“json validation”的搜索结果

根据 GitHub 申请议题验证 Spec Kit 社区扩展，添加或更新目录及文档介绍表，提交并推送修改，创建附有验证结果的拉取请求。

• 登记新申请的社区扩展
• 更新扩展版本或介绍信息
• 按发布规则验证申请

Gather market research data from maps, social platforms, and travel sites using Apify Actors, tools for collecting online data. Summarize regional demand, pricing, and consumer insights, with chat results or CSV and JSON exports.

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• Investigating business density and regional demand
• Comparing marketplace prices
• Exploring consumer needs through social posts

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

Design or review command-line tools that AI agents can call. Use 98 proposed rules to define structured output, input validation, error handling, safety controls, and help information, with three levels for gradual adoption.

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• Design a new command-line tool for AI agents
• Adapt an existing tool for agent use
• Standardize interfaces for automated workflows

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

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

Guides creation and validation of custom dotnet new templates from existing projects. Generates a .template.config/template.json that preserves the source project's conventions. USE FOR: creating a reusable dotnet new template from an existing project, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, adding parameters or conditional content to a template you are authoring, validating the template.json you are authoring before publishing, packaging templates as NuGet packages for distribution. DO NOT USE FOR: validating an existing template.json as a standalone task (use template-validation), finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).

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Implement API schema validation using OpenAPI specifications and JSON Schema to enforce input/output contracts and prevent injection, data exposure, and mass assignment attacks.

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Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.

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• Convert raw examples into training-ready JSONL
• Check conversation templates and assistant-only training targets
• Review example boundaries after sequence packing

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

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

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• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Find input files in confirmed workspace folders, save outputs to explicit locations, and check the results against task requirements. Useful in restricted execution environments where familiar folders may be missing or inaccessible.

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• Finding input files in an unfamiliar workspace
• Checking generated spreadsheet sheets and formulas
• Validating CSV or JSON outputs before delivery

Write and review Cloudflare Workers code using current official guidance. Check configuration, asynchronous tasks, large response handling, secrets, and service connections, with findings supported by documentation, code locations, or validation results.

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• Writing a new Cloudflare Worker
• Reviewing Workers code before production use
• Checking wrangler.jsonc and service bindings

TypeScript-first schema validation and type inference. Use for validating API requests/responses, form data, env vars, configs, defining type-safe schemas with runtime validation, transforming data, generating JSON Schema for OpenAPI/AI, or encountering missing validation errors, type inference issues, validation error handling problems. Zero dependencies, compact core (~5kb gzipped; zod/mini ~1.9kb).

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Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.

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检查Perl代码中的输入处理、文件操作、外部命令、SQL及网页输出，依据具体示例和检查清单识别不安全写法，并调整为更安全的实现。

• 检查Perl输入验证
• 检查文件操作与命令执行
• 防范DBI查询中的SQL注入

Review Agent-Native Research Artifacts, structured research packages for agents, across six dimensions of research quality. Produce a JSON report with scores, strengths, severity-ranked findings, practical improvements, and an overall acceptance or rejection recommendation.

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• Reviewing an ARA before publication
• Checking whether evidence supports research claims
• Assessing experimental methods and research records

Add a new safe-output message type to gh-aw, connecting configuration validation, Go parsing, and JavaScript rendering. Follow the required updates through tests, exports, documentation, and generated action bundles so the message reaches workflow output.

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• Add a new safe-output message type to gh-aw
• Support custom templates with placeholders
• Test default and customized message output

TypeScript discipline for any JavaScript/TypeScript project (frontend + backend): strict mode, type design, generics, narrowing, error types, module resolution, tsconfig hygiene. Apply when the project has `tsconfig.json` and `typescript` in devDependencies. Stack-agnostic — referenced by every JS/TS framework plugin in the marketplace. Use this skill to: - Write types that catch bugs at compile time, not runtime. - Use generics, conditional types, and discriminated unions correctly. - Avoid `any`, `unknown`, and unsafe casts. - Match the project's tsconfig strictness level. - Type third-party libraries (with @types/* or declaration files). Do NOT use this skill for: - Plain JavaScript projects (no tsconfig.json). - Framework-specific type idioms (React component props, Vue defineProps, Angular signals — those live in framework plugins' own conventions skills). - tRPC/Zod runtime-validation specifics — handled by validation libs at the boundary.

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Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

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Generate Zod v4 TypeScript schemas from OpenAPI (openapi.yaml / openapi.json) definitions. This skill exists because npm packages like openapi-zod-client do not support Zod v4 — Claude reads the YAML/JSON directly and writes the conversion. Use this skill whenever the user wants to convert OpenAPI/Swagger definitions to Zod schemas, generate TypeScript types from API specs, or create validation schemas from openapi.yaml. Trigger on keywords: 'openapi', 'swagger', 'zod', 'API schema', 'yaml to types', 'schema generation', 'validation generation'. OpenAPI定義からZodスキーマを生成するスキル。openapi.yamlから型を作りたい、APIスキーマをTypeScriptの型にしたい、Zodバリデーションを作りたい、といったリクエストで使う。「openapi」「swagger」「zod」「APIスキーマ」「yamlから型」「スキーマ生成」「バリデーション生成」などのキーワードが出たら積極的にこのスキルを使うこと。

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Guides creation and validation of custom dotnet new templates. Generates templates from existing projects and validates template.json for authoring issues. USE FOR: creating a reusable dotnet new template from an existing project, validating template.json files for schema compliance and parameter issues, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, packaging templates as NuGet packages for distribution. DO NOT USE FOR: finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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