'inputs' 검색 결과

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.

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• Register and organize biological datasets
• Validate DataFrame or AnnData metadata
• Standardize annotations with biological vocabularies

Prepare experiments for Ginkgo Cloud Lab by choosing a protocol, organizing required inputs, checking configured pricing, and navigating browser ordering. Covers protein expression, RNA synthesis, stability assays, and setup of measurement methods.

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• Choosing a protein expression and purification protocol
• Preparing inputs for mRNA or circRNA synthesis
• Reviewing stability assays and measurement method setup

Prepare and launch nf-core/pacsomatic workflows for matched tumor and normal PacBio HiFi samples. Generate sample sheets and launch files from unaligned BAM inputs, run local checks, submit cluster launchers, and troubleshoot startup failures.

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• Prepare sample sheets for matched tumor and normal samples
• Review generated launch files with a dry run
• Submit a Nextflow launcher to a computing cluster

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Convert existing scripts, CLI tools, or Crawlee projects into reusable Apify Actors, programs packaged as Docker images. Define JSON inputs and outputs, integrate the SDK, test locally, and deploy to Apify.

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• Turning an existing script or CLI tool into an Actor
• Migrating a Crawlee project to Apify
• Defining JSON inputs and structured outputs

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

Turn existing website workflows into experimental WebMCP tools that agents can call. Create scripts, test cases, and reports to check that tool calls match the interface’s behavior and handle invalid inputs safely.

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• Expose an existing website workflow as an agent tool
• Check that tool calls produce the expected interface changes
• Test invalid inputs, cancellation, and recovery

Capture and analyze network traffic to understand custom protocols, including message boundaries and field meanings. Use the findings to document formats, investigate communication problems, and validate behavior through replay and tests with unusual inputs.

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• Investigating network communication failures
• Understanding proprietary message formats
• Documenting protocol specifications

Build Solidity contract tests with Hardhat and Foundry. Check individual functions, contract interactions, gas usage, and varied inputs, and use a local copy of mainnet state to test against existing contracts.

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• Testing token transfers and expected failures
• Checking edge cases with varied generated inputs
• Testing interactions with existing mainnet contracts

Check that an actual app, API, or command-line tool works by exercising its real entry point with realistic inputs. Collect observable evidence such as outputs, logs, exit codes, or screenshots, and report remaining uncertainty.

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• Checking app behavior through the actual interface
• Verifying API behavior with realistic inputs
• Collecting command-line outputs and exit codes

Use the command line to discover and run tools on MCP servers, which connect agents to external systems. Inspect required inputs, pass JSON arguments, and retrieve results as JSON or raw text for scripts.

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• Listing available MCP servers and tools
• Searching for tools by name
• Checking tool inputs before calling an external API

Investigate failed Power Automate cloud flows through FlowStudio MCP. Inspect each action’s inputs and outputs, trace incorrect data upstream, fix expression or data issues, and replay a previous run to check the repair.

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• Investigate why a cloud flow failed
• Find null values or incorrect field paths in expressions
• Inspect HTTP error response details

Investigate CUDA crashes in SGLang using logs and inputs saved before execution. Inspect tensor shapes, data types, and numerical anomalies, with guidance for separate process logs and additional CUDA debugging tools.

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• Investigating illegal memory access and device-side assertions
• Tracing NaN or Inf values through kernel calls
• Saving inputs before a CUDA crash

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

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• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Screen AI application inputs and historical logs for prompt injection, where malicious instructions try to redirect the model. Combine pattern matching, text structure scoring, and a DeBERTa classifier to flag suspicious content and produce JSON results.

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• Screening chatbot and AI agent inputs
• Checking external text used in retrieval-based AI applications
• Auditing historical interaction logs for injection attempts

Design tools that AI agents can understand and call correctly. Covers JSON Schema, a format for defining inputs, along with clear descriptions, input examples, validation, and error responses that help agents recover from failures.

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• Designing inputs for an AI agent tool
• Improving tool descriptions and input examples
• Reviewing validation and error responses

Prepare for payroll by forecasting cash, drafting overdue invoice reminders to address shortfalls, and checking timesheets for anomalies. Produce a validated payroll worksheet or stage approved inputs in Gusto, leaving final payroll submission to the owner.

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• Checking whether cash will cover the next payroll
• Prioritizing overdue invoices to address a payroll shortfall
• Reviewing timesheet anomalies before payroll

Scope a Claude Managed Agent through six questions about its job, trigger, inputs, actions, success criteria, and recurrence. Produce a validated build sheet with configuration components, deferred features, and an evaluation plan, without requiring an API key.

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• Turn an agent idea into a concrete build plan
• Define success criteria before implementation
• Separate the initial scope from later integrations

Turn user stories and acceptance criteria into detailed test scenarios. Define objectives, starting conditions, user roles, actions, and observable results, including invalid inputs and boundary cases, so a quality assurance team can carry out the checks.

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• Writing test cases from user stories
• Preparing acceptance test scenarios
• Checking invalid inputs and boundary cases

Build AI applications in Python with Genkit, including conversational agents, generation workflows, streaming responses, and tool use. The skill also guides setup and debugging through traces, recorded details of model inputs, outputs, and actions.

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• Build a chatbot that keeps conversation history
• Implement streaming responses and tool calls
• Investigate Genkit import and API errors

Verify Kortix changes with focused tests and real inputs and outputs. This skill guides test placement, API and CLI workflows, browser checks, database verification, and reporting of results, including skipped cases.

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• Verify a bug fix with focused tests
• Check API and CLI behavior through real inputs and outputs
• Test database query conditions

Turn JSON specifications into React interfaces using a catalog of available components. This skill helps connect component implementations, shared data, form inputs, conditional display, and user actions to render interactive screens, including AI-generated designs.

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• Render AI-generated JSON as a React interface
• Define a catalog of reusable UI components
• Connect form inputs to shared data

Build native mobile interfaces from JSON specifications with @json-render/react-native. Use standard or custom components, connect form inputs to application state, and update visible content in response to taps and other interactions.

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• Building mobile screens from JSON specifications
• Connecting form inputs to application state
• Switching tab content in response to taps