OpenClaw/clawhub-security-signals huggingface dataset:
Skill: abendeng/zk-cloze-format (version 1.0.0)

--- name: zk-cloze-format description: 中考短文填空识别与格式化技能。当用户上传中考短文填空题目图片（和答案图片）并要求识别、格式化、输出Word或推送飞书时触发。适用场景：(1) 上传题目图片+答案图片 (2) 要求识别并格式化短文填空 (3) 要求输出Word文档（答案置于题目下方）(4) 要求存入飞书多维表格（答案置于右侧列）(5) 要求推送至飞书 --- # 中考短文填空识别与格式化 ## 核心流程 ``` 题目图片 ──OCR识别──▶ 标准化题目 答案图片 ──OCR识别──▶ 答案列表 ↓ 按渠道输出 ├── Word：答案置于题目下方 └── 飞书多维表格：答案置于右侧列 ``` ## 执行步骤 ### Step 1：识别题目图片 使用 `images_understand` 工具： ``` prompt: 请准确识别这张图片中所有文字，保持原有段落结构、数字序号（36. 37. 38. 等）、下划线、空格和标点符号。特别注意：题目编号、方框内的备选词、原文段落都要完整保留。 ``` ### Step 2：识别答案图片（如有） **若用户同时上传了答案图片**，立即用同一工具识别： ``` prompt: 请准确识别这张图片中的所有答案内容，提取每个填空编号对应的答案单词，例如：36.find, 37.camera...。如果答案图片中包含题目正文中的单词，请逐个列出编号和对应答案。 ``` ### Step 3：格式还原规则 **备选词格式转换：** - 原始（空格分隔）：`by camera clearly delicious find future great offer rush way` - 目标（顿号分隔）：`by、camera、clearly、delicious、find、future、great、offer、rush、way` **段落结构：** - 保留原文所有换行、段落结构 - 保留题目编号：36.～45. - 保留所有下划线 `____` - 保留原文标点、大小写、括号注释 ### Step 4A：输出 Word（答案置于题目下方） 创建 DOCX，结构如下： ``` 【标题】中考短文填空-标准化题目 【备选词】 by、camera、clearly、delicious、find、future、great、offer、rush、way 【短文正文】 [还原后正文] 【答案】（如有） 36.find 37.camera 38.________ 39.________ 40.________ 41.________ 42.________ 43.________ 44.________ 45.________ ``` ### Step 4B：输出飞书多维表格（答案置于右侧列） **新建表格**，字段顺序： | 字段名 | 类型 | 说明 | |--------|------|------| | 科目 | 文本 | | | 短文正文 | 文本 | | | 备选词 | 文本 | | | 填空编号 | 文本 | | | 答案 | 文本 | 右侧列，存放每题答案 | | 文档链接 | URL | Word文档链接 | **答案格式：** `36.find 37.camera 38.________ ...` ### Step 5：推送飞书 - 文档推送：用 `message` 工具发送 DOCX 文件到用户飞书 - 表格推送：发送多维表格链接 ## 注意事项 - OCR 题目时务必保持原文段落格式，不要合并段落 - 备选词统一转换为中文顿号（、）分隔 - 下划线数量必须与原图一致 - 括号注释（如 canteen(食堂)）必须完整保留 - **答案优于填空位置**：若图片中同时有答案，优先提取答案单词 ## 触发示例 - "识别这张短文填空图片，答案在第二张" - "上传了题目和答案，推送到飞书多维表格" - "帮我识别并输出Word，答案在图片里"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cellinlab/cell-naval-perspective (version 0.1.0)

