3231 个 · 按相关度排序
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
中文简介正在准备中。当前显示原文说明。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
中文简介正在准备中。当前显示原文说明。
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
中文简介正在准备中。当前显示原文说明。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
中文简介正在准备中。当前显示原文说明。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
中文简介正在准备中。当前显示原文说明。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
中文简介正在准备中。当前显示原文说明。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context.
中文简介正在准备中。当前显示原文说明。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
中文简介正在准备中。当前显示原文说明。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
中文简介正在准备中。当前显示原文说明。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
中文简介正在准备中。当前显示原文说明。
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
中文简介正在准备中。当前显示原文说明。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
中文简介正在准备中。当前显示原文说明。
用于以Solid 2实现、迁移、审查和测试OpenClaw界面，涵盖状态管理、迁移期间与Lit的共存、菜单与对话框、性能检查及界面测试。
核对OpenClaw常规版与extended-stable版的发布位置、分发产物来源、测试证据和Gateway实际运行情况，判断版本是否已完整发布并可运行。
通过真实用户账号在Telegram测试服务器上操作OpenClaw，记录回复、编辑、删除、回应和输入状态，验证用户可见的行为，并用实际客户端截图检查显示效果。
用于编写和修改OpenClaw在Docker中的端到端测试，结合模拟服务器与真实服务，核查实际发送的请求、图像理解结果及用户最终看到的聊天回复。
使用REA分析已发布的应用、二进制文件、软件包和固件，解释功能并比较版本差异。结合反编译与运行时观察，区分证据、推断和未知事项。
通过复现步骤、日志和近期更改，调查缺陷、测试失败及异常行为的原因。按证据推进假设验证、修复和修复后的测试，逐步确认问题根源。
帮助设计用法精简、内部集中处理复杂逻辑的模块。通过统一术语和判断标准，评估模块是否易用、实现替换位置是否合适，以及是否便于测试。
按照先写测试的流程编写Rust单元、集成和异步测试，涵盖生成多样输入、替换依赖、性能基准测试与覆盖率检查，用于验证行为并补充未测试部分。
使用pytest设计和编写Python测试，复用准备与清理步骤，模拟外部服务，以多组输入验证行为，并测量代码覆盖率、找出尚未验证的部分。
为Kotlin代码先编写测试，使用Kotest和MockK验证行为与依赖交互，涵盖异步测试、生成输入的属性测试及Kover覆盖率检查。
分别衡量实时界面与数据推送的延迟、数据新鲜度和待处理任务，梳理事件发生到屏幕显示的完整路径，逐段改进，并在运行环境中验证结果。
协助将Python应用发布为Windows安装包，涵盖Nuitka编译、无用文件清理和DLL体积分析，并通过Inno Setup制作兼顾体积与启动速度的安装程序。