OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xueyetianya/orc (version 1.0.0)

--- name: "orc" version: "1.0.0" description: "Apache ORC columnar storage format reference. File structure with stripes and indexes, schema types with evolution rules, compression codecs (ZLIB/SNAPPY/ZSTD), orc-tools CLI, Python/Java writer APIs, Hive ACID transactions, Spark integration, and performance tuning with bloom filters." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [orc, columnar, hadoop, hive, spark, data, analytics] category: "data" --- # Apache ORC Apache ORC columnar storage format reference. ## Commands | Command | Description | |---------|-------------| | `intro` | ORC overview, file structure, vs Parquet | | `schema` | Types, complex types, schema evolution | | `compression` | ZLIB/SNAPPY/LZO/ZSTD codecs, ratios | | `read` | orc-tools CLI, Python/Java read APIs | | `write` | Writer APIs, stripe/buffer sizing | | `hive` | Hive integration, ACID transactions | | `spark` | Spark ORC read/write, pushdown | | `performance` | Bloom filters, indexes, vectorized reads |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: realroc/coding-2 (version 0.1.0)

--- name: coding description: 1. 创建网页需要先根据拿到的数据设计数据库结构，需要调用工具在数据库中建立结构。 2.后端接口设计需要考虑现有的数据库结构，接口地址为：https://teamo-dev.floatai.cn/api/engine/generalDataApi，method="post"，入参为{"session_group_id":"$SESSION_GROUP_ID$", "collection_name":${表名}}，返回值为{"code":0, "result": {"data": ${JSON数组}}} 基于上边的接口和字段设计，开发动态html看板。 要求每个图表各自请求一次接口完成渲染，间隔60S轮训接口，实时更新页面。3. 注意将传给你的数据信息插入到创建好的数据库结构中。 --- # Coding ## Overview This skill provides specialized capabilities for coding. ## Instructions 1. 创建网页需要先根据拿到的数据设计数据库结构，需要调用工具在数据库中建立结构。 2.后端接口设计需要考虑现有的数据库结构，接口地址为：https://teamo-dev.floatai.cn/api/engine/generalDataApi，method="post"，入参为{"session_group_id":"$SESSION_GROUP_ID$", "collection_name":${表名}}，返回值为{"code":0, "result": {"data": ${JSON数组}}} 基于上边的接口和字段设计，开发动态html看板。 要求每个图表各自请求一次接口完成渲染，间隔60S轮训接口，实时更新页面。3. 注意将传给你的数据信息插入到创建好的数据库结构中。 ## Usage Notes - This skill is based on the Coding agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: iammiracle/cocreator-content-research (version 1.0.2)

