OpenClaw/clawhub-security-signals huggingface dataset:
Skill: likwang/ip-locator-skill (version 1.0.1)

--- name: ip-locator description: 自动获取 IP 地址的归属地信息。支持查询任意 IP 或当前公网 IP。使用 ip-api.com 免费服务，无需 API 密钥。当用户需要查询 IP 位置、网络信息、归属地时触发此技能。 --- # IP 归属地查询技能 ## 何时使用 ✅ **使用场景：** - "查询这个 IP 的归属地" - "我的公网 IP 是多少？在哪里？" - "192.168.1.1 是哪个国家的？" - "帮我看看这个 IP 的详细信息" - 网络安全分析、日志排查 ❌ **不使用场景：** - 内网 IP 定位（内网 IP 无地理位置） - 需要精确到街道级别的位置 - 商业用途（免费版有速率限制） - 批量查询大量 IP（需购买付费 API） ## 快速命令 ### 查询当前公网 IP ```bash curl -s "ip-api.com/json/?fields=61439" ``` ### 查询指定 IP ```bash curl -s "ip-api.com/json/8.8.8.8?fields=61439" ``` ### 简洁输出（仅国家 + 城市） ```bash curl -s "ip-api.com/json/8.8.8.8?fields=country,city" ``` ## 脚本使用 ```bash # 查询指定 IP ./scripts/ip-lookup.sh 8.8.8.8 # 查询当前公网 IP ./scripts/ip-lookup.sh # 批量查询 ./scripts/ip-lookup.sh 8.8.8.8 1.1.1.1 208.67.222.222 ``` ## API 字段说明 `fields=61439` 包含以下信息： - `status` — 查询状态 - `country` — 国家 - `countryCode` — 国家代码 - `region` — 省/州 - `regionName` — 省/州全名 - `city` — 城市 - `zip` — 邮编 - `lat` — 纬度 - `lon` — 经度 - `timezone` — 时区 - `isp` — 网络服务提供商 - `org` — 组织 - `as` — AS 号 - `query` — 查询的 IP ## 输出示例 ```json { "status": "success", "country": "美国", "countryCode": "US", "region": "CA", "regionName": "California", "city": "Mountain View", "zip": "94035", "lat": 37.386, "lon": -122.0838, "timezone": "America/Los_Angeles", "isp": "Google LLC", "org": "Public DNS", "as": "AS15169 Google LLC", "query": "8.8.8.8" } ``` ## 注意事项 - ⚠️ 免费版限制：每分钟 60 次查询，每天 4500 次 - ⚠️ 内网 IP（如 192.168.x.x）无法定位 - ⚠️ 位置信息是估算值，非精确位置 - ✅ 无需 API 密钥 - ✅ 支持 IPv4 和 IPv6 - ✅ 商业使用需购买付费版 ## 相关资源 - 详细字段说明：见 `references/fields.md` - API 文档：https://ip-api.com/docs/


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jesseovo/papercash (version 1.0.0)

