'json parsing' 검색 결과

Design and review API contract tests, which check agreed request and response behavior. Examine schemas, server output, and client parsing to identify compatibility problems, reproduce failures locally, and report gaps in test coverage.

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• Checking client impact before changing an API response
• Investigating client and server disagreements at runtime
• Reproducing compatibility failures with local test data

Turn Git history into release notes and recommend the next version based on commit types and breaking changes. Check commit formatting, filter changes by package, and generate Markdown or JSON for automated release workflows.

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• Prepare release notes before publishing a version
• Determine the next version from commit history
• Check commit message formats before merging

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

Inspect files with Apache Tika to identify their actual format, embedded content, revision traces, and risk indicators. Save extracted evidence and file hashes while distinguishing recorded claims from observed structure, without asserting authorship or validating signatures.

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• Triaging suspicious files and misleading extensions
• Reviewing recorded authors, dates, and creating applications
• Comparing earlier PDF revisions with the final version

Claude Code session log schema — JSONL record types, message structure, tool call/result pairing, subagent file locations, team session layout, task/plan/team configuration paths. Use when parsing ~/.claude/projects/**/*.jsonl files, writing PostToolUse hooks that measure response sizes, building session analyzers, or any agent that reads or queries Claude session transcripts.

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Parse Claude Code JSONL session logs from ~/.claude/projects/ for tool call inventory, token costs, error detection, subagent traces, and compaction detection

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Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha

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Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy operations.

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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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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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Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".

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PDF 분석, 텍스트 추출, 이미지 분석 결과를 파일 내용에 따라 저장하고 재사용하도록 설계합니다. 파일을 옮기거나 이름을 바꿔도 재사용하며 내용이 바뀌면 다시 처리합니다.

• PDF 텍스트 추출 결과를 재사용합니다
• 이동한 파일의 처리 결과를 재사용합니다
• 기존 처리 함수에 캐시를 추가합니다

Extract website content into Markdown, HTML, or plain text with Scrapling. Handle JavaScript-rendered pages and anti-bot challenges, or write Python crawlers that follow links, collect structured data, and resume interrupted runs.

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• Save article text as Markdown
• Extract content loaded by JavaScript
• Retrieve pages with anti-bot protections

Analyze proxy, DNS, and firewall traffic logs to uncover unapproved SaaS and cloud services. Group usage by domain, compare it with approved applications, and produce a report with traffic volumes, user counts, risk scores, and remediation recommendations.

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• Finding unapproved SaaS and cloud services
• Measuring traffic volumes and users by service
• Prioritizing services by potential data leakage risk

Find source files with no matching test references by parsing code without building or running tests. Produces a source-to-test map and suggested test locations, helping plan test additions across supported programming languages.

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• List source files with no matching tests
• Choose locations for new test files
• Check source-to-test pairing after adding tests

Guide gh-aw release preparation and assess whether command-line changes break existing usage. The reference covers changesets, which record release changes, version previews, release steps, firewall log classification, and summaries of Go module usage.

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• Preview a release version and changelog
• Assess command-line compatibility and changeset types
• Interpret allowed and blocked traffic in firewall logs

Find scientific papers in PubMed by turning a research question into targeted search terms and synonyms. Collect titles, authors, abstracts, and identifiers into a structured paper list ready for relevance evaluation.

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• Finding an initial paper set for a research question
• Searching for methods, compounds, or diseases
• Filtering papers by date, journal, or author

Turn source code imports and folder structure into diagrams for understanding a project, reviewing dependencies, or preparing documentation. Supports Python, JavaScript, TypeScript, Go, and Java, with Mermaid text output and optional SVG images.

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• Understand the structure of an unfamiliar project
• Review module dependencies during code review
• Create architecture diagrams for technical documentation

Extract Markdown and metadata from PDFs, Office documents, emails, archives, and other formats using Apache Tika. Keep attachments and embedded items separate in structured output, detect parsing problems, and read selected portions without overwhelming the agent's context.

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• Extracting readable content from Office documents
• Reading emails together with their attachments
• Checking file types, languages, and metadata

Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

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Read Microsoft Excel (.xlsx) files robustly with `openpyxl` (or `pandas`). Covers multi-sheet workbooks, header rows, empty cells, merged cells, comma-separated list cells, and converting a sheet to a list-of-dicts the rest of your code can consume. Use when a task input or reference document is an `.xlsx` file rather than JSON/CSV.

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Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the data.

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Use when the user mentions Foundation Models review, on-device AI audit, LanguageModelSession issues, or @Generable/guardrail problems — including missing availability checks, manual JSON parsing, and guardrail handling.

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Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.

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