--- name: cocreator-content-research description: "Pure intelligence gathering for social media platforms (TikTok & Instagram). Use when an agent needs to discover trending hooks, analyze a competitor's strategy, or look up a specific creator's profile data. This skill does not generate content or post; it relies entirely on ScrapeCreators to return data for analysis." metadata: { "openclaw": { "emoji": "🔍", "requires": { "bins": ["uv"], "env": ["SCRAPE_CREATORS_API_KEY"] }, "primaryEnv": "SCRAPE_CREATORS_API_KEY", "install": [ { "id": "uv-install", "kind": "bash", "script": "curl -LsSf https://astral.sh/uv/install.sh | sh", "bins": ["uv"], "label": "Install uv (cross-platform via bash)" } ] } } --- # Content Research Skill This skill provides agents with the ability to gather raw intelligence on social media performance (TikTok and Instagram) using the ScrapeCreators API. It does not generate content or interact with posting APIs. ## Prerequisites - `uv` installed - `SCRAPE_CREATORS_API_KEY` set in the environment ## Capabilities ### 1. Broad Content Discovery (Keywords & Hashtags) Use this to find top-performing content. **Agent Instructions for Keyword Search:** - **DO NOT** use the same hardcoded keyword (like "affirmations") every time. - **ALWAYS** brainstorm 3-5 diverse keywords based on the user's app (e.g., if it's an affirmation app, try searching "mindset shift", "daily routine", "self healing", "morning motivation"). - Use time frames (`--date-posted`) to find recent trends, and test different sorting methods. - Use the `--format` filter (`video`, `slideshow`, or `both`) if the user specifically requests only one type of content. - **CRITICAL:** The script returns an `is_slideshow` boolean and a `video_url`. Use this to distinguish between video trends and slideshow trends. ```bash uv run {baseDir}/scripts/keyword-search.py --platform tiktok --type keyword --query "morning routine" --date-posted this-month --sort-by most-liked --format slideshow uv run {baseDir}/scripts/keyword-search.py --platform instagram --type keyword --query "morning routine" --format video ``` ### 2. Competitor Hook Research Use this to analyze specific competitor handles. **Agent Instructions for Competitor Research:** - **ALWAYS** ask the user if they have specific competitors or creator profiles they want you to check before you assume. - If they don't know, brainstorm potential top creators in their niche using the keyword search first, extract their handles, and then run competitor research on them. ```bash uv run {baseDir}/scripts/competitor-research.py --platform tiktok --handles user1 user2 user3 uv run {baseDir}/scripts/competitor-research.py --platform instagram --handles user1 user2 ``` ### 3. Profile Lookup Use this to get raw metric data (followers, following, bio) for a specific creator. ```bash uv run {baseDir}/scripts/profile-lookup.py --platform tiktok --handle <handle> uv run {baseDir}/scripts/profile-lookup.py --platform instagram --handle <handle> ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: unisound-llm/unisound-med-synonym (version 1.0.0)

--- name: med-eval-med-synonym description: 医学术语同义匹配。由调用方提供题目文本或含 `question` 字段的结构化输入，经内部医疗大模型按题干约束生成作答；仅含 `scripts/run.py`，可独立拷贝部署。 metadata: { "openclaw": { "emoji": "🧪" } } --- # 医学术语同义匹配 ## 概述 在多种表述中识别等价或最接近的规范术语，并简要说明匹配依据。 本能力与具体业务请求解耦：平台或调用方如何组织用户输入由集成方决定；此处只约定**如何把题目交给脚本**（见下）。 ## 题目输入（三选一） 1. **`--question`**：直接传入完整题干字符串。 2. **`--input PATH`**： - `.jsonl`：每行一个 JSON 对象，须含非空字符串字段 `question`；可用 `--index` / `--batch` / `--match-id` 选取记录； - `.json`：单个对象，须含 `question`； - 其他扩展名：按 UTF-8 纯文本整文件作为题干。 - 传 `-` 时从 **stdin** 读取一条 JSON（含 `question`）或纯文本。 3. **stdin**（且未传 `--question` / `--input`、且 stdin 非 TTY）：读入 JSON 对象或纯文本，规则同 `-`。 ## 模型 - 默认 `u1-insuremed`，接口 `https://maas-api.hivoice.cn/v1/chat/completions`（可通过参数覆盖）。 ## 用法示例 ```bash # 命令行直接给题 python3 scripts/run.py --question "题干……" --appkey YOUR_KEY # 从任意路径的 jsonl 取一条（与文件名无关） python3 scripts/run.py --input /path/to/items.jsonl --index 0 --appkey YOUR_KEY ``` ## 参数 - `--question` / `--input`：见上，与 stdin 三选一（`--question` 与 `--input` 不可同时使用）。 - `--appkey STRING`：**必填**（`--dry-run` 时除外）。内部医疗大模型鉴权 key。 - `--index`、`--match-id`、`--batch`：仅对 `.jsonl` 生效。 - `--dry-run`：不调用模型，仅输出解析后的题目 JSON。 - `--model`、`--api-url`、`--temperature`、`--timeout`、`--system-prompt`：调模型参数。 - `--output PATH`：另存完整 JSON（批量为 NDJSON）。 - `--text-only`：标准输出仅打印模型答案文本。 ## 输出 默认可解析 JSON：含 `status`、`question`、`answer`、`record_index`、`meta`、`model`、`input_mode`、`input_path`（来自文件时非空）等；`skill` 为本任务标题。 ## 合规说明 输出为模型辅助信息，不构成正式诊疗决策；涉及真实患者数据须先脱敏并遵守院内流程。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guohongbin-git/skill-finder-cn (version 1.0.1)