--- name: papercash description: "论文全流程辅助: 8源检索、综述生成、查重预检、降AI率、参考文献格式化、Word导出" --- # PaperCash — 论文全流程辅助 Skill > 论文检索、文献综述、写作辅助、查重预检、降AI率、参考文献管理 —— 一个 Skill 全搞定。 ## 触发方式 当用户提到以下关键词时激活本技能： - "论文"、"paper"、"文献"、"查重"、"降重"、"降AI"、"参考文献"、"引用"、"综述"、"摘要" - "/papercash"、"papercash" ## 核心能力 ### 1. 论文检索 (search) 搜索 Semantic Scholar、arXiv、CrossRef、百度学术等 8 大学术数据源，返回评分排序的论文列表。 **调用方式：** ``` /papercash search <研究主题> ``` **示例：** ``` /papercash search 深度学习在医学图像中的应用 ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py search "<主题>" --emit compact` 2. 将输出结果呈现给用户，包含论文标题、作者、年份、引用数、摘要摘录 3. 可选 `--export-docx` 导出 Word 文档 4. 提供后续建议：生成综述、格式化引用、深入某篇论文 ### 2. 文献综述 (review) 基于检索结果，自动生成结构化文献综述。 **调用方式：** ``` /papercash review <研究主题> [--format gb7714|apa] ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py review "<主题>" --format gb7714` 2. 脚本自动完成检索 + 综述生成，输出结构化综述： - 研究背景与现状 - 国内外研究对比 - 研究方法分类 - 研究空白与不足 - 每段附带真实引用 [作者, 年份] 3. 可选 `--export-docx` 直接导出 Word 文档 ### 3. 写作辅助 (outline / expand / polish) 生成论文大纲、段落扩写、学术润色。 **调用方式：** ``` /papercash outline <论文题目> /papercash expand <关键点描述> /papercash polish <待润色文本> ``` **Agent 执行步骤：** - outline: Agent 基于题目和检索到的相关文献，生成符合学术规范的多级大纲 - expand: Agent 将关键点扩展为学术段落，附带文献支撑 - polish: Agent 将口语化表达改为学术用语，保持原意不变 ### 4. 查重预检 (check) 将论文拆成句子，逐句检索学术库，标记高相似度句子。 **调用方式：** ``` /papercash check <文件路径或粘贴内容> ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py check "<文件路径>" --emit json` 2. 呈现结果：标记高风险句子（相似度 > 70%）、中风险句子（50-70%） 3. 对高风险句提供改写建议 4. **重要提示**：告知用户"这是预检工具，不替代知网/维普等正式查重" ### 5. 降AI率改写 (humanize) 通过句式变换、个人观点注入、学术语气调整，降低AI生成内容的检测率。 **调用方式：** ``` /papercash humanize <文件路径或粘贴内容> ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py humanize "<文件路径>"` 2. 返回改写后的文本，并标注修改点 3. 改写策略： - 被动改主动、长句拆短句 - 注入"笔者认为"、"从实践角度看"等个人视角 - 替换AI高频套话（"综上所述"→具体总结） - 增加领域特有细节 ### 6. 参考文献格式化 (cite) 从 DOI/URL 提取文献信息，输出 GB/T 7714、APA、BibTeX 等格式。 **调用方式：** ``` /papercash cite <DOI或URL> [--style gb7714|apa|bibtex] ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py cite "<DOI>" --style gb7714` 2. 返回格式化的引用条目 3. 支持批量输入（多个 DOI 用空格分隔） ### 7. 格式检查 (format) 检查论文是否符合常见高校格式要求。 **调用方式：** ``` /papercash format <Word文件路径> ``` **Agent 执行步骤：** 1. 运行 `python scripts/papercash.py format "<文件路径>"` 2. 返回格式检查报告：字体、字号、行距、页边距、参考文献格式等 ## 配置诊断 ``` /papercash setup # 交互式配置向导 /papercash diagnose # 检查数据源可用状态 ``` ## 数据源说明 | 数据源 | 覆盖范围 | 需要配置 | |--------|---------|---------| | Semantic Scholar | 2亿+论文，全领域 | 无需（免费API） | | arXiv | STEM预印本 | 无需（免费） | | CrossRef | 1.4亿DOI元数据 | 无需（免费） | | 百度学术 | 中文论文元数据 | 无需（公开搜索） | | Google Scholar | 全领域 | 可选（需代理） | | PubMed | 生物医学 | 可选（免费） | | 知网 CNKI | 中文核心期刊 | 需Cookie | | 万方 | 中文学术 | 需Cookie | ## 重要约束 1. **学术诚信**：本工具辅助写作，不鼓励学术不端。生成内容需用户审核修改。 2. **查重声明**：查重预检仅供参考，正式查重请使用学校指定系统。 3. **引用真实性**：所有引用来自真实学术数据库检索结果，但 Agent 应提醒用户核实。 4. **降AI率说明**：改写结果仅供参考，最终文本需体现学生本人的思考和理解。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: makkoncept/browser-bookmarks-cleanup (version 1.0.0)

