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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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Discover MCP servers and inspect their available tools from the command line, then invoke operations with typed arguments or JSON. Use local files, remote endpoints, or configured server names, and return structured output for scripts.
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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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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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Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.
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Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.
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Continue file reading and data processing when read_file or execute_code_sandbox fails. The skill uses run_shell with short Python commands or multiline scripts to inspect files, parse data, and save transformed output.
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Convert SKILL.md files into SSL JSON, a structured format describing invocation conditions, workflow stages, and individual actions. Receive a validation report covering format errors, invalid references, and rejected outputs.
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通过mcporter命令管理连接AI代理与外部工具的MCP服务器。可列出服务器、检查工具输入输出规范、配置连接、完成认证并调用工具。
Prepare WaveTone 2.61 audio analysis workflows by inspecting audio metadata and recording tempo, labels, and intended settings in a JSON project manifest. Launch the Windows application to perform analysis and save its outputs.
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Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.
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Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.
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Build conversations and workflows with the Gemini Interactions API on Gemini Enterprise Agent Platform. Follow Python or JavaScript examples for server-stored conversation history, streaming responses, structured JSON output, function calls, and background agent tasks.
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Helps design and evaluate prompts for more reliable AI responses. Covers clear instructions, input-output examples, task breakdown, and structured formats such as JSON, with guidance on comparing quality, cost, and response time across models.
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Inspect GitHub pull requests, issues, and automated workflow runs using the gh command-line tool. Check PR status, investigate failed jobs, and retrieve structured JSON or API data without opening a browser.
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Turn API documentation, SDKs, or existing scripts into reusable command-line tools for Codex. Build focused read and write commands, stable JSON output, authentication checks, and a companion skill explaining how future sessions should use the tool.
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Choose suitable libraries for converting spreadsheet data and image files, or extracting text and tables from documents. Covers supported format pairs and identifies conversions unavailable in the described environment before work begins.
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使用10个已知漏洞和9个容易混淆的正常样例，比较四种安全检测方式，并将检出率、误报、修复测试、复现、安全风险输出和成本等结果汇总为JSON。
Build and improve Python command interfaces for GUI applications with OpenClaw. The skill guides source analysis, command design, testing, and packaging, including interactive sessions and structured JSON output for agents.
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Manage ChromaDB collections and documents from the command line, and search by meaning rather than exact wording. JSON output supports automated agent workflows, while server checks and an interactive mode help with everyday database operations.
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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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Use ccxt-cli to query cryptocurrency exchanges from the terminal without writing application code. Check prices, balances, and orders, stream live market data, display candlestick charts, and return JSON results for shell scripts.
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Extract identity, ownership, control, and source-of-funds details from investor or client onboarding documents into a structured JSON record. Inventory the submitted documents and flag obvious missing or expired items before passing the information to KYC screening rules.
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Implement or modify GitButler’s but commands using its established structure. It covers typed arguments, input validation, execution, and human-readable, shell, or JSON output, while keeping operations usable from the terminal interface.
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