--- name: skill-finder-cn description: "Skill 查找器 | Skill Finder. 帮助发现和安装 ClawHub Skills | Discover and install ClawHub Skills. 回答'有什么技能可以X'、'找一个技能' | Answers 'what skill can X', 'find a skill'. 触发词：找 skill、find skill、搜索 skill." author: 赚钱小能手 metadata: openclaw: emoji: 🔍 requires: bins: [clawhub] --- # Skill 查找器 v1.0.1 帮助用户发现和安装 ClawHub 上的 Skills。 ## 功能 当用户问： - "有什么 skill 可以帮我...？" - "找一个能做 X 的 skill" - "有没有 skill 可以..." - "我需要一个能...的 skill" 这个 Skill 会帮助搜索 ClawHub 并推荐相关的 Skills。 ## 使用方法 ### 1. 搜索 Skills ```bash clawhub search "<用户需求>" ``` ### 2. 查看详情（含统计数据） ```bash clawhub inspect <skill-name> ``` 或者直接 API 查询 stats： ```bash curl "https://clawhub.ai/api/v1/skills/<skill-name>" | jq '.skill.stats' ``` ### 3. 安装 Skill ```bash clawhub install <skill-name> ``` ### 4. 验证安装（新增！） 安装后必须验证： ```bash # 检查是否安装成功 ls ~/.openclaw/workspace/skills/<skill-name>/SKILL.md # 如果文件存在，说明安装成功 # 如果文件不存在，说明安装失败 ``` ### 5. 检查已安装的 Skill ```bash clawhub list ``` 或者： ```bash ls ~/.openclaw/workspace/skills/ ``` ## 工作流程 ``` 1. 理解用户需求 2. 提取关键词 3. 搜索 ClawHub 4. 列出相关 Skills（含下载量/stars） 5. 推荐最合适的 Skill 6. 安装后验证是否成功 ``` ## 输出格式（优化版） 搜索结果应该包含： - Skill 名称 - 简短描述（中文） - 下载量 - Stars 数 - 是否已安装 ``` 🔍 "时间管理" 搜索结果： 1. time-management-2 描述：有效管理时间，避免过度工作 下载：1,234 | Stars: 15 | ✅ 已安装 2. productivity 描述：生产力系统，包含目标/任务/习惯 下载：5,678 | Stars: 42 | ❌ 未安装 ``` ## 示例 **用户**: "有什么 skill 可以帮我监控加密货币价格？" **搜索**: `clawhub search "crypto price monitor"` **推荐输出**: ``` 🔍 "加密货币价格监控" 结果： 1. crypto-tracker 描述：加密货币价格追踪和提醒 下载：2,345 | Stars: 28 | ❌ 未安装 → 推荐安装 安装命令: clawhub install crypto-tracker 安装后验证: ls ~/.openclaw/workspace/skills/crypto-tracker/SKILL.md ``` ## v1.0.1 更新 - ✅ 安装后验证步骤 - ✅ 搜索结果显示下载量/stars - ✅ 检查是否已安装 - ✅ 中文输出优化 --- *帮助用户发现需要的 Skills 🔍*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gingiris/growth-advisor (version 1.0.0)

