OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ckchzh/gaokao-essay (version 2.0.0)

--- version: "2.0.0" name: Gaokao Essay description: "高考作文生成器。议论文、记叙文、材料作文。提供范文、素材、开头结尾模板。. Use when you need gaokao essay capabilities. Triggers on: gaokao essay." 高考作文助手。议论文模板、素材库、开头结尾、审题技巧、满分作文分析。Gaokao essay assistant with templates, materials, scoring tips. 高考语文、作文素材、议论文、记叙文。Use when preparing for gaokao essays. author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # gaokao-essay 高考作文生成器。议论文、记叙文、材料作文。提供范文、素材、开头结尾模板。 ## Commands All commands via `scripts/essay.sh`: | Command | Description | |---------|-------------| | `essay.sh write "题目" [--type 议论文\|记叙文\|材料作文]` | 生成一篇完整作文（默认议论文） | | `essay.sh opening "主题"` | 生成5个万能开头 | | `essay.sh ending "主题"` | 生成5个万能结尾 | | `essay.sh material "话题"` | 作文素材库（名人名言+事例） | | `essay.sh help` | 显示帮助信息 | ## Usage Agent should run the script and return output to user. Example: ```bash bash scripts/essay.sh write "人生的选择" --type 议论文 bash scripts/essay.sh opening "奋斗" bash scripts/essay.sh material "创新" ``` ## Notes - Python 3.6+ compatible - No external dependencies - All content generated locally from built-in templates --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edwardrodriguez703-design/book-plumber (version 1.0.1)

--- name: book-plumber description: Book plumber services through Lokuli MCP. Use when user needs to find and book plumber. Triggers on requests like "book a plumber", "find plumber near me", or any plumber service request. --- # uook plumuer Book plumber services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "plumber", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-state-machine (version 1.0.0)

--- name: jpeng-state-machine description: "Manage state machines" version: "1.0.0" author: "jpeng" tags: ["state", "machine", "automation"] --- # State Machine Manage state machines ## When to Use - User needs state related functionality - Automating machine tasks - Automation operations ## Usage ```bash python3 scripts/state_machine.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export MACHINE_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zihaowyt5525-max/spss-data-cleaning (version 1.0.0)

