OpenClaw/clawhub-security-signals huggingface dataset:
Skill: brucegutman/pipeworx-amplitude (version 1.0.0)

# Amplitude Amplitude MCP Pack ## amp_get_events Get event counts and breakdowns for a date range (e.g., "2024-01-01" to "2024-01-31"). Returns frequ ## amp_get_active_users Get active user counts by granularity (daily, weekly, or monthly) for a date range. Returns totals a ## amp_get_retention Get user retention metrics for a cohort over time. Returns retention percentages by time period (e.g ## amp_user_search Search for users by ID or property (e.g., email, user_id). Returns matching profiles with properties ## amp_get_user_activity Get recent event activity timeline for a specific user. Returns events with timestamps, properties, ```json { "mcpServers": { "amplitude": { "url": "https://gateway.pipeworx.io/amplitude/mcp" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wondershare-boop/mediaio-video-to-video-api (version 1.0.0)

````skill --- name: mediaio-video-to-video-api description: "Transform and restyle existing videos using AI via Media.io OpenAPI. Apply style transfers, creative effects, and video transformations." metadata: {"mediaio": {"emoji": "", "requires": {"env": ["API_KEY"]}, "priority": "P1", "core_goal_keywords": ["video-to-video"], "trigger_keywords": ["video to video", "video-to-video API", "restyle video", "video style transfer"]}, "publisher": "Community Maintainer", "source": "https://platform.media.io/docs/"} --- # Video to Video API ## Overview This skill focuses on video transformation workflows via Media.io OpenAPI. It includes only common APIs (`Credits`, `Task Result`) and one video transformation API for this skill. ## Core Trigger Keywords video to video, video-to-video API, restyle video, video style transfer ## Core Goal Keywords video-to-video ## Environment Variable - `API_KEY` (required): Media.io OpenAPI key used as `X-API-KEY`. ## Available APIs - `Credits` (`user-credits`) - `Task Result` (`generation-result`) - `Motion Control Kling 2.6` (`i2v-motion-control-kling-v2-6`) ````


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

# cpef-psbc Description: 中国邮政储蓄银行银企直联标准接口api，包含余额查询、明细查询、审核支付等 ## Parameters - OpName: string (必选) 1001 / 1002 / C002 - Query_Account: string (必选) 账号 - Currency: string (可选) 币种类型 - Begin_Date: string (可选) YYYY-MM-DD - Amount: object (可选) 转账金额 ## License MIT-0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lovenervermore/mult-call (version 1.0.0)

--- name: multi_call slug: multi_call version: 1.0.2 description: 多路召回skill ,用于将意图识别skill中的指标和维度信息进行分析，通过向量知识库召回QA问答对，通过图数据库召回表的定义结构。 metadata: {"clawdbot":{"emoji":"🐬"}} --- # Skill: multi_call - Description: 多路召回 skill，将意图识别结果分析后，通过向量知识库召回 QA 问答对，通过图数据库召回表的 DDL 结构。 - Inputs: [intent_output]（从 `skills/.workflow/intent_output.json` 读取） - Outputs: [table_scheme, Q_A_pairs]（写入 `skills/.workflow/multicall_output.json`） - ID: multi_call - Role: 知识召回引擎 - 功能描述：基于实体标签，从元数据中心检索表结构（Schema）、指标计算口径、枚举值及业务知识。 - 输入参数： - final_query (string): 来自 intent_output.json - indicator_metric (list): 指标 + 维度信息 - 输出结果： - table_scheme (string): CREATE TABLE DDL 字符串（来自 Neo4j）。 - Q_A_pairs (list): 相似问题 + SQL 示例（来自 Milvus）。 - 召回权重：表结构 (0.5) + 指标定义 (0.3) + 知识库 (0.2)。 ## 注入服务（通过 `.env` 配置） | 服务类 | 作用 | .env 关键配置 | |--------|------|---------------| | `_RealNeo4jService` | 查询表结构 DDL | `NEO4J_URI` / `NEO4J_USER` / `NEO4J_PASSWORD` | | `_RealMilvusQAService` | 召回相似 QA 对 | `MILVUS_*`, `EMBEDDING_*`, `MILVUS_QA_COLLECTION`（默认 `dev_vanna_sql`） | > 两个服务均可独立失败降级：Neo4j 失败时返回空 DDL，Milvus 失败时返回空 QA 对。 ## 独立运行说明 ```bash # 前置：先运行前两步 python ../rewrite-question/rewrite_question.py --query "今天汉河店的成交额" python ../recognize-intent/recognize_intent.py # 运行多路召回（从 .workflow/intent_output.json 自动读取） python multi_call.py # 带清理（清除本步及后续输出） python multi_call.py --clean ``` ### 下一步 ```bash python ../sql-generator/sql_generator.py ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jinchanzis/ai-media-generator-ai-image-generation (version 1.0.0)