--- name: growth-advisor version: 1.0.0 description: Hire a growth advisor - Expert guidance for startup launches, user acquisition, and scaling strategies. author: Gingiris tags: - growth - advisor - consulting - startup --- # Growth Advisor > **Expert growth guidance from someone who's done it.** ## 🏆 Track Record | Metric | Achievement | |--------|-------------| | Product Hunt | 30x Daily #1, 3x Weekly #1 | | GitHub Stars | 0 → 33k (AFFiNE) | | Funding | $10M+ raised | | Countries | 100+ reached | ## 🎯 How I Can Help ### Launch Phase - Product Hunt strategy and execution - GitHub trending tactics - Viral launch campaigns - KOL/influencer activation ### Growth Phase - User acquisition channels - Retention optimization - Community building - Content marketing ### Scale Phase - B2B sales playbook - Enterprise expansion - International growth - Team building ## 💰 Engagement Options **Self-Serve:** - [Free GitHub Playbooks](https://github.com/Gingiris) - [Gumroad Guides](https://gingiris.gumroad.com) — from ¥99 **1-on-1 Consulting:** - 30 min: ¥800 - 60 min: ¥1500 - Monthly: ¥7000 ## 📞 Get Started 👉 **[gingiris.com](https://gingiris.com/zh/services)** --- Keywords: growth advisor, growth consultant, startup advisor, launch advisor, user acquisition consultant, scaling consultant


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

--- name: btc-analyzer version: 1.0.0 description: Fetch live BTCUSDT 15m candles from Binance public API and analyze market direction UP/DOWN/SKIP using EMA20 and RSI14. Use when asked to analyze BTC price direction, get trading signal, or check market trend. metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["python3"] }, }, } --- # BTC Analyzer Fetch real-time BTCUSDT candlestick data from Binance public REST API and compute a directional trading signal based on EMA20 slope and RSI14 momentum. ## When to Use - User asks: "analisa BTC sekarang" - User asks: "BTC akan naik atau turun?" - User asks: "berikan sinyal trading BTC 15 menit" - User asks: "cek market BTC sekarang" ## How It Works This skill runs a local Python script that: 1. Fetches 200 candles of BTCUSDT 15m OHLCV data from Binance public API (no API key needed). 2. Computes EMA20 from closing prices. 3. Computes RSI14 from closing prices. 4. Determines direction based on: price vs EMA20, RSI level, and recent candle slope. 5. Returns a strict JSON object with decision, confidence score, and reasoning. ## Workflow Step 1 — Run the analyzer script via bash tool: python3 ~/.npm-global/lib/node_modules/openclaw/skills/btc-analyzer/analyze.py Step 2 — Parse the JSON output. Step 3 — Present the result to the user clearly, including: - Decision (UP / DOWN / SKIP) - Confidence percentage - Reason (EMA, RSI, slope context) - Last close price - Timestamp ## Output Format The script returns strict JSON: { "decision": "UP", "confidence": 72, "reason": "price above EMA20, RSI 58, bullish slope last 3 candles", "lastClose": 94500.00, "ema20": 94200.00, "rsi14": 58.3, "timestamp": "2026-02-23T00:00:00Z" } Decision values: - UP: bullish signal, consider long - DOWN: bearish signal, consider short - SKIP: no clear signal, stay out Confidence range: 0-100 (higher = stronger signal) ## Signal Logic - RSI < 30: decision = UP (oversold) - RSI > 70: decision = DOWN (overbought) - Price > EMA20 AND slope up: decision = UP - Price < EMA20 AND slope down: decision = DOWN - Otherwise: decision = SKIP ## Error Handling If Binance API is unreachable or returns an error: {"decision":"SKIP","confidence":0,"reason":"API error or network issue","lastClose":0,"timestamp":""} ## Guardrails - Always run the script via bash tool — never fabricate or guess output values. - Do not hardcode prices or decisions. - If script fails, show the actual error message to the user. - This skill uses Binance public API only — no API key or authentication required. - Data is real-time; do not cache or reuse previous results.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaozha87/content-checker (version 1.0.0)

name: content_checker description: 参谋官审核，检查内容质量、纠错 version: 1.0.0 # 参谋官审核 你是专业内容参谋官，严格审核上一步生成的稿件。 审核维度：错别字、语句通顺、逻辑合理、内容完整性、是否符合需求。 输出要求： 1. 明确给出【审核结论】：合格 / 不合格 2. 不合格必须说明具体问题 3. 只输出审核结果，简洁明了


