“execution”的搜索结果

Design tools that AI agents can choose and use correctly. Define clear descriptions, inputs, and outputs, manage large search results, and plan approval checks, isolated execution environments, and interfaces for collaboration between agents.

中文简介正在准备中。当前显示英语简介。

• Reviewing the scope of tools for an MCP server
• Improving tool descriptions and input examples
• Designing pagination and truncation for search results

Build quantum circuits with Qiskit, test them locally, and prepare them for IBM quantum hardware. The skill covers hardware-specific circuit conversion, sampling, expectation values, and analysis of measurement results, uncertainty, and resource usage.

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• Building and simulating quantum circuits locally
• Adapting circuits to IBM quantum hardware
• Comparing sampled results and expectation values

Build and operate bioinformatics workflows on Latch using Python, Nextflow, or Snakemake. Configure computing resources and input forms, move data, connect Registry records, and register, debug, launch, and monitor analysis runs.

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• Create and register Python analysis workflows
• Package Nextflow or Snakemake pipelines for Latch
• Connect Latch Data and Registry records

Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.

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• Transfer and organize genomics data on DNAnexus
• Validate app configuration and build applets
• Import WDL, CWL, or Nextflow workflows

Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.

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• Rank candidate regulator–target gene relationships
• Compare GRNBoost2 and GENIE3 results
• Check network stability across seeds and resampled data

Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.

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• Develop research plugins with defined inputs and outputs
• Run data preparation, training, prediction, and backtests
• Investigate failed tasks through status and logs

Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.

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• Building a Java app with reusable AI agents
• Managing conversation threads and messages
• Checking execution states and handling timeouts

Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.

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• Designing an agent's planning and execution loop
• Breaking a complex goal into manageable tasks
• Reviewing self-correction and reliability patterns

Store and retrieve execution output, model responses, reports, and operation receipts in YYLO Ledger. Choose storage and retention settings, attach provenance describing the source, and verify immutable evidence using record identifiers and content digests.

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• Preserving test reports and execution output
• Recording AI responses with their source information
• Storing operational plans and contracts as evidence

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

Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.

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• Review defenses against malicious instructions
• Isolate tool execution and restrict permissions
• Assess data leakage risks between tenants

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

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.

中文简介正在准备中。当前显示英语简介。

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

Explore unfamiliar code with GitNexus to understand project structure and execution flows. Find related functions, inspect their callers and callees, and read implementation files to explain how a feature works, with repository and index freshness information.

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• Understand the structure of an unfamiliar project
• Trace authentication or payment flows
• Find a function's callers and callees

Trace queued jobs from submission to their eventual effects in a CTF sandbox. Compare request-time and worker-time settings, follow retries and failed-message handling, and document the smallest sequence that explains a worker-specific result.

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• Trace a queued payload to its eventual downstream effect
• Compare request-time settings with worker runtime state
• Investigate retries and dead-letter queue handling

Analyze custom virtual machines written in JavaScript by extracting instruction tables, examining constants, and capturing runtime state. Use the findings to reconstruct how obfuscated code executes, including custom runtimes used in risk-control engines.

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• Identify a custom virtual machine in obfuscated JavaScript
• Extract and classify virtual machine instructions
• Trace exported functions and their execution flow

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

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• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Investigate slow SQL using execution plans, which show how a database processes a query. Review indexes, filters, joins, and maintenance practices through PostgreSQL examples to identify bottlenecks and plan changes to queries or database structure.

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• Investigating slow SQL queries
• Reviewing index choices and column order
• Reading EXPLAIN execution plans

Test Temporal workflows in Python with pytest. Use time skipping to avoid long waits, replace activities with controlled test versions, and replay execution histories to check that workflow changes remain consistent with previous runs.

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• Testing long-running workflows without waiting
• Testing workflow logic with mocked activities
• Checking workflow changes against execution histories

Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.

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• Reviewing an MCP server before release
• Checking authentication and validation on a network-exposed server
• Auditing tool code for unsafe execution paths

Update GitHub Actions when workflow logs warn about deprecated runtimes. The skill guides compatible version selection, pins actions to exact commits, preserves existing workflow behavior, and checks execution results and outputs after the upgrade.

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• Addressing deprecated runtime warnings
• Upgrading actions while preserving workflow behavior
• Pinning action versions to exact commits

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

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• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Turns vague development requests into reviewed plans through planning, architecture, and quality-review agents. It compares alternatives, defines testable acceptance criteria, and records decisions, stopping before implementation unless execution has been explicitly approved.

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• Defining the scope of a vague development request
• Comparing implementation approaches before coding
• Planning migrations or security-sensitive changes

Turn a vague idea into a written specification through focused questions. The skill scores clarity across goals, constraints, and success criteria, explores hidden assumptions, and requires explicit approval before moving from planning to execution.

中文简介正在准备中。当前显示英语简介。

• Clarifying a vague idea before implementation
• Defining goals, constraints, and acceptance criteria
• Checking requirements across multiple project components