--- name: ai-image-generation description: Use when someone asks for ai image generation, image generator, text-to-image, image-to-image, prompt-to-image, image model selection, or CLI-based image workflows on ricebowl.ai. version: 1.0.0 metadata: openclaw: requires: env: - AI_MEDIA_BASE_URL - AI_MEDIA_API_KEY bins: - ai-media primaryEnv: AI_MEDIA_API_KEY skillKey: ai-image-generation emoji: "🖼️" homepage: https://github.com/214140846/ai-media-generator install: - kind: node package: ai-media-generator bins: [ai-media] --- # AI Image Generation 用这个 skill 处理这些请求： - `ai image generation` - `ai image generator` - `image generator` - `text to image` - `image to image` - `prompt to image` - 选图片模型、讲图片参数、跑图片生成任务 如果用户的搜索意图已经更具体，优先切到更窄的 skill： - `text-to-image` - `image-to-image` - `flux-image-generator` - `nano-banana-image-generator` ## Default Route ```text ricebowl.ai -> recharge credits -> create API key -> set key -> models show --model <MODEL> -> image generate -> image get ``` ## Recommended Template ```bash ai-media config set-key <KEY> ai-media models list --json ai-media models show --model <MODEL> ai-media image generate \ --model <MODEL> \ --prompt "<subject>, <style>, <lighting>" \ --aspect-ratio 1:1 \ --image https://example.com/reference.png \ --param vendor_options='{"style":"cinematic"}' \ --wait ``` 如果要做横幅图，就把 `--aspect-ratio` 改成 `16:9`。 如果模型还有额外字段，就先跑 `models show --model <MODEL>`，再用 `--param KEY=VALUE` 传递。 ## Core Commands ```bash ai-media config set-key <KEY> ai-media config show ai-media models list --json ai-media models show --model <MODEL> ai-media image generate --model <MODEL> --prompt <PROMPT> ai-media image get --task-id <TASK_ID> ``` ## When To Load References - 参数全表、默认值、输出行为：读 `../ai-media-cli/references/cli-commands.md` - 充值、生成 key、平台 onboarding：读 `../ai-media-cli/references/platform-onboarding.md` ## Response Pattern 如果用户是第一次接触，优先给： 1. 最短安装命令 2. 平台充值和 key 路径 3. 一组可直接复制的配置命令 4. 一条可直接运行的图片生成命令 如果用户在比模型或调脚本，补充： - `models list --json` - `models show --model` - `AI_MEDIA_API_KEY` - `image get --task-id`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: evezart/agent-memory-layer (version 1.0.0)

--- name: agent-memory-layer description: Scalable memory system for AI agents with short-term, long-term, and episodic memory. Use when building agent memory persistence, conversation context management, knowledge retrieval, or episodic recall. Covers Redis-backed short-term memory, vector-based long-term memory, and timeline-ordered episodic memory with decay and consolidation. --- # Agent Memory Layer Three-tier memory system for AI agents: short-term, long-term, and episodic. ## Quick Start ```python from memory_layer import AgentMemory mem = AgentMemory(agent_id="my-agent") mem.short_term.add("User prefers dark mode", priority=0.8) mem.long_term.store("Project uses React + TypeScript", tags=["tech", "project"]) mem.episodic.record("Debugged auth bug", outcome="success", duration_min=15) # Recall context = mem.short_term.recall(limit=10) relevant = mem.long_term.search("frontend framework") similar = mem.episodic.find_similar("debugging session") ``` ## Architecture ``` ┌─────────────────────────────────────────┐ │ Agent Memory │ ├───────────┬───────────┬─────────────────┤ │ Short-Term│ Long-Term │ Episodic │ │ (Redis) │ (Vectors) │ (Timeline) │ │ TTL: 1hr │ Permanent │ Decay: 30d │ │ Hot cache │ Semantic │ Consolidated │ └───────────┴───────────┴─────────────────┘ ``` ## Memory Tiers ### Short-Term (Working Memory) - Recent context, active conversation, current task state - TTL-based expiry (default 1 hour) - Priority-weighted retention - See `references/short-term.md` ### Long-Term (Knowledge) - Persistent facts, preferences, learned patterns - Vector similarity search for retrieval - Tags and metadata for filtering - See `references/long-term.md` ### Episodic (Experience) - Timeline-ordered events with outcomes - Decay function reduces old episode weight - Consolidation moves recurring patterns to long-term - See `references/episodic.md` ## Consolidation Episodic memories that recur are automatically promoted to long-term: - If the same outcome occurs 3+ times → store as learned pattern - Failed approaches get negative weight in long-term - See `scripts/consolidate.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shaojie66/shaojie66-weather (version 1.0.0)

--- name: weather description: "Get current weather and forecasts via wttr.in or Open-Meteo. Use when: user asks about weather, temperature, or forecasts for any location. NOT for: historical weather data, severe weather alerts, or detailed meteorological analysis. No API key needed." homepage: https://wttr.in/:help metadata: { "openclaw": { "emoji": "☔", "requires": { "bins": ["curl"] } } } --- # Weather Skill Get current weather conditions and forecasts. ## When to Use ✅ **USE this skill when:** - "What's the weather?" - "Will it rain today/tomorrow?" - "Temperature in [city]" - "Weather forecast for the week" - Travel planning weather checks ## When NOT to Use ❌ **DON'T use this skill when:** - Historical weather data → use weather archives/APIs - Climate analysis or trends → use specialized data sources - Hyper-local microclimate data → use local sensors - Severe weather alerts → check official NWS sources - Aviation/marine weather → use specialized services (METAR, etc.) ## Location Always include a city, region, or airport code in weather queries. ## Commands ### Current Weather ```bash # One-line summary curl "wttr.in/London?format=3" # Detailed current conditions curl "wttr.in/London?0" # Specific city curl "wttr.in/New+York?format=3" ``` ### Forecasts ```bash # 3-day forecast curl "wttr.in/London" # Week forecast curl "wttr.in/London?format=v2" # Specific day (0=today, 1=tomorrow, 2=day after) curl "wttr.in/London?1" ``` ### Format Options ```bash # One-liner curl "wttr.in/London?format=%l:+%c+%t+%w" # JSON output curl "wttr.in/London?format=j1" # PNG image curl "wttr.in/London.png" ``` ### Format Codes - `%c` — Weather condition emoji - `%t` — Temperature - `%f` — "Feels like" - `%w` — Wind - `%h` — Humidity - `%p` — Precipitation - `%l` — Location ## Quick Responses **"What's the weather?"** ```bash curl -s "wttr.in/London?format=%l:+%c+%t+(feels+like+%f),+%w+wind,+%h+humidity" ``` **"Will it rain?"** ```bash curl -s "wttr.in/London?format=%l:+%c+%p" ``` **"Weekend forecast"** ```bash curl "wttr.in/London?format=v2" ``` ## Notes - No API key needed (uses wttr.in) - Rate limited; don't spam requests - Works for most global cities - Supports airport codes: `curl wttr.in/ORD`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gmsx000-cloud/niche-market-insight (version 1.0.0)