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jingconan/deepvista-openclaw (version 0.1.0-alpha.21)

--- name: deepvista-openclaw description: DeepVista auto-capture and knowledge integration for OpenClaw agents. Automatically saves notable user statements, decisions, and insights to DeepVista knowledge base. Use when the user shares facts about themselves, their work, decisions, or plans. --- # DeepVista Auto-Capture for OpenClaw ## Overview This skill enables automatic knowledge capture from OpenClaw conversations into DeepVista. When the user shares notable information during a conversation, save it to DeepVista without asking for confirmation. ## Prerequisites - `deepvista` CLI installed and authenticated (`deepvista auth status`) - If not authenticated, prompt: `deepvista auth login` ## Auto-Capture Rules ### ALWAYS capture (no confirmation needed): - Personal or professional facts: role, company, team, background - Decisions made or conclusions reached - Key insights, learnings, or observations - Action items, commitments, or plans - Meeting or conversation highlights - Relationships between people (who works with whom, who met whom) - Product/business strategy decisions - Important dates, deadlines, milestones ### NEVER capture: - Passwords, API keys, tokens, or secrets - Pure questions with no factual content - Small talk, greetings, acknowledgments - Commands directed at the agent (e.g., "list my notes") ## How to Capture For single-line facts or short insights: ```bash deepvista notes +quick "<text>" ``` For longer structured content (meeting notes, analyses, multi-point insights): ```bash deepvista notes create --title "<title>" --content "<markdown content>" ``` ## Capture Format Guidelines - Preserve the user's original wording when possible - For decisions: include the decision AND the reasoning - For people: include the relationship context (e.g., "Jing is working with Bruce Wang on a thesis") - For meetings: structure as bullet points with key takeaways - Keep notes concise — capture the essence, not the entire conversation ## Integration with OpenClaw Heartbeat If configured in HEARTBEAT.md, this skill supports periodic knowledge base maintenance: - Search for recent notes and check for duplicates - Suggest merges for similar notes - Flag outdated information ## Checking DeepVista Status Before first capture in a session, verify authentication: ```bash deepvista auth status ``` If token is expired, inform the user: > DeepVista auth expired. Please run: `deepvista auth login` and share the code.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: wzwangyc/all-weather-strategy (version 1.0.0)

--- name: all-weather-strategy description: Calculate asset allocation weights for a portfolio of ETFs based on Risk Parity (All Weather) principles. Includes annualized return and risk metrics. Fully interactive with proactive AI guidance. author: wzwangyc version: 1.0.0 --- # All Weather Strategy Skill (Professional Edition) This skill provides a professional implementation of the **Risk Parity (All Weather)** investment strategy. It is optimized for the OpenClaw marketplace. ## Core Capabilities - **Risk Parity Optimization**: SLSQP-based solver for equal risk contribution. - **Annualized Performance**: Forecasts return and volatility based on historical lookback. - **Proactive AI Guidance**: Instructions optimized for AI agents to interact with humans. ## Default Parameters (Standard Config) - **Amount**: `10,000.0` CNY - **Lookback**: `365` days - **Market Pool**: A mix of Global (S&P 500, Nasdaq) and Domestic (STAR 50, Treasury, Gold) ETFs. ## AI Interaction Logic The AI assistant must act as a financial consultant: 1. **Intro**: "I can generate an All Weather asset allocation for you." 2. **Options**: Offer to use the "Standard Professional Config" or custom settings. 3. **Prompt**: Ask specifically about the investment amount and if they want to modify the ETF pool. ## Usage Example ```python from scripts.engine import AllWeatherEngine # The engine handles all defaults natively engine = AllWeatherEngine() results = engine.run() ``` --- **Copyright © 2026 wzwangyc. All Rights Reserved.**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hshgogogo/series-storytelling-skill (version 1.0.3)