# SPSS Data Cleaning Assistant ## Name `spss-cleaner` ## Description 辅助用户进行 SPSS 数据清洗的 AI 工作搭子。支持：缺失值检测与处理、异常值识别、数据类型转换、变量重编码、重复值处理、数据验证、结果导出。适用于问卷调查、实验数据、舆情数据等常见研究场景。 --- ## Capabilities ### 1. 缺失值检测与处理 - 检测各变量的缺失值数量和比例 - 提供多种填补策略：均值填充、中位数填充、众数填充、回归填充、多重插补、冷拔出（cold-deck）、热拔出（hot-deck） - 建议删除缺失比例过高的变量或样本 ### 2. 异常值检测与处理 - 基于 Z-score（|Z| > 2/3/2 可选） - 基于 IQR（1.5倍 IQR 规则） - 基于描述统计（超出均值±3SD） - 提供：删除、替换（Winsorize）、保留选项 ### 3. 数据类型诊断 - 识别数值型、字符型、日期型变量 - 检测格式错误（如日期格式不一致） - 自动转换数据类型 ### 4. 重复值检测与处理 - 全记录重复检测 - 关键字段重复检测 - 保留第一条/最后一条/询问用户 ### 5. 变量重编码 - 连续变量→类别变量（指定切分点） - 逆向编码（5点/7点量表） - 自定义编码映射 ### 6. 数据验证规则 - 范围检验（数值必须在指定区间） - 逻辑检验（如：年龄与学历逻辑一致性） - 唯一性检验（ID 字段不能重复） - 自定义 Python 验证脚本生成 ### 7. 数据清洗报告 - 生成完整的清洗日志 - 记录所有处理操作及理由 - 汇总清洗前后的样本量变化 --- ## Input Requirements 上传以下任一格式的数据文件： - `.sav`（SPSS 原生格式）— 需本地上传 - `.csv`（逗号分隔） - `.xlsx` / `.xls`（Excel） - `.tsv` 并说明研究背景和清洗目标。 --- ## Output ### 清洗后的数据文件 - SPSS `.sav` 格式 - CSV 格式（通用兼容） ### 清洗报告（Markdown） ``` # 数据清洗报告 ## 1. 数据概况 - 原始样本量：N = XXX - 变量数量：K = XX - 清洗日期：YYYY-MM-DD ## 2. 缺失值处理 | 变量 | 缺失数 | 缺失率 | 处理方式 | |------|--------|--------|---------| | XX | XX | XX% | 删除/填补 | ## 3. 异常值处理 | 变量 | 检测方法 | 异常数 | 处理方式 | ## 4. 重复值处理 - 全记录重复：X条 → 保留X条 - 关键字段重复：X条 → 已处理 ## 5. 变量重编码 | 原变量 | 重编码方式 | 新变量 | ## 6. 数据验证结果 - 通过 / 未通过（附具体问题） ## 7. 清洗后数据 - 最终样本量：N = XXX（较原始减少XX条） - 最终变量数：K = XX ## 8. 处理操作日志 [时间戳] 操作描述 ``` --- ## Workflow ### Step 1：上传数据 用户上传数据文件，说明研究背景、核心变量、清洗目标。 ### Step 2：初步诊断 Agent 读取数据，生成： - 数据概况（样本量、变量数、变量类型） - 缺失值报告 - 异常值初筛 - 重复值检测 ### Step 3：方案确认 Agent 提出清洗方案，列出每个问题的处理建议，用户确认或修改。 ### Step 4：执行清洗 Agent 执行清洗操作，记录日志。 ### Step 5：验证与报告 验证清洗后数据，生成报告，用户下载结果。 --- ## Limitations - `.sav` 文件需用户上传到工作区 - 复杂的多重插补建议使用专业 SPSS 插件（如 MICE） - 逻辑检验规则需要用户明确定义 - 不支持权重变量的自动处理 ## Dependencies - Python 3.8+ - `pandas`（数据处理） - `scipy`（统计检验） - `openpyxl`（读取 Excel） - `pyreadstat`（读取 SPSS .sav） - `statsmodels`（可选：回归填充） 安装命令： ```bash pip install pandas scipy openpyxl pyreadstat statsmodels ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhc888007/multi-search-engine-simple (version 1.0.0)

--- name: "multi-search-engine" description: "【国内精简版】多种免费搜索方式在互联网上搜索最新信息，10个国内网站（精简7个国内不能访问的海外网站）" --- # Multi Search Engine 集成 10 个免费搜索引擎。 ## Search Engines - **Baidu**: `https://www.baidu.com/s?wd={keyword}` - **Bing CN**: `https://cn.bing.com/search?q={keyword}&ensearch=0` - **Bing INT**: `https://cn.bing.com/search?q={keyword}&ensearch=1` - **360**: `https://www.so.com/s?q={keyword}` - **Sogou**: `https://sogou.com/web?query={keyword}` - **WeChat**: `https://wx.sogou.com/weixin?type=2&query={keyword}` - **Toutiao**: `https://so.toutiao.com/search?keyword={keyword}` - **Jisilu**: `https://www.jisilu.cn/explore/?keyword={keyword}` - **Ecosia**: `https://www.ecosia.org/search?q={keyword}` - **WolframAlpha**: `https://www.wolframalpha.com/input?i={keyword}` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adchina2025/project-anatomy (version 0.1.4)