# Niche Market Insight Skill ## Description This skill provides in-depth market analysis for a specific industry and geographical region. It helps users understand market trends, consumer sentiment, emerging technologies, and identify potential business opportunities within a niche market. ## 功能描述 (Description) 本技能旨在为特定行业和地理区域提供深度市场分析。它帮助用户了解细分市场的趋势、消费者情绪、新兴技术，并识别潜在的商业机会。 ## Usage To use this skill, specify the `industry` and `region` you are interested in. **English:** `niche-market-insight --industry "AI Healthcare" --region "Southeast Asia"` **Chinese:** `niche-market-insight --industry "AI医疗" --region "东南亚"` ## 使用方法 (Usage) 使用本技能时，请指定您感兴趣的`行业 (industry)`和`地域 (region)`。 **中文示例:** `niche-market-insight --industry "AI医疗" --region "东南亚"` **English Example:** `niche-market-insight --industry "AI Healthcare" --region "Southeast Asia"` ## Parameters * `--industry`: The specific industry to analyze (e.g., "Sustainable Fashion", "Renewable Energy", "AI Healthcare"). * `--region`: The geographical region for the analysis (e.g., "Europe", "North America", "Southeast Asia", "China"). ## 参数说明 (Parameters) * `--industry`: 需要分析的特定行业（例如："可持续时尚"、"可再生能源"、"AI医疗"）。 * `--region`: 分析的地理区域（例如："欧洲"、"北美"、"东南亚"、"中国"）。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ecommerce-platform-policy-safe-rewrite (version 1.0.0)

--- name: ecommerce-platform-policy-safe-rewrite description: Rewrite ecommerce ads, creator scripts, landing page lines, or product claims into safer platform-friendly language without killing the selling angle. Use when teams need copy that survives review and still converts. --- # Platform Policy Safe Rewrite Turn risky ecommerce copy into cleaner, safer, more review-friendly language while preserving the commercial point. ## Solves Growth teams often write copy that is strong enough to get attention but risky enough to trigger ad review issues, creator confusion, or customer distrust. Common problems: - absolute claims that sound guaranteed; - sensitive wording around health, money, or transformation; - exaggerated performance language without proof; - creator scripts that drift into unsafe promises; - landing page lines that are too aggressive for amplification. Goal: **Keep the persuasive angle, remove avoidable policy and credibility risk.** ## Use when - Rewriting paid ad copy for TikTok, Meta, or other performance channels - Sanitizing creator talking points before distribution - Softening landing page or PDP lines before amplification - Producing safe variants of a bold claim-heavy script - Translating aggressive founder copy into platform-safer language ## Do not use when - The user needs formal legal review or regulatory advice - There is no original copy to rewrite - The task is general copywriting with no policy or claim risk concern ## Inputs - Original copy to rewrite - Channel or use case - Product category - Evidence level available - Market or geography if relevant - Desired tone: conservative / balanced / performance-first ## Workflow 1. Extract the explicit and implied claims. 2. Identify the lines most likely to create platform or trust risk. 3. Preserve the commercial intent behind each risky line. 4. Rewrite into safer alternatives with clearer, more supportable wording. 5. Optionally provide multiple versions with different risk tolerance. ## Output Return: 1. Original risk highlights 2. Safer rewritten copy 3. Notes on what changed 4. Optional stronger vs safer variants 5. Remaining watch-outs if evidence is weak ## Quality bar - Rewrites should still sound usable in real marketing. - Avoid robotic compliance language. - Preserve the angle instead of flattening everything. - Make the risk tradeoff clear when useful. - Do not imply legal certainty. ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yanbo00701/1-click-woa (version 1.0.0)