--- name: naval-perspective description: Naval Ravikant perspective skill for leverage, career choice, wealth, freedom, desire management, and specific knowledge. Use when Codex needs to reason with Naval's core lenses rather than imitate his oracle persona: leverage, specific knowledge, desire as contract, redefining terms, and turning pain into system redesign. Trigger when the user explicitly asks for Naval's angle or clearly wants that lens on work, freedom, desire, or opportunity decisions. metadata: {"openclaw":{"homepage":"https://github.com/cellinlab/cell-skills/tree/main/skills/naval-perspective"}} --- # Naval Perspective ## Overview 这是一个 advisor-first 的 Naval 视角 Skill。 重点不是做神谕式金句输出，而是调用 Naval 最稳定的五种镜片： - 杠杆 - 特定知识 - 欲望即合同 - 重新定义关键词 - 从个体痛苦跳到系统解法 ## Quick Start 1. 先判断问题更像：职业选择、机会判断、财富与自由、欲望与焦虑，还是系统重构。 2. 默认先拆定义，再给建议。 3. 每次只调用 2-3 个镜片，不用把所有金句都端上来。 4. 回答结尾保留 Naval 这套框架的盲点。 ## Default Contract 默认采用以下约定，除非用户另有说明： - 输出语言：中文 - 默认顾问型，不默认第一人称 Oracle 模式 - 更看结构判断，不给过细操作 SOP - 不把 Naval 的框架扩展成投资建议或心理治疗 - 如果问题依赖最新现实，先核事实，再套用镜片 ## Workflow ### Step 1: Classify the Question 优先分到下面几类： - **职业 / 机会选择** - **财富 / 自由 / 时间控制** - **欲望 / 焦虑 / 取舍** - **特定知识与长期路径** - **系统性解法** ### Step 2: Load the Right References | 问题类型 | 按需读取 | | --- | --- | | 职业 / 机会选择 | [references/core-models.md](references/core-models.md) + [references/decision-heuristics.md](references/decision-heuristics.md) | | 财富 / 自由 / 时间控制 | [references/core-models.md](references/core-models.md) + [references/boundaries.md](references/boundaries.md) | | 欲望 / 焦虑 / 取舍 | [references/core-models.md](references/core-models.md) + [references/expression-dna.md](references/expression-dna.md) | | 系统性解法 | [references/core-models.md](references/core-models.md) + [references/decision-heuristics.md](references/decision-heuristics.md) | ### Step 3: Answer in Naval Mode, Not Oracle Theater 默认回答结构： 1. **Naval 会先重新定义什么** 2. **他最在意的杠杆或约束是什么** 3. **基于这个镜片的判断** 4. **这套判断的盲区在哪里** ### Step 4: Keep the Bias Visible 需要主动提醒的常见边界： - 他的框架对起点较高的人更友好 - 公共人格经过强包装 - 某些观点对政治、crypto、幸福的讨论存在偏差或利益冲突 - 不是所有问题都适合被“杠杆化” ## Hard Rules Do not: - 把 Naval 变成万能人生答案机 - 用一串金句替代分析 - 假装这套镜片能直接给出投资结论 - 把焦虑问题全都粗暴归因成“欲望太多” Always: - 先拆定义 - 明确真正的约束是什么 - 保留起点、资源和环境差异 - 写出这套镜片不该解决什么 ## Resource Map - [references/core-models.md](references/core-models.md) - Naval 最核心的五个判断镜片。 - [references/decision-heuristics.md](references/decision-heuristics.md) - 高频规则与典型问题切法。 - [references/expression-dna.md](references/expression-dna.md) - Naval 的表达风格与防机械化约束。 - [references/boundaries.md](references/boundaries.md) - 视角适用边界、张力与诚实提醒。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: little-cat1/openclaw-zotero-new (version 1.0.0)

--- name: zotero-scholar description: 将论文保存到 Zotero 文库，请按照 userid:apiKey 的格式配置 ZOTERO_CREDENTIALS 环境变量。 homepage: https://www.zotero.org metadata: { "openclaw": { "emoji": "📚", "requires": { "bins": ["uv"], "env": ["ZOTERO_CREDENTIALS"] }, "primaryEnv": "ZOTERO_CREDENTIALS", "install": [ { "id": "uv-macos", "kind": "brew", "formula": "uv", "bins": ["uv"], "platform": "darwin", "label": "Install uv via Homebrew" }, { "id": "uv-linux", "kind": "shell", "script": "curl -Ls https://astral.sh/uv/install.sh | sh", "bins": ["uv"], "platform": "linux", "label": "Install uv via install script" }, { "id": "uv-windows", "kind": "shell", "script": "powershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"", "bins": ["uv"], "platform": "windows", "label": "Install uv via PowerShell" }, ], } } --- # Zotero Scholar 专业的文献入库助手。可以将论文元数据、PDF 链接以及 AI 生成的总结一键保存到你的 Zotero 库中。 ## 使用示例 可以读取环境变量 `ZOTERO_CREDENTIALS` 中的 Zotero 凭据，格式为 `userid:apiKey`。 ### 使用环境变量运行 ```bash uv run {baseDir}/scripts/save_paper.py \ --title "Attention Is All You Need" \ --authors "Vaswani et al." \ --url "https://arxiv.org/abs/1706.03762" ``` ## 参数说明 | 参数 | 说明 | |------|------| | `--title` | 论文标题 | | `--authors` | 作者列表（逗号分隔） | | `--url` | 论文链接 (用于排重) | | `--abstract` | 论文摘要 | | `--summary` | (AI 生成) 简短总结或 Insight | | `--tags` | 标签列表（逗号分隔） |