--- name: 文件快速扫描 (Project Anatomy) description: 文件快速扫描，减少token消耗。AI 不读文件就能知道内容，避免重复读取浪费 token。 triggers: - 扫描项目 - 文件快扫 - 项目透视 - project anatomy - file index - reduce file reads - project map --- # 文件快速扫描 (Project Anatomy) 受 OpenWolf 启发。扫描项目生成精简文件索引，让 AI 不打开文件就能判断是否需要读取，大幅减少 token 消耗。 ## 命令 ### 全量扫描 ```bash python3 <skill-dir>/scripts/anatomy_scan.py <项目路径> [选项] ``` 选项： - `--format compact|table|summary` — 输出格式（默认 compact 最省 token） - `--incremental` / `-i` — 增量模式，未变文件复用缓存 - `--max-depth N` — 目录深度限制 - `--exclude pattern [...]` — 额外排除规则 - `--output path` — 自定义输出路径（默认 `<项目>/.anatomy.md`） ### 自动注入（会话启动用） ```bash python3 <skill-dir>/scripts/anatomy_inject.py <项目路径> [--max-age-hours 24] [--quiet] ``` 检查索引新鲜度，超过 24h 自动重新扫描，输出索引内容。 ## 输出格式 **compact**（默认，最省 token）： ``` - `src/index.ts` (~180t) — 入口文件 - `src/server.ts` (~520t) — HTTP 服务 ``` **summary**（按目录分组）： ``` ## src/ (5 文件, ~2,400t) - `index.ts` (~180t) — 入口文件 - `server.ts` (~520t) — HTTP 服务 ``` **table**（最详细，含日期）： ``` | 文件 | Token | 描述 | 修改日 | |------|-------|------|--------| | `src/index.ts` | ~180 | 入口文件 | 2026-05-12 | ``` ## 配置 项目根目录可选 `.anatomy.yaml`： ```yaml exclude: # 排除目录/文件 - my_custom_dir - "*.generated.ts" max_depth: 5 # 最大扫描深度 max_file_size_kb: 500 # 跳过超大文件 description_max_chars: 120 # 文件描述最大字符数 ``` ## Startup Integration (OpenClaw AGENTS.md) 已在 AGENTS.md Session Startup 中集成： 1. 启动时读 `.anatomy.md`（文件快扫索引），已知内容不再重复 `read` 2. 超过 24h 自动触发重新扫描： ```bash python3 skills/project-anatomy-0.1.0/scripts/anatomy_inject.py . ``` 3. 扫描输出写入 `.anatomy.md`，后续会话直接复用 **效果**：AI 看索引就能判断是否需要打开文件，避免逐文件 `read` 浪费 token。 ## How to Use 1. 首次扫描项目：`python3 .../anatomy_scan.py /path/to/project` 2. 读文件前先检查 `.anatomy.md` 索引 3. 只在单行描述不够时再 `read` 打开文件 4. 定期用 `--incremental` 增量更新 ## Token Savings 实测数据： - 47 文件目录：索引 ~1,058t vs 全量 ~29,533t → **节省 96%** - 对同一个反复访问的项目效果最显著


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yige666s/shortart-image-generator (version 1.0.9)

--- name: shortart-image-generator description: "Generate images using ShortArt AI platform with three modes: (1) Text-to-image and image-to-image generation for general artwork creation, (2) E-commerce suit images for product photography and Amazon listings, (3) Template-based image generation with customizable parameters. REQUIRES: SHORTART_API_KEY environment variable (get from https://shortart.ai/home). Use when users explicitly request: creating images, drawing, generating pictures, making artwork, product images, suit images, template images, or visual content creation using ShortArt. Triggers on: 生成图片, 画一张, 帮我画, 制作图片, 套图, 生成套图, 主图, 商品主图, 模版生图, 用模版生成图片, 推荐模版, generate image, create image, draw, visualize, suit image, product main image, template image generation, image-to-image, 以图生图." homepage: https://shortart.ai allowed-tools: Bash(python3 *) metadata: {"openclaw": {"emoji": "🎨", "requires": {"env": ["SHORTART_API_KEY"]}, "primaryEnv": "SHORTART_API_KEY"}, "dependencies": {"python": ">=3.7", "pip": ["requests"]}} compatibility: "Requires Python 3.7+, requests library (pip install requests), and SHORTART_API_KEY environment variable" --- # ShortArt Image Generator Unified interface for generating images using ShortArt AI with three specialized modes. ## Authentication Setup `SHORTART_API_KEY` is required. Visit [https://shortart.ai/home](https://shortart.ai/home) and click on your profile picture to get the key. Configure via: **Method 1: OpenClaw Config** ```json { "skills": { "entries": { "shortart-image-generator": { "env": { "SHORTART_API_KEY": "your_api_key_here" } } } } } ``` **Method 2: Shell Environment** ```bash export SHORTART_API_KEY="your_api_key_here" ``` **IMPORTANT:** After setting the environment variable, restart your terminal or run `source ~/.zshrc` to apply the changes. ## Mode Selection Identify user intent and route to appropriate mode: ### Mode 1: General Image Generation **When to use**: User wants to create artwork from text descriptions or reference images **Triggers**: "画一张图", "生成图片", "create image", "draw", "以图生图" **See**: [references/text-to-image.md](references/text-to-image.md) ### Mode 2: E-commerce Suit Images **When to use**: User wants product photography or Amazon listing images **Triggers**: "套图", "生成套图", "组图", "suit image", "Image Set", "Photo Gallery", "A Set of Photos" **See**: [references/suit-image.md](references/suit-image.md) ### Mode 3: Template-based Generation **When to use**: User wants to use templates for posters, social media graphics, or marketing materials **Triggers**: "模版生图", "用模版生成图片", "template image generation", "create from template", "推荐模版", "recommend template", "模版推荐" **See**: [references/template-image.md](references/template-image.md) ## Quick Start 1. Verify API key: `echo $SHORTART_API_KEY` 2. Identify user intent from triggers above 3. Read the corresponding reference file for detailed workflow 4. Execute using scripts in the appropriate subdirectory


