'inputs' 검색 결과

Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.

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• Running C/C++ fuzzing across multiple CPU cores
• Exploring further after libFuzzer progress stalls
• Monitoring fuzzing progress and stability

Generates cryptographic test vectors, inputs with expected outcomes, by deliberately changing implementation code and finding changes existing tests miss. Verifies new vectors across independent implementations and compares detection rates before and after adding them.

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• Finding coverage gaps in cryptographic test vectors
• Creating invalid inputs that exercise validation checks
• Building test vectors shared across independent implementations

Writes, reviews, and debugs property-based tests, which generate inputs to check general rules. Helps test conversions, parsers, normalization, and state invariants, assess whether existing assertions are meaningful, and distinguish faulty rules from implementation bugs.

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• Testing encode/decode round trips across generated inputs
• Checking normalization and sorting properties
• Reviewing whether existing property tests assert meaningful rules

Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.

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• Choose an analysis workflow for an epigenomics assay
• Check FASTQ inputs and sample-sheet readiness
• Organize control and biological replicate information

Generate tests for a specified file or module using the project’s testing conventions. Cover expected behavior, unusual inputs, boundaries, failures, and external interactions, with independent tests and mock substitutes for external dependencies.

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• Add tests for existing functions or modules
• Check empty inputs and boundary values
• Test failures in APIs, databases, and file operations

Use a starter template to learn how an agent skill is organized and outline its intended use, required inputs, steps, and output format. Replace the placeholder body with your own working instructions before relying on it.

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• Learn the basic layout of an agent skill
• Start drafting a custom skill
• Describe when an agent should select a skill

Build Vue 3 components with TypeScript and Composition API, a way to organize component logic. Define inputs, events, and two-way bindings, manage reactive data, and use built-in components for transitions and state preservation.

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• Build Vue components with TypeScript
• Define component inputs, events, and two-way bindings
• Update displays and react to data changes

Creates bold interface components with thick black borders, vivid colors, sharp corners, and solid offset shadows. Uses shared CSS settings to coordinate cards, buttons, navigation, inputs, and badges, including interaction states and accessibility considerations.

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• Creating cards with bold borders and solid shadows
• Adding hover and pressed states to buttons
• Coordinating navigation, inputs, and badges

Build soft, clay-like interface components using rounded corners and layered shadows. Includes CSS variables and examples for cards, buttons, inputs, and toggles, with guidance for dark mode, readable text contrast, and visible keyboard focus.

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• Designing soft, dimensional cards and buttons
• Giving inputs and toggles a consistent appearance
• Adapting clay-like components for dark mode

Call tools on configured MCP servers, which connect agents to external services, through the mcporter CLI. Inspect available tools and their inputs, retrieve API information, and check connections before deciding how to proceed.

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• Look up a GitHub repository or issue status
• Gather information before assigning work
• Check an API connection after setup

Provide other skills with tools for generating and editing images, analyzing image content, and improving text through external APIs. This foundation passes inputs to backend services without preprocessing and returns results as text or JSON.

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• Adding image generation to another skill
• Editing reference images with written instructions
• Analyzing the contents of multiple images

Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.

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• Add custom steps to an existing build sequence
• Repair dependency settings that replace SDK build steps
• Correct stale results returned by query targets

Build and troubleshoot canvas extensions displayed in the GitHub Copilot app's side panel. Follow guidance for scaffolding, implementing actions and inputs, preserving state, matching app themes, and checking behavior through canvas tools and extension logs.

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• Create a canvas extension for the side panel
• Check actions and input validation
• Investigate extension loading failures

Find test inputs and bypass references for web security checks, including XSS, SQL injection, and access control flaws. Use the guidance to investigate impact and assess whether a finding is suitable for a vulnerability report.

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• Finding test inputs for web vulnerability checks
• Reviewing WAF bypass techniques and response differences
• Tracing user input into risky browser operations

Inspect available APIs in Open-Science and find files, session status, and agent conversations within the current project. Check which host features are available before using them, and trace the inputs behind generated file versions.

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• Check which host APIs are available
• Find generated files and uploaded versions
• Inspect session status and conversation history

Find test inputs and filter bypass examples for web security checks, organized by vulnerability type. Includes URL classification patterns, discovery wordlists, and examples of combining findings to demonstrate concrete security impact.

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• Choosing test inputs for injection vulnerabilities
• Reviewing authentication and MFA bypass patterns
• Classifying URLs and selecting discovery wordlists

Build an initial target profile from a domain, URL, or IP address by collecting availability, ports, services, subdomains, and technology details. Produces factual inputs for known vulnerability lookups and decisions about subsequent security testing.

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• Start a security assessment from a domain, URL, or IP address
• Inventory exposed ports and services
• Identify subdomains and web technologies

Plan and tune training for vision-language models that use MoE, a mixture of expert networks, in Megatron Bridge. Compares FSDP and 3D parallel training, with guidance on memory use, batch sizes, and performance checks using realistic image inputs.

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• Choosing a parallel training approach for a new model
• Investigating training failures and out-of-memory errors
• Comparing performance with realistic image inputs

Predict biomolecular structures and estimate how strongly a ligand binds using Boltz2 NIM. Submit proteins, DNA, RNA, and ligand inputs through NVIDIA's hosted API or local Docker, then save structures and inspect confidence and affinity scores.

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• Predicting protein structures and DNA or RNA complexes
• Providing ligand inputs with SMILES or CCD identifiers
• Estimating binding affinity for one ligand

Create a library-grade Vue composable that accepts maybe-reactive inputs (MaybeRef / MaybeRefOrGetter) so callers can pass a plain value, ref, or getter. Normalize inputs with toValue()/toRef() inside reactive effects (watch/watchEffect) to keep behavior predictable and reactive. Use this skill when user asks for creating adaptable or reusable composables.

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Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values. Use to collect or pull financial/market data, or gather DCF inputs, for a ticker.

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Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.

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Filter degenerate, uninformative inputs before statistical tests: single-sequence alignments, empty files, constant features, zero-variance inputs, all-NaN columns. See nan-safe-correlation for NaN-aware correlation; statistical-analysis for test guidance.

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Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.

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