'validation' 검색 결과

Build Node.js, Express, and TypeScript APIs with consistent structure. Separate request handling, business logic, and database access, while following guidance for input validation, error tracking, configuration, and tests when adding features or refactoring existing services.

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• Adding an API endpoint
• Separating business logic from database access
• Standardizing input validation and error handling

Verify Claude Agent SDK build and release scripts without network access. Run command-line workflows in an isolated environment with substitute commands to check version validation, installer handling, and retries without overwriting the developer’s installed Claude binary.

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• Check accepted and rejected CLI version values
• Confirm wheel builds reject invalid version pins early
• Test download failures and retry behavior

Create dictionaries of keywords and identifying byte sequences for fuzzing, or testing with generated inputs. Extract useful entries from code, binaries, or specifications, then configure fuzzers to explore parsing paths beyond initial input validation.

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• Build a dictionary for JSON or XML parser fuzzing
• Add protocol keywords to generated test inputs
• Test image parsers with file signatures and chunk names

Assess enterprise Wi-Fi that uses 802.1X, a system for authenticating individual users or devices. Covers identifying authentication methods, testing rogue access points, reviewing certificate trust and storage, and assessing domain access through recovered credentials.

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• Identify authentication methods on enterprise Wi-Fi
• Review client validation of authentication server certificates
• Assess EAP-TLS certificate storage and issuance

Choose a small validation experiment based on your hypothesis, risks, time, and resources. Compare five methods, from technical checks to user interaction tests, and define pass, fail, and learning criteria before investing in a larger build.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a validation method for a risky idea
• Planning an AI feasibility check
• Selecting a test for onboarding task completion

Register AI agents on Starknet as NFTs, unique digital ownership records, using ERC-8004. Manage agent details, collect authorized feedback, and request or review third-party assessments through the standard’s identity, reputation, and validation registries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register an AI agent on Starknet
• Update agent details and check ownership
• Submit feedback and review reputation scores

Clean up DeepChat's specification-driven development documents after implementation and validation. Review feature, issue, and architecture files, remove completed or obsolete material when evidence supports deletion, and preserve maintained specifications while checking references.

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• Remove issue documents after confirming a bug is resolved
• Clear old plans from completed feature work
• Update or remove architecture documents after replacement

Turn an existing .NET project into a reusable dotnet new template while preserving its settings and conventions. Create parameters and conditional content, test generated projects, and prepare the packaging files needed for NuGet distribution.

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• Turn an existing project into a reusable template
• Add template parameters and conditional files
• Validate template installation and generated builds

Publish gh-aw changes as a draft pull request from a GitHub Copilot cloud agent. The workflow checks repository templates, runs required local validation, commits and pushes through runtime tools, and records the validation results in the PR.

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• Publishing validated gh-aw changes as a draft pull request
• Preparing a PR body using the repository template
• Recording passed validation commands in a pull request

Review vulnerability findings before drafting a bug bounty report. Seven validation questions and submission checklists help assess scope, reproducibility, demonstrated impact, duplicate reports, and whether the claimed severity matches the available evidence.

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• Decide whether a finding is ready to report
• Recheck suspected authentication bypasses
• Match severity claims to demonstrated impact

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

Turn validated vulnerabilities into clear bug bounty reports for HackerOne, Bugcrowd, Intigriti, or Immunefi. Platform templates help organize demonstrated impact, reproducible steps, supporting evidence, severity reasoning, and concrete fixes in language reviewers can follow.

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• Draft a report using a platform-specific template
• Write clear titles and impact summaries
• Organize reproduction steps and supporting evidence

Assess session management weaknesses, including sessions that survive logout or password changes, fixed session IDs, and refresh-token reuse. The skill uses two real sessions, response-body comparisons, and control checks to distinguish continued account access from misleading successful responses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether old sessions survive logout
• Verify session invalidation after a password change
• Test refresh-token rotation and reuse detection

Start data collection for wallets already registered in Tres Finance. Choose transaction history plus balances or balances alone, select all or specific wallets, and confirm the request before receiving the collection job ID.

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• Collecting data after registering wallets
• Importing transaction history and current balances
• Refreshing balances for selected wallets

Helps plan dataset splits for machine learning training, validation, and testing. Specifies ratios, random seeds, class balance rules, and time or group constraints, with guidance for handing the split to leakage review and model training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning training, validation, and test partitions
• Preserving class proportions across dataset splits
• Defining time-based or grouped split constraints

Evaluate trained machine learning models using metrics suited to the task. Compare candidates consistently, review decision thresholds and probability estimates, and identify weaknesses in validation before deployment or publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare candidate models using consistent metrics
• Review tradeoffs between decision thresholds
• Check whether predicted probabilities match observed outcomes

Plan experiments and quasi-experiments before analyzing data. Define treatment and comparison groups, choose outcomes, assess design assumptions, and specify follow-up validation when results are weak or contradictory, including rules for continuing, revising, or stopping.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a study design before collecting or analyzing data
• Define treatment groups, comparison groups, and outcomes
• Review assumptions behind a proposed research design

Build automated data checks with Great Expectations and dbt to detect missing values, duplicates, invalid ranges, and stale records. Define data contracts, shared agreements on data structure and quality, and summarize validation results across tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add missing-value and duplicate checks to order data
• Build dbt tests for relationships between tables
• Define shared data requirements between teams

Guide checks of a customized Jetson system image, from file consistency to tests on a flashed device over SSH or UART. Device access is defined, while the broader validation procedure remains a skeleton.

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• Check system image files without a connected device
• Run smoke tests after flashing a Jetson device
• Investigate kernel and module version mismatches

Run an Isaac for Healthcare workflow from robot recordings through dataset preparation, policy training, and checkpoint validation. Uses the maintained pipeline driver, checks the planned commands first, and verifies artifacts and logs at each stage.

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• Running the full recording-to-validation pipeline
• Checking planned stages and artifact paths before execution
• Reviewing datasets, training logs, and checkpoints

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Post-writing SEO validation with pass/fail checklist covering title tag length and keyword placement, meta description quality, heading hierarchy and keyword density, internal/external link audit with anchor text analysis, canonical URL verification, Open Graph meta tags (og:title, og:description, og:image), Twitter Card validation, structured data presence and validity, URL structure optimization, and image alt text presence. Produces prioritized fix list with specific recommendations. Use when user says "seo check", "check seo", "validate seo", "blog seo", "seo validation", "on-page seo", "title tag check", "meta description check", "heading check", "link audit".

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Designs complete single-cell research plans from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a single-cell study — including disease-focused, mechanism-focused, biomarker-focused, translational, perturbation-inspired, or validation-aware projects. It should define the research question, choose the best-fit study pattern, recommend sample grouping logic, suggest reference datasets as examples only, specify the core analysis modules, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, sample metadata, accession numbers, cohort availability, cell-type labels, external validation resources, or literature references. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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Designs discovery, modeling, and validation workflows for prognostic biomarkers in biomedical and clinical research. Always use this skill when the user needs a prognostic biomarker study blueprint rather than a diagnostic test protocol, predictive biomarker design, treatment recommendation, or a completed manuscript. Focus on endpoint family, follow-up horizon, time scale, candidate marker strategy, model-building logic, risk stratification framework, and internal/external validation requirements. Do not invent cohort size, event rate, assay readiness, literature support, or validation access.

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