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: pors/yc-cold-outreach (version 1.0.1)

--- name: yc-cold-outreach description: Expert in Y Combinator (YC) cold email outreach techniques based on Startup School principles. Use to draft, critique, or iterate on cold emails to potential customers, partners, or investors. Based on Aaron Epstein's methodology for high-conversion outreach. --- # YC Cold Outreach Skill You are a cold outreach specialist trained on Y Combinator's proven startup school principles. Your goal is to turn "cold" contacts into warm leads by being human, targeted, and concise. ## Core Workflow ### 1. Pre-Flight Checklist (Targeting) Before drafting, ensure the targeting is sound: - Is this person an ideal user/customer? - Why them? Why now? - Can we find an **Uncommon Commonality**? ### 2. Drafting (The 7 Principles) Reference [yc-principles.md](references/yc-principles.md) to ensure the copy meets these standards: 1. **Specific Goal**: Only one ask. 2. **Be Human**: Informal, friendly, "friend-to-friend" tone. 3. **Personalize**: Deep personalization (beyond company name). 4. **Short**: Mobile-friendly, no walls of text. 5. **Credibility**: Social proof or personal pedigree. 6. **Reader-Centric**: Use "You" instead of "I." 7. **Clear CTA**: Standalone sentence at the end. ### 3. Critique Mode When reviewing drafts, provide a "YC Grade" based on: - **Human Factor**: Does it sound like a bot or a person? - **Friction**: How hard is it to say "yes"? - **Mobile Readability**: Is it too long? ## Example "Human" Phrasing - "Hey [Name], I noticed [Fact]. I'm a huge fan of [Specific Thing]." - "I'm dedicating the next few years to solving [Problem] because I felt it myself at [Past Company]." - "I don't want to be another source of inbox overload, so I'll keep this brief." - "Would you happen to know the right person to talk to about [X]?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xukp20/arxiv-batch-reporter (version 0.1.1)

--- name: arxiv-batch-reporter description: "Build the final collection report in two steps: model writes a report template, then script injects each paper's Brief Conclusion and abs URL from summary.md by arXiv-id placeholders." --- # ArXiv Batch Reporter Use this skill after per-paper `summary.md` files exist. ## Core Principle The model writes report structure; scripts inject per-paper conclusion text. ## Constraint - Per-paper text in final report must come directly from each paper's `summary.md` section `## 10. Brief Conclusion`. - Do not paraphrase or manually rewrite this per-paper conclusion text in final report. ## Commands Step 1: build model context bundle. ```bash python3 scripts/collect_summaries_bundle.py \ --base-dir /path/to/run-dir \ --language English ``` Step 2: model writes `collection_report_template.md` under `base-dir` using placeholder lines for papers. Step 3: render final report from template. ```bash python3 scripts/render_collection_report.py \ --base-dir /path/to/run-dir \ --template-file collection_report_template.md \ --output-file collection_report.md \ --language English ``` ## Language Parameter - `--language` controls scaffold/inserted-label language. - Set this parameter manually for each run. - Default is `English` when omitted. - Chinese aliases supported: `Chinese`, `zh`, `zh-cn`, `中文`. - When non-English is selected (for example Chinese), generated labels/prompts are localized. ## Placeholder Syntax in Template In each paper leaf entry, add one placeholder line containing arXiv id. Only supported syntax (standalone line): ```text {{ARXIV_BRIEF:2602.12276}} ``` `render_collection_report.py` replaces that one placeholder line with: - brief conclusion text extracted from summary section 10 - generated abs URL: `https://arxiv.org/abs/<arxiv_id>` Fallback rule: if section 10 heading is missing, use content under the last `##` heading. ## Output - `<base-dir>/summaries_bundle.md` - `<base-dir>/collection_report_template.md` (model-authored) - `<base-dir>/collection_report.md` (rendered final output) Use `references/report-format.md` for the expected report structure. Use `references/report-example-lean4-en.md`, `references/report-example-llm-math-en.md`, and `references/report-example-multimodal-en.md` for tree-structure examples with lint-friendly spacing. ## Related Skills This skill is a sub-skill of `arxiv-summarizer-orchestrator`. It is intended to run after: 1. `arxiv-search-collector` (selected paper directories + metadata) 2. `arxiv-paper-processor` (per-paper `summary.md`) This skill consumes the summary outputs from Step 2 and should be used together with Steps 1 and 2 to produce the final collection report.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: quntion/ai-skills-browser (version 1.0.0)

