Build production-grade AI agents from scratch using patterns inspired by production-grade agentic systems like Claude Code. Covers two audiences — (1) workflow builders who need planner+executor pipelines, human-in-the-loop checkpoints, and multi-agent orchestration, and (2) application embedders who need HTTP/SSE APIs, multi-user session management, and React frontend integration. Use when asked to build an AI agent, create a coding assistant, embed an agent in an app, build an API agent, clone Claude Code, build a CLI agent, design an agent architecture, scaffold an agentic system, or automate a multi-step workflow. Triggers on keywords like agent builder, agent blueprint, coding assistant, CLI agent, agentic tool, claude code clone, agent scaffold, agent architecture, workflow automation, multi-agent, planner executor, SSE streaming, agent API.

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为主要由AI智能体实施的开发工作制定完成条件，拆分可独立验证的任务，并比较实施前后的评估结果；同时按难度选择模型，记录费用、耗时与重试情况。

• 规划交给AI智能体的开发工作
• 将大型变更拆成可验证的任务
• 比较实施前后的评估结果

Create agents that learn from interactions and adapt their behavior over time. Monitor learning progress, review efficiency and accuracy metrics, trigger adjustments from feedback, and share acquired knowledge with other agents through dedicated tools.

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• Create an agent with initial learning settings
• Monitor learning progress, efficiency, and accuracy
• Adjust agent behavior based on feedback

Manage code changes across multiple AI agents and record their operations and outcomes. Use those records to find successful patterns, review past failures, and get suggestions for similar development or deployment tasks.

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• Coordinate code changes across multiple AI agents
• Get deployment suggestions from past results
• Review failures from similar merge attempts

Coordinate background tasks with specialized agents for research, optimization, audits, and more. Use execution history, quality scores, success rates, and latency to guide agent selection, track performance, and check benchmark compliance.

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• Choose specialized agents for different task types
• Review agent quality, success rates, and latency
• Record execution feedback to inform future assignments

Plan and edit a presentation explaining the progression from unstructured Claude Code use to configured engineering workflows. It provides a teaching sequence, proficiency levels, and a TodoApp example connecting project instructions, skills, agents, and automation.

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• Structuring a Claude Code training presentation
• Checking the order of slide concepts
• Explaining workflows through TodoApp examples

Audit a Python pytest suite for missed faults, uncovered code, slow tests, and unstable results. Deeper checks deliberately alter code to measure detection, while proposed test removals receive separate validation. The skill reports findings without changing tests.

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• Find gaps in a pytest suite's fault detection
• Investigate slow tests and setup costs
• Identify intermittent or order-dependent failures

Check whether tests detect faults in changed Python code by deliberately modifying the changed lines. Add tests for missed behavior, confirm which tests fail against the previous code, and report measured evidence alongside any remaining gaps.

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• Verify test coverage for a Python feature or fix
• Add tests for faults the current suite misses
• Confirm new tests fail against the previous code

Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.

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• Plan an agent deployment on AgentCore
• Expose a REST API as an agent tool through MCP
• Configure credentials and access controls

Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.

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• Auditing AI agent integrations in GitHub Actions
• Tracing issue and PR text into agent prompts
• Reviewing sandbox settings and tool permissions

Manage a Binance Web3 wallet through the baw command-line tool. Check balances, transfer or swap tokens, manage limit orders and token permissions, and access prediction markets and DeFi, blockchain-based financial services.

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• Check wallet balances and transaction history
• Send tokens, swap assets, and manage limit orders
• Review and revoke token spending permissions

Build applications with GitHub Copilot SDK using Python, TypeScript, Go, or .NET. Add custom tools, display responses as they arrive, preserve conversations across restarts, and connect external tools through MCP, a protocol for connecting agents to tools.

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• Embed an AI assistant in an application
• Add custom tools for an agent to call
• Display generated responses as they arrive

Set up repeatable security tests for LLM apps with Promptfoo and DeepTeam. Connect them to automated build pipelines, detect renewed vulnerabilities to malicious instructions or data leakage, and compare results across model or prompt changes.

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• Running automated attack tests on every code change
• Blocking merges when security tests fail
• Comparing security across model or prompt versions

Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.

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• Designing a data platform across multiple clouds
• Planning data ingestion and AI serving components
• Preparing infrastructure code and deployment instructions

Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.

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• Design real-time technical guidance using live audio and video
• Plan hazard detection and visual inspection from video streams
• Create cloud architecture diagrams and deployment instructions

Build end-to-end tests that follow user journeys in browsers and mobile simulators or emulators. Guides e2e configuration, AI-driven actions, precise outcome checks, failure investigation, and exploratory bug hunts backed by reproducible tests.

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• Testing browser user journeys with AI-driven actions
• Checking app flows on mobile simulators and emulators
• Investigating failed tests through reports and traces

Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.

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• Reviewing workflows that run AI coding agents
• Tracing issue and PR content into agent prompts
• Checking sandbox settings and user allowlists

Tests whether an AI agent accepts unverified plugins, tools, models, or updates. It checks risks introduced through external dependencies, including unsafe installation sources and missing version controls, and reports confirmed findings with conversation evidence.

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• Checking acceptance of plugins and tools from unknown sources
• Reviewing dependency version pinning and integrity checks
• Testing validation of user-supplied tool manifests

Organize AI agent security findings using the ten OWASP ASI risk categories. Map issues such as leaked credentials or unauthorized tool calls to category IDs, and assign severity levels with suggested remediation timeframes.

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• Classify AI agent security findings
• Assign categories to leaks and unauthorized tool calls
• Organize findings from multiple detection skills

Manage a crypto wallet through the onchainos CLI, a command-line tool. Check balances, send or swap tokens, bridge assets between blockchains, manage limit orders, track transactions, and inspect token approvals and security risks.

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• Check wallet balances and transaction history
• Send or swap tokens
• Move assets between blockchains and track arrival

Build blockchain applications with ChainGPT APIs and SDKs. The skill guides chatbot integration, NFT image creation and minting, smart contract generation and auditing, and crypto news feeds, with starter templates and usage cost estimates.

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• Build a blockchain chatbot
• Create an NFT image generation and minting service
• Add smart contract auditing to CI/CD

Add per-request API payments with x402, a protocol that uses HTTP 402 to request payment. Inspect charges, prepare wallet authorizations, assemble payment headers, and call payment processing services, or generate payment requirements for an API you operate.

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• Accessing an API that requires x402 payment
• Inspecting the amount and recipient before payment
• Adding per-request charges to an API