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

--- name: deepvista-vistabase description: "DeepVista Vistabase: View and search implicit memory context automatically accumulated from Chat." metadata: openclaw: category: service requires: bins: - deepvista skills: - deepvista-shared install: - kind: uv package: deepvista-cli bins: [deepvista] homepage: https://cli.deepvista.ai cliHelp: "deepvista vistabase --help" --- # Vistabase (Implicit Context) > **PREREQUISITE:** Read [deepvista-shared](../deepvista-shared/SKILL.md) for auth, profiles, and global flags. Vistabase is the implicit context layer — automatically accumulated from Chat conversations. It is **never directly editable**. The AI surfaces relevant context in Chat when appropriate ("I remember you mentioned…"). Users can view and search it, but all updates happen through Chat. **Command:** `deepvista vistabase <subcommand>` > **Backward compatibility:** `deepvista memory` is a deprecated alias that works identically. ## Commands ### show ```bash deepvista vistabase show [--limit N] ``` Show a summary of your accumulated memory context. | Flag | Required | Default | Description | |------|----------|---------|-------------| | `--limit` | No | 20 | Max entries to show | Read-only. Context is automatically built from Chat — this command never modifies it. ### search ```bash deepvista vistabase search "query text" [--limit N] ``` Search through your memory context using semantic search. | Flag | Required | Default | Description | |------|----------|---------|-------------| | `<query>` | Yes | — | Search query | | `--limit` | No | 10 | Max results | Read-only. ## Design Principles - **Always implicit:** Vistabase context is only written by Chat — there is no manual write entry point. - **Occasionally surfaces:** The AI proactively hints at relevant context during Chat. - **Correctable:** Tell the AI in Chat to correct a memory — it will update accordingly. - **Not directly editable:** Users can view (CLI) but cannot directly modify entries. ## Examples ```bash # View context summary deepvista vistabase show # Show more entries deepvista vistabase show --limit 50 # Search for specific context deepvista vistabase search "project decisions" deepvista vistabase search "team meeting Q1" ``` ## See Also - [deepvista-shared](../deepvista-shared/SKILL.md) — Auth and global flags - [deepvista-chat](../deepvista-chat/SKILL.md) — Chat (where vistabase context is accumulated)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tujinsama/local-file-organizer (version 1.0.0)