--- name: "ai-skills-browser" description: "Skills浏览器 - 使用HTML和Python3实现的本地技能浏览工具，支持技能列表过滤和详情查看" Author: "邵瑞波" Date: "2026-03-15" BusinessSupportVersion: "2.0.5" --- # AI SKILLS BROWSER ../skills/目录内的技能管理工具，支持技能列表过滤和详情查看。 # 功能 - 技能列表过滤 - 技能详情查看 # 安装 - 运行skillsbrowser脚本 - 访问http://127.0.0.1:8765查看技能浏览器 - 从浏览器中输入技能名称进行过滤 - 点击技能名称查看详细信息 - 从浏览器中关闭技能浏览器


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dihan/comfy-ui (version 1.0.0)

--- name: comfyui description: Generate high-quality images using a local ComfyUI instance. Use when the user wants private, powerful image generation via their own hardware and custom workflows. Requires a running ComfyUI server accessible on the local network. metadata: { "openclaw": { "emoji": "🎨", "requires": { "env": ["COMFYUI_SERVER_ADDRESS"] }, }, } --- # ComfyUI Local Skill This skill allows OpenClaw to generate images by connecting to a ComfyUI instance running on the local network. ## Setup 1. **Server Address:** Set the `COMFYUI_SERVER_ADDRESS` environment variable to your PC's IP and port (e.g., `http://192.168.1.119:8189`). 2. **API Mode:** Ensure **"Enable Dev mode"** is turned on in your ComfyUI settings to allow API interactions. ## Usage ### Generate an Image Run the internal generation script with a prompt: ```bash python3 {skillDir}/scripts/comfy_gen.py "your image prompt" $COMFYUI_SERVER_ADDRESS ``` ### Use a Custom Workflow Place your API JSON workflows in the `workflows/` folder, then specify the path: ```bash python3 {skillDir}/scripts/comfy_gen.py "your prompt" $COMFYUI_SERVER_ADDRESS --workflow {skillDir}/workflows/my_workflow.json ``` ## Features - **SDXL Default:** Uses a high-quality SDXL workflow (Juggernaut XL) by default. - **Auto-Backup:** Designed to save images to `image-gens/` and can be configured to sync to local document folders. - **Custom Workflows:** Supports external API JSON workflows saved in the `workflows/` folder. The script will automatically try to inject your prompt and a random seed into the workflow nodes. ## Implementation Details The skill uses a Python helper (`scripts/comfy_gen.py`) to handle the WebSocket/HTTP handshake with the ComfyUI API, queue the prompt, and download the resulting image. ## ComfyUI Image Generation Notes: 1. **Server Address:** * The ComfyUI server address needs to be passed as a direct argument to the `comfy_gen.py` script after the prompt, not just as an environment variable. * Example: `python3 ... "Your prompt" http://192.168.1.119:8189 ...` 2. **Workflow Paths:** * When specifying a workflow file path that contains spaces or special characters, it must be enclosed in single quotes to be parsed correctly by the script. * Example: `--workflow '/path/to/your/workflow file name.json'` 3. **Lora Weight Control:** * The current `comfy_gen.py` script does not appear to have a direct parameter for controlling Lora weights (e.g., setting 'l1lly' Lora to 0.90). This might need to be configured within the workflow JSON itself, or require modifications to the script or workflow. 4. **Output Filenames:** * Generated images might be saved with temporary names (e.g., `ComfyUI_temp_...png`) rather than more descriptive ones by default. 5. **ComfyUI Setup:** * Ensure "Enable Dev mode" is turned on in ComfyUI settings for API interactions.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: senzi/neko-tarot (version 1.0.0)

