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Test app interfaces on an Android emulator using ADB, a command-line device tool. Inspect element positions, perform taps and text entry, then check screenshots and React Native logs to verify behavior and investigate layout or interaction bugs.

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• Verify app interfaces after UI changes
• Investigate layout and interaction bugs
• Check feature behavior through automated interactions

Test Android app flows defined in XML by performing taps, swipes, and text entry, then checking screen states. Produce a JSON report with step results, commands, and failure reasons, stopping at the first failure.

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• Verify login flows against an XML test specification
• Test product search and cart navigation
• Investigate UI failures on devices or emulators

Design and audit analytics tracking so recorded actions support business decisions. Define events and conversions, investigate duplicate or missing records, and document tracking rules, traffic sources, consent checks, and validation steps.

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• Designing events around business decisions
• Investigating mismatched purchase counts
• Reviewing traffic source tracking

Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.

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• Designing data pipelines and task dependencies
• Scheduling recurring batch jobs
• Testing Airflow workflows locally

Inspect local AI coding session logs with agenttrace to identify cost spikes, delays, failed tools, and retry loops. Produce readable reports, compare attempts, and configure automated checks for unusual or unhealthy runs.

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• Investigate slow or expensive AI coding sessions
• Review failed runs before retrying
• Compare tool usage and verification across attempts

Organize supplied health records, including sleep, exercise, nutrition, and test results, to describe trends, relationships, and risk factors. Prepare general health information, summaries, and questions for a doctor, with clear limits on medical interpretation.

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• Summarize sleep, exercise, nutrition, and health records together
• Explore changes and relationships in health metrics
• Prepare questions to discuss with a doctor

Turn articles, notes, or scripts into solo podcast MP3s using the paid Beatra service. The referenced package supports a consistent host voice, natural pacing, and pronunciation control, with cost approval and audio review steps.

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• Turning an article into a solo podcast
• Creating a narrated episode from notes
• Producing an MP3 from a finished script

Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.

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• Set team rules before introducing AI coding agents
• Restrict access to credentials and infrastructure files
• Design an isolated environment for agent execution

Convert human-written CLAUDE.md instructions into a structured label format for AI agents. Use it to review lengthy rules, reduce instruction overhead, and prepare prompts for migration between AI tools while preserving the intended rules.

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• Restructuring a lengthy CLAUDE.md
• Reviewing instruction token usage
• Preparing rules for migration between AI tools

Plan AI features for products, covering language model integration, RAG architecture that retrieves information for answers, prompt design, user experiences people can trust, and cost optimization. Use it to guide development decisions for production use.

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• Plan language model integration in a product
• Consider RAG architecture for reference-based answers
• Review prompt design and AI user experience

Investigate failed Agent QA runs using recorded steps, logs, and saved evidence. Classify each failure, explain confidence and supporting findings, and identify the likely area to fix and the next action.

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• Investigating a failed or interrupted Agent QA run
• Reviewing saved evidence and execution logs
• Comparing recurring failures across recent runs

Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.

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• Planning an AI SaaS product for a specific problem
• Designing prompts for product features
• Balancing model choices, costs, and user experience

Search AI and machine learning jobs through AI Dev Jobs MCP, a connection agents use to access job data. Inspect employers and requirements, match roles to skills and preferences, and review available salary and market statistics.

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• Finding remote machine learning jobs
• Discovering companies hiring for AI roles
• Matching jobs to skills and work preferences

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

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• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.

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• Finding bottlenecks in workflows involving multiple agents
• Reviewing task distribution and parallel execution
• Reducing model costs within quality thresholds

Give an AI agent an email address through the AgentMail API for theagentmail.net. Covers account creation, sending and reading messages, retrieving attachments, setting up incoming-email notifications, and checking organization billing balances.

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• Creating an email account for an AI agent
• Retrieving verification emails for authorized service signups
• Sending authorized messages and checking replies

Plan reviews of AI-generated code before running or merging it. Define language-specific checks, tests in isolated environments, and evidence requirements, while recording coverage limits and gaps that prevent approval.

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• Planning reviews of AI-generated code before merging
• Defining language-specific security checks
• Recording test evidence and unresolved gaps

Set up recurring agent tasks using a supported scheduler, cron, or an authorized external timer. Choose a suitable cadence, preserve task state, prevent overlapping runs, define stop conditions, and verify that scheduled work runs as intended.

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• Running agent checks at regular intervals
• Choosing between built-in scheduling and cron
• Receiving monitoring updates when something changes

Design tools that AI agents can understand and call, covering input schemas, clear descriptions, validation, and error handling. The guidance includes JSON Schema, a format for defining inputs, and MCP, a protocol for connecting agent tools.

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• Design inputs for a new agent tool
• Improve tool descriptions that guide model choices
• Review validation and error handling

Configure permission checks for unattended Antigravity CLI runs. The setup combines blocking rules, automatic approval for scoped operations, AI classification for ambiguous calls, and single-use approval tokens to control tool access within a verified workspace.

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• Setting up permission checks for unattended Antigravity CLI runs
• Defining rules for shell commands, file changes, and network access
• Configuring a local model to classify ambiguous tool calls

Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.

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• Choosing an execution loop for a tool-using agent
• Designing memory and recovery for long-running tasks
• Setting tool permissions, runtime limits, and cost caps

Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.

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• Building an AI assistant that answers from company documents
• Improving search with keyword and vector retrieval
• Designing AI agents with multiple specialized roles

Delegate a clearly scoped coding task to Google Antigravity CLI when explicitly requested. Provides a workflow for preparing instructions, reading the implementer’s report, reviewing changes, rerunning project checks, and committing verified work.

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• Delegate a clearly scoped coding task to Google Antigravity CLI
• Prepare an implementation brief with constraints and checks
• Review delegated changes and rerun project checks

Document the tools an AI agent may call, including input rules, caller permissions, execution limits, and retry conditions. Produce a tool register and verification plan for reviewing registrations or MCP routing designs.

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• Defining which tools an agent may call
• Reviewing a proposed tool registration
• Documenting timeout and retry rules