--- name: browser-bookmarks-cleanup description: Analyze, organize, and clean browser bookmarks on macOS using on-disk bookmark and history files. Detects duplicates, stale bookmarks, tracking parameters, and folder issues. All writes are opt-in with backup and rollback. --- # Browser Bookmark Cleanup Analyze first, organize, then clean up. All writes are opt-in and reversible. > **Supported browsers:** Chrome and Firefox out of the box. Other Chromium-based browsers (Edge, Brave, Arc, etc.) can be handled by pointing to their profile directories, which use the same Bookmarks JSON format as Chrome. ## Workflow 1. Discover browser profiles. 2. Run read-only analysis and share findings. 3. Propose cleanup plan with preview. 4. Get explicit user approval before any writes. 5. Dry-run first, then write with automatic backup. 6. Report changes and how to roll back. ## Hard Safety Rule Never send network requests to bookmark URLs. Analysis uses only local files. ## Commands Discover profiles: ```bash python3 scripts/browser_bookmarks.py discover [--browser chrome|firefox] ``` Analyze bookmarks: ```bash python3 scripts/browser_bookmarks.py analyze --bookmarks "<PATH>" --output /tmp/bookmark-analysis.json ``` Apply plan (dry-run by default, `--write` to commit): ```bash python3 scripts/browser_bookmarks.py apply-plan --bookmarks "<PATH>" --plan /tmp/bookmark-plan.json [--write] ``` ## Analysis Categories 1. Exact and semantic duplicate URLs 2. Tracking-parameter variants (`utm_*`, `gclid`, `fbclid`, etc.) 3. Subdomain and domain concentration 4. HTTP links that should be HTTPS 5. Empty, singleton, deep, and oversized folders 6. Weak bookmark names 7. Old bookmarks by age 8. Never-visited and stale bookmarks (from browser history) ## Approval Gate Before any write: show plan preview, get explicit approval, ask user to close the browser. ## Plan Format See `references/plan-schema.md` for the JSON plan structure. ## Rollback Restore the timestamped backup file created before any write.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: boyd4y/sellersprite-api (version 1.0.0)

--- name: sellersprite-api description: "SellerSprite Product Research — Fetch Amazon market data via SellerSprite API: product research, keyword analysis, competitor lookup, ASIN details, Blue Ocean Index scoring. Triggers: product research, sellersprite, amazon product research, blue ocean, keyword research, competitor analysis, market analysis, asin research, amazon fba" compatibility: darwin,linux metadata: version: 1.0.0 requires: bins: - bun env: - name: SELLERSPRITE_SECRET_KEY description: "SellerSprite Open API secret key (get at https://open.sellersprite.com)" required: false note: "Can also be set via: bunx @teamclaw/sellersprite-cli config set secretKey <key>" packages: - "@teamclaw/sellersprite-cli" --- # SellerSprite Product Research Fetch Amazon market data via SellerSprite API. Each command calls exactly one API endpoint — compose them as needed. ## Quick Start ```bash # Set API key export SELLERSPRITE_SECRET_KEY="your-secret-key" # Browse top categories bunx @teamclaw/sellersprite-cli market # Research products by keyword bunx @teamclaw/sellersprite-cli product --keyword "wireless earbuds" # Get ASIN details bunx @teamclaw/sellersprite-cli asin --asin B08N5WRWNW # Check remaining quota bunx @teamclaw/sellersprite-cli quota ``` ## Commands | Command | API Endpoint | Description | |---|---|---| | `market` | `/v1/market/research` | Market/category research | | `product` | `/v1/product/research` | Product research by keyword | | `competitor` | `/v1/product/competitor-lookup` | Competitor lookup by ASIN | | `asin` | `/v1/asin/{market}/{asin}/with-coupon-trend` | ASIN details + coupon trend | | `keyword` | `/v1/keyword-research` | Keyword research | | `quota` | `/v1/visits` | Check API quota | | `config` | — | Local config management | ## Options | Option | Applies to | Description | Default | |---|---|---|---| | `--keyword <kw>` | product, keyword | Search keyword | (required) | | `--asin <asin>` | competitor, asin | Target ASIN | (required) | | `--marketplace <code>` | all data commands | Marketplace code | `US` | | `--month <yyyyMM>` | product, competitor, keyword | Query month | latest | | `--page <n>` | market | Page number | `1` | | `--size <n>` | market, product, competitor, keyword | Results per page (max 100) | `20`/`50` | | `--format <format>` | all commands | `text` or `json` | `text` | ## References Detailed specs in `references/` directory: - `references/api-endpoints.md` — API parameters, response fields, computed stats, rate limits - `references/marketplace-codes.md` — Supported marketplaces with currencies - `references/error-handling.md` — Error types, unauthorized quota tips, module mapping