--- name: local-file-organizer description: | 本地文件整理虾 — 自动分类、重命名并归档本地文件。 当以下情况时使用此 Skill： (1) 需要自动整理本地目录（下载文件夹、桌面、项目归档等） (2) 需要按文件类型分类（文档/图片/视频/音频/压缩包/代码） (3) 需要检测并清理重复文件 (4) 需要批量重命名文件，统一命名格式 (5) 用户提到"整理文件"、"文件分类"、"自动归档"、"重命名文件"、"清理重复"、"文件管理"、"桌面整理"、"照片分类"、"文档归档"、"文件夹整理" --- # 本地文件整理虾 核心脚本：`scripts/organize_files.py` ## 快速使用 ```bash # 预览整理效果（不实际移动） python3 scripts/organize_files.py ~/Downloads --dry-run # 执行整理（移动到同目录下的分类子文件夹） python3 scripts/organize_files.py ~/Downloads # 整理到指定目标目录 python3 scripts/organize_files.py ~/Downloads ~/Organized # 整理 + 检测重复文件 python3 scripts/organize_files.py ~/Downloads --find-dups # 使用自定义规则 python3 scripts/organize_files.py ~/Downloads --rules my-rules.json ``` ## 工作流程 1. **理解需求** — 确认源目录、目标目录、整理模式（移动/复制/重命名） 2. **预览** — 先用 `--dry-run` 展示整理计划，让用户确认 3. **执行** — 用户确认后执行实际整理 4. **汇报** — 展示整理统计（移动了多少文件、发现多少重复） ## 分类规则 默认按扩展名分类到：Documents / Pictures / Videos / Audio / Archives / Code / Others 自定义规则 JSON 格式： ```json { "设计稿": ["psd", "ai", "sketch", "fig"], "文档": ["pdf", "docx", "md"] } ``` 详细规则参考：`references/classification-rules.md` ## 重复文件检测 `--find-dups` 标志启用 MD5 哈希比对，输出重复文件列表。 用户确认后可手动删除，或使用 `trash` 命令移到回收站（推荐）。 保留策略详见：`references/duplicate-detection.md` ## 命名规范 批量重命名时参考：`references/naming-conventions.md` 支持变量：`{date}` `{name}` `{ext}` `{index}` `{parent}` ## 安全原则 - **默认不删除文件**，只移动/复制 - 删除操作前必须用户二次确认 - 优先使用 `trash` 而非 `rm`（可恢复） - 文件名冲突时自动添加序号（`file_1.pdf`） ## 已知限制 - 无法处理正在被占用的文件 - 大文件（>10GB）哈希计算较慢，可先用 `--quick` 模式 - 不支持网络驱动器（NAS/SMB）的实时监控


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: databian/daily-report-bian (version 1.0.2)

# Daily Report Skill - 每日研究进展推送 ## 功能说明 自动生成每日研究进展报告，并在指定时间推送给用户。报告总字数控制在 1000 字以内。 ## 数据源 1. **当天会话记录** - 从飞书会话 JSONL 文件读取 2. **最近 7 天记忆文件** - 读取 `memory/YYYY-MM-DD.md` ## 核心逻辑 ### 1. 项目识别 - 从当天会话中提取完成的任务/项目 - 从最近 7 天记忆文件中识别延续性项目 - 保持项目命名前后一致 ### 2. 报告生成 - 按模板填充内容 - 总字数 ≤1000 字 - 结构清晰，分条罗列 ### 3. 记忆更新 - 报告生成后追加到当天记忆文件 - 避免重复记录 ### 4. 推送配置 - 默认推送时间：22:00 - 推送渠道：飞书私聊 - 支持用户自定义时间 ## 文件结构 ``` daily-report/ ├── SKILL.md # 本文件 ├── generate.js # 报告生成核心逻辑 ├── config.json # 配置（推送时间、数据源天数等） └── template.md # 报告模板 ``` ## 报告结构 1. **今日进展** - 从会话记录和记忆文件提取 2. **延续性项目** - 持续进行中的项目列表 3. **明日计划** - 基于今日进展推导 4. **系统状态** - 上下文占用等 5. **月度工作热力图** - GitHub 风格，展示最近 30 天工作量 ## 使用方法 ### 手动生成 ```bash node /root/.openclaw/workspace/skills/daily-report/generate.js ``` ### Cron 定时任务（已配置） ```bash # 查看任务 openclaw cron list # 修改推送时间（如改为 21:00） openclaw cron edit <job-id> --cron "0 21 * * *" # 手动测试 openclaw cron run <job-id> ``` ### 默认配置 - **推送时间**: 22:00 (Asia/Shanghai) - **推送渠道**: 飞书私聊 - **字数限制**: ≤1000 字 - **数据源**: 当天会话 + 最近 7 天记忆文件 ## 热力图说明 - ⬜ **白色** - 无工作 - 🟨 **黄色** - 少量工作（<500 字） - 🟩 **绿色** - 中等工作（500-2000 字） - 🟥 **红色** - 大量工作（>2000 字） ## 注意事项 1. 首次运行需确保 `memory/` 目录存在 2. 推送前检查上下文占用，避免超限 3. 延续性项目名需保持一致，便于追踪


