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Continue analysis after repeated tool failures by embedding known data in Python scripts run through run_shell. Save intermediate results as JSON with timestamps and source metadata, creating records that can be checked and reused in later steps.
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Analyze JSON, syslog, or Nginx logs to group similar errors, count occurrences, and identify busy error periods. Filter by severity or time range, then view the report in the terminal or export it as JSON.
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Read active and pending tasks from todos.json, group them by P0–P9 priority, and identify what comes next. Tasks with equal priority are ordered by status and creation time, with JSON output also available.
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将音频文件发送到OpenAI语音转写API，并将结果保存为文本或JSON。可指定模型和语言，也可为发言添加说话人标签，便于区分录音中的不同说话人。
使用 Java 的 tinystruct 实现可从终端和网页调用的功能，按开发流程生成启动脚本、构建 JSON API、保存数据并测试，也提供文件上传、服务器通知和 MCP 工具示例。
Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.
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Turn work history, education, and skills into a complete resume for Reactive Resume. Gather missing facts through conversation and produce JSON, a structured data format, with the chosen template and layout.
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Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.
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Helps integrate YAML output into json-render, turning incoming text into incremental UI updates. Covers prompt generation, streaming parsing, and targeted edits to existing UI specifications through merge, patch, or diff formats.
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Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.
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Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.
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Prepare a ChatGPT Apps submission JSON file by inspecting an MCP server, which exposes tools to AI agents. Generate app details, tool behavior justifications, and test cases, then report review findings and missing output schemas.
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Retrieve gene data, search downloads, and tissue or cell-line pages from Human Protein Atlas. Get concise summaries by default, with raw JSON or HTML saved to a file only when requested.
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Retrieve GTEx associations between a genetic variant and gene expression in individual tissues. Supply one rsID or genome coordinate, and receive JSON with the resolved variant, association rows, counts, and warnings.
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Use ccwf to validate workflow JSON files, preview their steps in a browser, and produce Mermaid flowcharts. Export workflows as skill files for supported agents, or generate the files and launch Claude Code to proceed with execution.
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Imperative guidance for the System.Text.Json APIs added in .NET 11: the built-in `JsonNamingPolicy.PascalCase` naming policy, and the strongly-typed generic `JsonSerializerOptions.GetTypeInfo` and `JsonSerializerOptions.TryGetTypeInfo` metadata accessors (generic overloads that return a typed `JsonTypeInfo`). USE ONLY when the user is targeting net11.0 or later and needs PascalCase JSON property or dictionary-key names without writing a custom naming policy, a strongly-typed generic `JsonTypeInfo` instead of the non-generic `JsonTypeInfo`, or a no-throw way to probe whether a type's serialization metadata is resolved. DO NOT USE when the target is earlier than net11.0, the requested behavior uses an established pre-net11 naming policy, or the user explicitly selected another JSON library.
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通过grizzly在macOS的Bear中创建、读取和追加笔记，按标签查找内容。还可获取JSON格式的笔记内容或标签列表，并在执行前预览操作URL。
结合应用中的实际显示位置和项目术语更新JSON翻译文件，检查缺失翻译及原文改动，保留现有结构、动态值占位符和复数语法，并汇总修改与验证结果供审查。
Retrieve a single snapshot of recorded AI costs, including total spending, model breakdowns, the highest-cost session, and budget usage. Export JSON for scripts and dashboards, or Markdown for a quick review.
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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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Review PostgreSQL queries and data designs using execution plans, which show how queries run, and targeted index recommendations. Get guidance on JSONB, arrays, full-text search, analytical queries, extensions, and routine database maintenance.
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Convert Simplicité text logs into structured JSON while keeping only the fields needed for troubleshooting. Preserve multiline errors and stack traces, and reduce the amount of log content passed to an AI agent for analysis.
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Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.
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Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.
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