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: solomonneas/s3-threat-modeling (version 1.0.0)

--- name: threat-modeling-expert description: "Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extraction. Use for security architecture reviews, threat identification, and secure-by-design planning." --- # Threat Modeling Expert Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extraction. Use PROACTIVELY for security architecture reviews, threat identification, or building secure-by-design systems. ## Capabilities - STRIDE threat analysis - Attack tree construction - Data flow diagram analysis - Security requirement extraction - Risk prioritization and scoring - Mitigation strategy design - Security control mapping ## Use this skill when - Designing new systems or features - Reviewing architecture for security gaps - Preparing for security audits - Identifying attack vectors - Prioritizing security investments - Creating security documentation - Training teams on security thinking ## Do not use this skill when - You lack scope or authorization for security review - You need legal or compliance certification - You only need automated scanning without human review ## Instructions 1. Define system scope and trust boundaries 2. Create data flow diagrams 3. Identify assets and entry points 4. Apply STRIDE to each component 5. Build attack trees for critical paths 6. Score and prioritize threats 7. Design mitigations 8. Document residual risks ## Safety - Avoid storing sensitive details in threat models without access controls. - Keep threat models updated after architecture changes. ## Best Practices - Involve developers in threat modeling sessions - Focus on data flows, not just components - Consider insider threats - Update threat models with architecture changes - Link threats to security requirements - Track mitigations to implementation - Review regularly, not just at design time


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: boleyn/claw-skills (version 1.0.0)

--- name: xiaolongxia-assistant description: OpenClaw 插件开发助手，输出可运行的插件骨架、安装命令和调试步骤。 --- # OpenClaw 插件开发助手（小龙虾） 你是 OpenClaw 插件工程助手，目标是让用户用最少命令把插件跑起来并可发布。 ## 任务目标 1. 根据用户目标，判断应做 Skill 还是 Plugin（npm 包）。 2. 生成最小可运行插件骨架（含 `package.json`、`openclaw.plugin.json`、入口文件）。 3. 给出一键安装命令和回滚命令。 4. 给出本地调试命令和常见报错排查。 ## 决策规则 - 如果用户只需要 prompt/流程增强，优先建议 Skill。 - 如果用户需要代码执行、工具调用或能力扩展，建议 Plugin。 ## 输出格式 按以下结构输出： - 方案类型：Skill / Plugin - 目录结构： - 关键文件： - 安装命令： - 调试命令： - 发布命令： - 回滚命令： - 风险与排查： ## 约束 - 命令可直接复制执行。 - 对版本号使用 semver。 - 不输出与任务无关的背景说明。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: strategy-awal/strategy-awal (version 1.0.0)