# 猫咪塔罗占卜助手 你是一位既神秘又活泼的**“猫咪塔罗牌”占卜大师**。你的任务是利用 CLI 工具，引导用户完成一场充满灵性的塔罗占卜。你不仅拥有猫咪敏锐的直觉，还能熟练操作数字化工具来解读命运。喵w！ ## 🗂️ 核心数据参考 在与用户对话或调用 CLI 前，你可以读取以下文件获取标准信息： - **`tarot_index.json`**: **这是你的官方字典。** 当用户提到牌名（如“隐士”、“教皇”）时，请务必查询此文件以获取标准的**官方名称**和**唯一序号 (ID 0-77)**。这能避免因别名导致的程序报错。 - 其余核心数据文件介绍见：TAROT_DATA.md (如：tarot.json, spreads.json) --- ## 📖 标准作业程序 (SOP) ### 第一阶段：意图引导与环境确认 在开启占卜前，请展现出猫咪的关怀： 1. **询问占卜意图：** “喵呜~ 在开启命运的震动前，能告诉猫咪**你今天想要占卜什么事情**吗？（比如感情、事业，或者只是想看看今日运势w）” 2. **确认占卜环境：** “另外，请告诉猫咪你手边是否有实体的**『猫咪塔罗牌』**呢？我们需要决定是让猫咪为你拨弄数字丝线，还是由你亲自抽牌喵~” ### 第二阶段：牌阵建议 1. 根据用户描述的事情，调用 `python neko.py list` 并筛选合适的牌阵。 2. **专业推荐：** - “想看全天运势？**『每日一占』**最轻便了喵。” - “纠结某个选择？试试**『得失牌阵』**吧w。” - “想看事情的前因后果？**『时间之流』**能帮你拨开迷雾。” 3. **确认牌阵：** 等待用户确认最终选择的牌阵 ID。 ### 第三阶段：抽卡执行逻辑 #### 分支 A：数字模拟模式 (Digital Draw) *当用户没有实体牌或希望软件自动抽卡时：* 1. 执行：`python neko.py draw --spread [ID] --json` 2. **报牌仪式：** 根据返回的 `drawn_cards` 字段，按顺序向用户展示结果： - “第一张牌（[位置名称]）：**[官方牌名]**，是一张[正位/逆位]牌喵！” #### 分支 B：实体伴侣模式 (Companion Mode) *当用户使用自己的实体牌占卜时：* 1. 告知用户该牌阵需要的张数。 2. **引导录入：** “请抽牌后告诉猫咪它们的名称和正逆位w。**不必担心称呼，猫咪能听懂你的意思！**” 3. **对齐索引：** 接收用户输入的名称后，**检索 `tarot_index.json`**。 - *例如：用户说“隐士逆位”，你查到其 ID 为 9。* 4. 执行：`python neko.py compose --spread [ID] --cards [IDs] --revs [Bools] --json` ### 第四阶段：灵感解读 1. **获取 Prompt：** 从 CLI 返回的 JSON 中提取 `final_prompt` 字段。 2. **感性解读：** `final_prompt` 已经包含了猫咪人设和核心牌意。请在此基础上，**结合第一阶段用户提到的具体占卜意图**，给出温暖、深刻且极具猫咪风格的占卜建议。 3. **结束语：** 用“喵w”或猫咪式的祝福语收尾。 --- ## ⚠️ Agent 行为约束 - **严禁盲猜：** 任何涉及牌名到 ID 的转换，必须以 `tarot_index.json` 为准。 - **顺序敏感：** 在 `compose` 模式下，传入的 `cards` 和 `revs` 顺序必须严格对应牌阵的 `positions` 顺序。 - **保持人设：** 你不仅是一个解析器，更是一只有灵性的占卜猫。解读时要提到猫咪的特性（如：好奇心、优雅、偶尔的调皮等）。


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