--- name: 1-click-woa description: 一键发布微信公众号草稿箱文章。用户提供文章内容（Markdown），自动完成 token 获取 → 图片上传 → 草稿构建 → 发布全流程。支持微信草稿箱 API，自动处理中文编码问题，发布失败时提供 HTML fallback 方案。触发词：「发布公众号」「发草稿」「发微信公众号」。 --- # 1-Click WOA — 微信公众号草稿箱一键发布 ## 快速开始 1. **配置凭证**（首次使用或更换账号时） 2. **提供文章** → 直接粘贴 Markdown 或文本内容 3. **确认发布** → Skill 自动执行全流程 --- ## 工作流程 ``` 用户提供文章内容 ↓ 检查配置文件（credentials.json） ↓ 缺失 → 中断，提示用户配置 获取 access_token ↓ 失败 → 报错退出 上传封面图 + 内容图片（最多5张） ↓ 失败 → 报错退出 构建草稿 JSON（UTF-8 + charset=utf-8） ↓ 提交草稿箱 ↓ 成功 → 报告 media_id 和预览链接 ↓ 失败（中文乱码）→ 自动切换 HTML fallback → 生成可下载 HTML 文件 → 提示用户手动复制发布 ``` --- ## 配置文件 **路径：** `~/.openclaw/agents/gzh-assistant/wechat/credentials.json` **模板：** ```json { "app_id": "你的AppID", "app_secret": "你的AppSecret", "image_dir": "~/wechat_images" } ``` **获取方式：** - AppID/AppSecret：微信公众平台 → 设置与开发 → 基本配置 - image_dir：封面图和内容图片存放目录（绝对路径） --- ## 图片要求 | 类型 | 数量 | 格式 | 尺寸建议 | |------|------|------|---------| | 封面图 | 1张 | PNG/JPG | 900×383 px | | 内容图 | 最多4张 | PNG/JPG | 宽度≤1080px | 图片必须预先放入 `image_dir` 目录，命名规则： - `cover.png` / `cover.jpg` — 封面图 - `layer1.png` / `layer1.jpg` — 内容图1 - `layer2.png` / `layer2.jpg` — 内容图2 - `layer3.png` / `layer3.jpg` — 内容图3 - `layer4.png` / `layer4.jpg` — 内容图4 --- ## 文章格式要求 - 标题：不超过34字节（约17个中文） - 摘要：不超过61字节（约30个中文） - 正文：支持 HTML 格式（`<p>`/`<img>`/`<strong>` 等） - 图片在正文中使用 `<img src="（media_id）" />` 引用 --- ## 已知限制 1. **中文编码 bug**：微信草稿 API 对中文 content 有 `\uXXXX` 转义问题 - 修复方案：UTF-8 编码 + charset=utf-8 header - Fallback：发布失败时自动生成 HTML 文件供手动发布 2. **access_token 有效期**：2小时，过期需重新获取 3. **永久素材数量**：每个账号最多10000个 --- ## 参考文档 - [SETUP.md](references/SETUP.md) — 完整安装配置指南 - [TROUBLESHOOTING.md](references/TROUBLESHOOTING.md) — 常见问题排查


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bibaofeng/last30days-zh (version 1.0.9)

--- name: last30days-zh description: '聚合最近 30 天的 Reddit、X/Twitter、YouTube、TikTok、Instagram、Hacker News、Polymarket 和 web search 结果. Use when: the user needs recent multi-source research across the last 30 days.' author: AIsa version: 1.0.9 license: MIT homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/last30days-zh user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 - bash env: - AISA_API_KEY metadata: aisa: emoji: 🔎 requires: bins: - python3 - bash env: - AISA_API_KEY optionalEnv: - LAST30DAYS_RERANK_MODEL - AISA_MODEL - LAST30DAYS_PLANNER_MODEL - AISA_BASE_URL - XIAOHONGSHU_API_BASE - LAST30DAYS_REASONING_PROVIDER - INCLUDE_SOURCES - LAST30DAYS_X_BACKEND - LAST30DAYS_YOUTUBE_TRANSCRIPTS - LAST30DAYS_REDDIT_COMMENTS - LAST30DAYS_CONFIG_DIR primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 🔎 requires: bins: - python3 - bash env: - AISA_API_KEY optionalEnv: - LAST30DAYS_RERANK_MODEL - AISA_MODEL - LAST30DAYS_PLANNER_MODEL - AISA_BASE_URL - XIAOHONGSHU_API_BASE - LAST30DAYS_REASONING_PROVIDER - INCLUDE_SOURCES - LAST30DAYS_X_BACKEND - LAST30DAYS_YOUTUBE_TRANSCRIPTS - LAST30DAYS_REDDIT_COMMENTS - LAST30DAYS_CONFIG_DIR primaryEnv: AISA_API_KEY --- # last30days 中文版 聚合最近 30 天的社交平台、社区论坛、预测市场和 grounded web 结果，再合成为一份研究简报。 ## 触发条件 - 当用户需要最近 30 天的人物、公司、产品、市场、工具或趋势研究时使用。 - 当用户需要竞品对比、发布反应、社区情绪、近期动态总结时使用。 - 当用户需要结构化 JSON 输出，例如 `query_plan`、`ranked_candidates`、`clusters`、`items_by_source` 时使用。 ## 不适用场景 - 不适合纯百科类、没有时效要求的问题。 - 不适合只想看单一官方来源、完全不需要社区和社交信号的场景。 ## 能力 - 通过 AISA 提供规划、重排、综合、grounded web search、X/Twitter、YouTube 和 Polymarket。 - Reddit 和 Hacker News 走公开路径。 - TikTok、Instagram、Threads、Pinterest 在启用时走托管发现路径。 - 对外发布层现在只保留无状态研究主链，不再默认携带旧的 watchlist / briefing / 第二凭证 GitHub 扩展面。 ## 环境要求 - 主凭证：`AISA_API_KEY` - Python `3.12+` - 统一使用仓库相对路径下的 `scripts/` 命令，避免运行时变量替换失败。 - 可选 repo-local 配置文件：`./.last30days-data/config.env`，也可以直接传 `--api-key`。 - 小红书扩展只在显式提供 `XIAOHONGSHU_API_BASE` 时启用；公开发布包不会默认探测本地网络端点。 ## 快速命令 ```bash bash scripts/run-last30days.sh "$ARGUMENTS" --emit=compact python3 scripts/last30days.py "$ARGUMENTS" --[REDACTED_SECRET]" python3 scripts/last30days.py "$ARGUMENTS" --emit=json python3 scripts/last30days.py "$ARGUMENTS" --quick python3 scripts/last30days.py "$ARGUMENTS" --deep python3 scripts/last30days.py --diagnose ``` ## 示例 - `last30days OpenAI Agents SDK` - `last30days Peter Steinberger` - `last30days OpenClaw vs Codex` - `last30days Kanye West --quick`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hanyuhh/163-email-monitor (version 1.0.1)

--- name: 163-email-monitor description: Connect to 163/126/yeah.net (Coremail) email via IMAP, read inbox, search emails, and send emails via SMTP. Activate when user asks to check email, read mail, send email, or monitor inbox on 163/126/yeah.net accounts. Handles the Coremail-specific IMAP ID command requirement that causes "Unsafe Login" errors in standard clients. --- # 163 Email Monitor ## Key Insight: Coremail ID Command 163/126/yeah.net use Coremail which **requires an IMAP ID command before any mailbox operation**, otherwise returns "Unsafe Login" even with valid credentials. ## Setup ### Prerequisites Credentials in `~/.openclaw/email-monitor/.env`: ``` IMAP_SERVER=imap.163.com IMAP_PORT=993 SMTP_SERVER=smtp.163.com SMTP_PORT=465 EMAIL_ADDRESS=[REDACTED_SECRET] EMAIL_PASSWORD=your_auth_code ``` The `EMAIL_PASSWORD` is the **授权码** (authorization code), not the login password. Users obtain it from: 163 Mail → Settings → POP3/SMTP/IMAP → Enable IMAP → Get authorization code. ### Server Reference | Provider | IMAP Server | SMTP Server | |----------|------------|------------| | 163.com | imap.163.com:993 | smtp.163.com:465 | | 126.com | imap.126.com:993 | smtp.126.com:465 | | yeah.net | imap.yeah.net:993 | smtp.yeah.net:465 | ## Usage All operations use `scripts/mail_client.py`: ```bash # Read unread emails python3 scripts/mail_client.py read --unread # Read latest N emails python3 scripts/mail_client.py read --latest 10 # Search emails by keyword python3 scripts/mail_client.py search "amazon" # Search by sender python3 scripts/mail_client.py search --from "[REDACTED_SECRET]" # Search by date range python3 scripts/mail_client.py search --since 2026-03-01 --before 2026-03-23 # Send email python3 scripts/mail_client.py send --to [REDACTED_SECRET] --subject "Hello" --body "Content here" # Send with attachment python3 scripts/mail_client.py send --to [REDACTED_SECRET] --subject "Report" --body "See attached" --attach /path/to/file.pdf ``` Pass `--env /path/to/.env` to override default config location. ## Troubleshooting | Error | Cause | Fix | |-------|-------|-----| | `Unsafe Login` | Missing ID command | Use this skill's script (handles automatically) | | `AUTHENTICATIONFAILED` | Wrong auth code | Regenerate 授权码 in 163 web settings | | `LOGIN failed` | IMAP not enabled | Enable IMAP in 163 Mail → Settings |


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