--- name: strategy-awal description: OpenClaw MVP v8.1 — Email Classification & Google Workspace Execution for Awalcom. metadata: { "openclaw": { "emoji": "🦅", "requires": { "bins": ["gog", "gh"] } } } --- # Strategy Awal This skill monitors `[REDACTED_SECRET]`, classifies incoming emails by region (Oman, Qatar, MENA), and executes approved actions via Google Docs, Sheets, and Slides. ## Core Capabilities 1. **Email Monitoring:** Uses `gog` CLI to poll the `[REDACTED_SECRET]` inbox for unread messages. 2. **Contextual Classification:** Automatically categorizes emails based on internal strategy keywords (Oman, Qatar, MENA) stored in `context/email-classification-keywords.json`. 3. **Approval Workflow:** Pushes a summary to Telegram and waits for the Chairman's approval before executing actions. 4. **Google Workspace Automation:** Upon approval, autonomously drafts Google Docs, creates Google Slides, or sets up Google Sheets based on the email context. ## Prerequisites - `gog` configured and authenticated via OAuth with scopes for `gmail`, `docs`, `drive`, `sheets`. - Telegram integration enabled on OpenClaw. ## Workflow Commands - `/check` — Check for unread emails immediately and present a classification summary. - `/classify [region]` — List processed emails mapped to a specific region (e.g., Oman). - Custom Natural Language — Provide instructions directly against the email context (e.g., "Draft a response in Google Docs"). ## Regional Keywords The classification engine uses a weighted heuristic based on the Awalcom strategy: - **Oman:** ARISE, hydrogen, SSEZ, Duqm, Vision 2040. - **Qatar:** QNDS, climate awareness, climate action. - **MENA:** Regional initiatives, IsDB, Arab Fund. See `docs/approval-workflow.md` and `docs/telegram-commands.md` in the repository for full deployment guides and troubleshooting steps.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sky-lv/skylv-package-updater (version 1.0.0)

--- name: skylv-dependency-updater slug: skylv-dependency-updater version: 1.0.0 description: "Auto-checks and updates outdated dependencies. Shows changelogs and breaking changes before updating. Triggers: update dependencies, upgrade packages, check outdated, npm update." author: SKY-lv license: MIT tags: [dependencies, npm, pip, update, devops] keywords: [dependencies, npm, pip, update, upgrade, package] triggers: update dependencies, upgrade packages, check outdated --- # Dependency Updater ## Overview Scans project dependencies and checks for updates, shows changelogs, identifies breaking changes. ## When to Use - User asks to "update dependencies" or "check for updates" - Regular maintenance ## How It Works ### Step 1: Detect package manager package.json -> npm pyproject.toml -> pip Cargo.toml -> cargo go.mod -> go ### Step 2: Check outdated npm: npm outdated --json pip: pip list --outdated --format=json cargo: cargo outdated ### Step 3: Risk assessment Patch (1.2.3 -> 1.2.4): Low risk - auto-update Minor (1.2.3 -> 1.3.0): Medium - show changelog Major (1.2.3 -> 2.0.0): High - show breaking changes ## Output Format Major Updates: express 4.17.1 -> 5.0.0 [BREAKING changes] Minor Updates: axios 0.21.1 -> 0.21.4 [Bug fixes] Patch Updates: debug 4.3.1 -> 4.3.4 [Security patch] ## Update Strategy 1. Show report first - never update blindly 2. Update in stages: patches -> minors -> majors 3. Run tests after each update 4. Commit each update separately


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike007jd/baseline-kit (version 1.0.1)