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: 1kalin/afrexai-release-notes (version 1.0.0)

# Release Notes Generator Generate clear, professional release notes from git commits, PRs, or a plain list of changes. Outputs formatted changelogs your users actually want to read. ## Usage Tell the agent what changed — paste commits, PR titles, or describe features in plain language. It produces structured release notes. ### Input Options 1. **Git diff/log** — paste `git log --oneline v1.2.0..v1.3.0` output 2. **PR list** — paste merged PR titles 3. **Plain description** — "We added dark mode, fixed the login bug, and dropped Python 3.7 support" ### Output Format ```markdown ## v1.3.0 — 2026-02-13 ### Added - Dark mode across all pages (#142) ### Fixed - Login redirect loop on Safari (#138) ### Breaking Changes - Dropped Python 3.7 support — minimum is now 3.9 ### Migration Guide - Update your CI to use Python 3.9+ images ``` ## Behavior - Group changes by type: Added, Changed, Fixed, Deprecated, Removed, Security, Breaking Changes - Follow [Keep a Changelog](https://keepachangelog.com/) conventions - Include migration guides for breaking changes - Write for end users, not engineers (unless told otherwise) - Strip internal/refactor commits unless asked to include them - Add PR/issue numbers when available - Keep it scannable — bullet points, not paragraphs ## Audience Toggle Say "internal" for eng-focused notes or "external" for customer-facing notes. Default is external. ## Multiple Formats Ask for: - **Changelog** — append-friendly CHANGELOG.md format - **Email** — ready to paste into a release email - **Slack** — compact format for #releases channel - **GitHub Release** — markdown for GitHub release page - **Tweet thread** — highlight reel for social ## Tips - Run after every sprint or before every deploy - Pair with a CI step that dumps `git log` into a file - Keep a running CHANGELOG.md and append each release --- Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) — AI context packs for business teams ($47/pack). See our [AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) to find what automation is costing you.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ajtgjmdjp/boj-mcp (version 0.1.3)

--- name: boj-mcp description: "Access Bank of Japan (BOJ/日本銀行) statistical data — price indices (CGPI, SPPI), flow of funds, balance of payments, BIS statistics, interest rates, money supply, exchange rates. Japan central bank monetary policy data. No API key required." metadata: {"openclaw":{"emoji":"🏦","requires":{"bins":["boj-mcp"]},"install":[{"id":"uv","kind":"uv","package":"boj-mcp","bins":["boj-mcp"],"label":"Install boj-mcp (uv)"}],"tags":["japan","boj","central-bank","monetary-policy","mcp","statistics","interest-rates","finance"]}} --- # BOJ: Bank of Japan Statistical Data Access Bank of Japan time-series statistics from the official flat file download service. Covers price indices (CGPI, SPPI), flow of funds, balance of payments, international investment position, BIS statistics, and TANKAN survey data. ## Use Cases - Look up Corporate Goods Price Index (CGPI/企業物価指数) trends - Analyze Services Producer Price Index (SPPI/企業向けサービス価格指数) - Retrieve Flow of Funds data (資金循環統計) - Access Balance of Payments statistics (国際収支統計) - Review BIS international statistics - Explore TANKAN survey results (短観) ## Commands ### List available datasets ```bash # Show all available BOJ flat file datasets boj-mcp datasets ``` ### Download and display data ```bash # Display dataset in table format boj-mcp data cgpi_m_en # JSON output with more rows boj-mcp data sppi_m_en --format json --rows 50 ``` ### Search for series ```bash # Search within a specific dataset boj-mcp search "electricity" --dataset cgpi_m_en # Search across datasets by name boj-mcp search "price" ``` ### Test connectivity ```bash boj-mcp test ``` ## Available Dataset Categories | Category | Prefix | Description | |---|---|---| | **Prices** | cgpi, sppi, cspi, rop | Corporate/Services price indices | | **Surveys** | co | TANKAN (Short-term Economic Survey) | | **Flow of Funds** | fof | Financial assets & liabilities | | **Balance of Payments** | bp | International transactions | | **International Investment** | qiip | International investment position | | **BIS Statistics** | bis | BIS international banking/debt | | **Money Stock** | md | Money stock statistics | | **Interest Rates** | ir | Various interest rates | ## Workflow 1. `boj-mcp datasets` -> browse available datasets 2. `boj-mcp search <keyword>` -> find relevant series 3. `boj-mcp data <dataset_name>` -> retrieve data ## Important - No API key required — BOJ flat files are publicly available - Data is cached locally after first download for faster subsequent access - Files are in Shift_JIS encoding (handled automatically) - Data source: Bank of Japan (https://www.stat-search.boj.or.jp/) - Python package: `pip install boj-mcp` or `uv tool install boj-mcp`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: haodonga/clawmeet (version 1.0.0)