--- name: series-storytelling-skill description: Use this skill to turn a premise, video notes, a one-episode idea, or a long novel into a multi-line long-form series or season outline. Chinese trigger phrases include 长篇小说改故事大纲, 剧本策划, 从点子到故事大纲, 多线叙事, 长剧开发, 小说改编, 影视化改编, 剧集大纲, and 分集策划. homepage: https://github.com/hshgogogo/hua user-invocable: true metadata: {"openclaw":{"emoji":"🎬","homepage":"https://github.com/hshgogogo/hua","always":true}} --- # 长篇剧集创作 ## 何时使用 在用户有以下诉求时使用这个 Skill： - 想把一个单集故事扩展成 6 集、8 集、12 集或更长的剧集 - 想把长篇小说改造成故事大纲、季纲或剧集大纲 - 想搭建“主线 + 副线 + 情感线 + 工具人线”的多线结构 - 想做分集大纲、季纲、人物弧光、季终钩子 - 想做剧本策划或长剧开发 - 手里只有零散灵感、视频笔记、人物关系或一个 premise，需要整理成可执行的大纲 - 想从一个点子直接展开成故事大纲 如果用户只是想润色一场戏、修改单段对白，通常不需要调用整个 Skill。 ## 显式触发词 以下中文请求可以直接视为这个 Skill 的高优先级触发词： - 长篇小说改故事大纲 - 小说改编 - 影视化改编 - 剧本策划 - 从点子到故事大纲 - 多线叙事 - 剧集大纲 - 分集策划 - 长剧开发 与之相近的表达也应该优先触发，例如： - 把小说改成剧集大纲 - 把小说做影视化改编 - 帮我做季纲 - 帮我做剧集大纲 - 帮我做分集策划 - 把短故事扩成多线长剧 ## 最少输入 优先收集以下信息；如果缺少其中一部分，可以先做合理假设再起稿： - 类型：犯罪、悬疑、爱情、成长、家庭、职场等 - 载体：网剧、电视剧、限定剧、短剧 - 体量：总集数、每集时长 - 核心命题：这部剧最终想讨论什么 - 主角与对手：谁主动，谁阻挡 - 续季需求：只做单季闭环，还是要预留第二季 ## 工作流 ### 1. 先写“短篇单元”，再写“长篇系统” 每一集先作为独立短篇成立，至少输出： - 激励事件 - 故事脊椎 - 危机 - 进展纠葛 - 高潮与结局 优先使用 `{baseDir}/templates/episode-sop.md`。 ### 2. 为每条故事线都建立自己的推进逻辑 至少识别以下四类线： - 主线：本季最强目标与最大冲突 - 对抗线：对主线形成持续阻力的调查、追击、竞争或围剿 - 情感/成长线：亲密关系、身份秘密、价值观裂缝 - 工具人线：放大偶然性、制造麻烦、推进剧情转折的辅助线 不要只列线名，要明确每条线的： - 起点状态 - 中段升级 - 危机节点 - 本季高潮 - 阶段性结局 ### 3. 专门设计“线与线的交点” 多线创作的重点不是并排罗列，而是交叉施压。优先寻找： - 共享危机：一条线的危机成为另一条线的推进条件 - 错位信息：不同角色知道不同真相，造成误判 - 高潮交汇：多条线在同一场戏、同一地点或同一事件中爆发 如果一条副线无法影响主线，优先删减或重写。 ### 4. 用“危机 -> 选择 -> 后果”塑造人物弧光 每个关键角色至少要有一条清晰变化链： - 危机逼迫角色表态 - 角色做出带代价的选择 - 选择改变关系、资源或立场 - 新立场反过来影响后续剧情 不要只写人物“变了”，要写清楚“为什么变”以及“变了之后剧情怎么变”。 ### 5. 同时完成单季闭环和续季钩子 - 单季闭环：本季核心问题必须得到阶段性解决 - 续季钩子：结尾提出更大、更深或更危险的新问题 钩子最好来自结构升级，不只是“某人还活着”。 ## 默认输出格式 除非用户另有要求，优先输出为： 1. 一句话 logline 2. 主线/副线/情感线/工具人线概览 3. 角色弧光摘要 4. 分集表 5. 季终闭环与续季钩子 如果用户需要成稿，先交付结构稿，再写详细剧情，避免一开始陷入长文本。 ## 推荐资源 - 单集模板：`{baseDir}/templates/episode-sop.md` - 全季模板：`{baseDir}/templates/series-outline.md` - 进阶参考：`{baseDir}/references/advanced.md` - 案例拆解：`{baseDir}/examples/wolf-story/README.md` ## 质量检查 交付前，快速自检： - 每集是否都有自己的小闭环 - 每条线是否在本季中至少改变过一次状态 - 关键高潮是否由角色选择触发，而不是纯巧合触发 - 季终是否既完成阶段闭环，又留下下一阶段的结构性悬念 - 是否避免对已有作品进行逐字复刻，而是抽取其结构方法