--- name: baseline-kit description: Generate safer OpenClaw configuration baselines and audit existing config files for exposure, missing controls, and secret hygiene issues. homepage: https://github.com/mike007jd/openclaw-skills/tree/main/baseline-kit metadata: {"openclaw":{"emoji":"🧱","requires":{"bins":["node"]}}} --- # Baseline Kit Generate profile-based OpenClaw configuration JSON and audit an existing config before rollout. ## When to use - You need a starting profile for `development`, `team`, `enterprise`, or `airgapped`. - You want an offline audit for `gateway.bind`, auth rate limits, allowed skill sources, audit logging, backups, or secret-like values. - You need a reviewable JSON artifact without contacting external services. ## Commands ```bash node {baseDir}/bin/baseline-kit.js generate --profile enterprise --out ./openclaw.secure.json node {baseDir}/bin/baseline-kit.js generate --profile development --out ./openclaw.dev.json node {baseDir}/bin/baseline-kit.js audit --config ~/.openclaw/openclaw.json --format table node {baseDir}/bin/baseline-kit.js audit --config ./openclaw.secure.json --format json ``` ## Profiles | Profile | Focus | | --- | --- | | `development` | Faster local iteration with lighter rate limits and shorter retention | | `team` | Shared team defaults with moderate auth protection and audit logging | | `enterprise` | Tighter auth windows, longer retention, and recovery guidance | | `airgapped` | Loopback-only and local-mirror oriented settings | ## Audit checks - `NET_EXPOSURE`: whether `gateway.bind` is loopback-only - `AUTH_RATE_LIMIT`: whether auth rate limiting is configured completely - `SOURCE_RESTRICTION`: whether allowed skill sources are too broad - `AUDIT_LOGGING`: whether audit logging is enabled - `BACKUP_HINT`: whether backup settings are present - `SECRET_HYGIENE`: whether the config tree contains plaintext secret-like values ## Output - Each finding includes a severity, evidence path, recommendation, and compliance tag set. - Compliance tags currently map to `SOC2`, `ISO27001`, and `NIST CSF`. ## Boundaries - This tool audits JSON structure only. It does not enforce runtime policy. - Generated profiles are safer defaults, not a complete configuration management system.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: junwugit/intimate-ch11-conflict (version 1.0.0)

--- name: intimate-ch11-conflict description: 在对话中辅助学习"亲密关系/Intimate Relationships"第 11 章"冲突"。基于 Rowland S. Miller《Intimate Relationships》教材第 6 版。当用户讨论、提问、或尝试应用本章核心概念（interpersonal conflict 人际冲突、dialectics 辩证、triggering events 激发事件、attributional conflict 归因式冲突、negative affect reciprocity 负面情感相互作用、demand/withdraw pattern 要求/退避模式、voice/loyalty/exit/neglect 讨论/忠诚/离开/忽视、accommodation 顺应、volatile/validator/avoider/hostile couples 多变型/确认型/逃避型/敌对型夫妻、integrative agreement 整合式一致）时使用。使用渐进式披露，按需读取 references/ 下的细节，把课本知识自然引入学习对话 --- # Intimate Relationships 第 11 章：冲突 ## 何时启用 当用户出现以下信号时使用本 skill： - 询问"亲密关系里的冲突/争吵是否正常"、"为什么伴侣总会吵架" - 要求解释冲突相关术语：interpersonal conflict, dialectics, attributional conflict, accommodation - 讨论伴侣冲突的来源：批评、无理要求、拒绝、累积的烦恼、社会过敏 - 分析争吵怎样升级：负面情感相互作用、要求/退避模式、恶毒言论 - 询问 Rusbult 的 voice/loyalty/exit/neglect，或 Gottman 的四类夫妻 - 想把某个情侣/夫妻争执场景套进教材框架 - 复习本章研究、数据、经典实验、研究者姓名或年份 - 询问公平争斗、说话者-听话者技术、冲突是否可能有益 不要一启动就把整章内容倒出来。先判断用户是在问定义、模型、研究证据、例子，还是应用场景。 ## 渐进式披露总原则 1. **先问后讲**。除非用户明确要求"系统讲完整章"，先用 1-2 句话确认 Ta 想要定义、模型、研究证据、例子，还是把一个场景套进框架。 2. **最小必要信息**。每轮只给一个能独立理解的 chunk，通常 3-6 句；用户继续追问时再展开。 3. **用课本例子锚定**。优先使用教材里的政治偏好、提前离开聚会、洗碗机建议、三晚做饭洗碗、性暗示被拒、约翰和蒂娜等例子。 4. **层层下钻，不反向展开**。用户问细节时再读取相关 reference；用户只问一个概念时，不主动扩展到所有模型和研究。 5. **中文回答**。用户目前使用中文；术语首次出现时给英文原词，之后可只用中文。 ## 核心骨架 **人际冲突**（interpersonal conflict）发生在一方的动机、目标、信念、观点或行为妨碍另一方时。 冲突不等于愤怒；它可以被隐藏，也可能在当事人没有完全意识到时已经存在。 冲突不可避免有两个根源：伴侣偏好会不一致；亲密关系内部存在持续的辩证张力。 本章的四组辩证张力是：自主性/联系性、开放/封闭、稳定/变化、聚合/分离。 常见激发事件可先分成四类：批评、无理要求、拒绝、累积的烦恼。 归因会放大或缓和冲突：把伴侣行为看成内部、稳定、故意，通常更容易愤怒。 冲突过程可粗略看成：激发事件 -> 避免或介入 -> 协商或升级 -> 某种结束方式。 两种危险循环是负面情感相互作用和要求/退避模式。 Rusbult 的四反应框架按主动/被动、建设性/破坏性区分：讨论、忠诚、离开、忽视。 Gottman 的重点不是"吵不吵"，而是正面、接纳、尊重是否足以压过负面和敌意。 冲突结局从较破坏到较建设可记为：分离、支配、妥协、整合式一致、结构性改善。 ## 引入细节时去读哪个文件 按需读取（Read tool），不要预先全部加载： | 用户问题 / 场景 | 读取 | |---|---| | 术语定义、英文原词、易混概念 | `references/key-terms.md` | | 本章大模型：辩证、Peterson 流程、Rusbult、Gottman、冲突结局 | `references/models.md` | | 冲突为什么常见、哪些人/关系更容易冲突、常见导火索 | `references/conflict-sources.md` | | 升级、恶毒言论、健康影响、要求/退避模式 | `references/escalation-and-health.md` | | 研究者姓名、年份、经典研究、教材数据 | `references/research.md` | | 需要一个课本原例子来解释概念 | `references/examples.md` | | 想把教材用于具体争吵、复盘、沟通建议或常见误解辨析 | `references/applications.md` | ## 教学风格建议 - **苏格拉底式**：先让用户判断一个争执属于哪类激发事件、归因、反应类型或结局，再用教材框架修正。 - **先定位过程阶段**：问"现在是在导火索、归因、升级、协商，还是结局？"这样能避免把所有理论混在一起。 - **易混概念辨析**：重点区分潜在冲突/实际冲突、妥协/整合式一致、忠诚/忽视、逃避型/敌对型、暂停/退避。 - **例子锚点**：解释抽象概念时优先回到教材原例，不要编造情侣故事。 - **把研究证据放在用户需要处**：用户问"凭什么"、"教材怎么证明"、"有哪些数据"时再打开 `research.md`。 ## 范围边界 - 本 skill 仅覆盖 Miller《Intimate Relationships》第 6 版第 11 章"冲突"。 - 如果用户问第 4 章归因、第 5 章沟通、第 6 章满意度、第 7 章应答性、第 12 章权力或第 15 章顺应的完整内容，先说明这些是其他章节，再只用本章出现的钩子简要连接。 - 不虚构研究引用、年份、百分比、实验名称或表格数据；没有在本章文本中出现的细节宁可省略。 - 不把教材学习回答替代心理咨询、危机干预、法律建议或安全计划；若用户描述人身危险，应先建议联系当地紧急资源和可信任的人。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)