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jinntrance/macos-connectivity-restore (version 0.1.0)

--- name: macos-connectivity-restore description: Restores Universal Control and AirDrop settings on a managed Mac by resetting the relevant managed preference files and setting AirDrop discoverability to Contacts Only. Use when the user asks to reopen Link to Mac or iPad, re-enable Universal Control, unlock AirDrop, restore AirDrop receiving, or make these fixes persist after reboot. --- # MacOS Connectivity Restore ## Purpose This skill restores the two settings we previously repaired on this Mac: 1. Universal Control / `Link to Mac or iPad` 2. AirDrop availability plus AirDrop receiving mode It is designed for managed Macs where these settings are forced by files under `/Library/Managed Preferences`. ## What This Skill Changes Root-level managed preferences: - `com.apple.universalcontrol.plist` - `Disable = false` - `com.apple.applicationaccess.plist` - `allowUniversalControl = true` - `allowAirDrop = true` - `com.apple.NetworkBrowser.plist` - `DisableAirDrop = false` User-level preferences: - `com.apple.sharingd` - `DiscoverableMode = "Contacts Only"` - `com.apple.NetworkBrowser` - `DisableAirDrop = false` ## Scripts - `scripts/restore-root.sh` - Run as root. Fixes the managed preference files under `/Library/Managed Preferences`. - `scripts/restore-user.sh` - Run as the logged-in user. Sets AirDrop receiving mode to `Contacts Only`. - `scripts/install-startup.sh` - Installs a LaunchDaemon and LaunchAgent so these settings are restored automatically after reboot/login. ## Default Workflow When the user asks to reapply these settings: 1. Run `scripts/restore-root.sh <username>` with admin privileges. 2. Run `scripts/restore-user.sh`. 3. Read back the values to verify: - `allowUniversalControl = 1` - `allowAirDrop = 1` - `DisableAirDrop = 0` - `DiscoverableMode = "Contacts Only"` ## Persist Across Reboots When the user asks to make the change stick after every reboot: 1. Ensure this skill has been installed to a stable path under `~/.claude/skills/macos-connectivity-restore`. 2. Run `scripts/install-startup.sh`. 3. Confirm these startup items exist: - `/Library/LaunchDaemons/com.joseph.macos-connectivity-restore.root.plist` - `~/Library/LaunchAgents/com.joseph.macos-connectivity-restore.user.plist` ## Verification Commands ```bash defaults read '/Library/Managed Preferences/com.apple.applicationaccess.plist' 2>/dev/null | rg 'allowAirDrop|allowUniversalControl' defaults read '/Library/Managed Preferences/com.apple.NetworkBrowser.plist' 2>/dev/null defaults read com.apple.sharingd 2>/dev/null | rg 'DiscoverableMode' ```


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