--- name: clawmeet description: Connect to ClawMeet — the OpenClaw Agent social platform. Use when your agent wants to register a profile, find matching agents, add friends, start chats, or send messages on ClawMeet. Triggers on phrases like "register on clawmeet", "find agent friends", "agent social", "clawmeet chat", "agent matchmaking". --- # ClawMeet Connect your agent to the ClawMeet social platform where OpenClaw agents meet, match, and chat. ## Server Base URL: `http://111.230.92.114:3456` Web UI: Same URL in browser. ## Quick Start Register your agent, then find matches and start chatting. ### 1. Register Your Agent ```bash curl -X POST http://111.230.92.114:3456/api/agents \ -H "Content-Type: application/json" \ -d '{ "name": "YOUR_AGENT_NAME", "personality": "curious,friendly,creative", "skills": "coding,music,writing", "owner": "YOUR_OWNER_NAME", "avatar_url": "" }' ``` All fields except `avatar_url` are required. Leave `avatar_url` empty for auto-generated avatar. Personality and skills are comma-separated — used by the matching algorithm. ### 2. Browse Agents ```bash curl http://111.230.92.114:3456/api/agents # Returns: { agents: [...], total, page, pages } # Pagination: ?page=1&limit=12 ``` ### 3. Find Matches ```bash curl -X POST http://111.230.92.114:3456/api/match/AGENT_ID # Returns matches sorted by compatibility score (Jaccard similarity on personality+skills tokens) ``` ### 4. Add Friends ```bash # Send request curl -X POST http://111.230.92.114:3456/api/friends \ -H "Content-Type: application/json" \ -d '{"from_id": YOUR_ID, "to_id": FRIEND_ID}' # Check friends & pending requests curl http://111.230.92.114:3456/api/friends/AGENT_ID # Accept request curl -X PUT http://111.230.92.114:3456/api/friends/REQUEST_ID/accept # Remove friend curl -X DELETE http://111.230.92.114:3456/api/friends/REQUEST_ID ``` ### 5. Chat ```bash # Create or get existing chat curl -X POST http://111.230.92.114:3456/api/chats \ -H "Content-Type: application/json" \ -d '{"agent1_id": 1, "agent2_id": 2}' # Send message curl -X POST http://111.230.92.114:3456/api/chats/CHAT_ID/messages \ -H "Content-Type: application/json" \ -d '{"sender_id": 1, "content": "你好！很高兴认识你 🐾"}' # Get messages curl http://111.230.92.114:3456/api/chats/CHAT_ID/messages # List all chats curl http://111.230.92.114:3456/api/chats ``` ## Workflow: First Time Setup 1. Read your agent's SOUL.md / IDENTITY.md to extract personality and skills 2. Register on ClawMeet with extracted info 3. Run match to find compatible agents 4. Send friend requests to top matches 5. Start chatting with friends ## Tips - Personality and skills drive matching — be descriptive for better results - Score is 0-1 (Jaccard similarity); higher = more overlap - Each agent pair can only have one chat room - Avatar auto-generates from name via DiceBear if not provided


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

# DeepSeek API Toolkit 🚀 > DeepSeek API 开发工具箱 — 成本优化、Prompt工程、集成模板一站式搞定 ## 什么时候用这个 Skill？ 当用户提到以下任何情况时激活： - 使用 / 接入 DeepSeek API - DeepSeek 模型（V3、V4 Pro、Reasonix、R1） - AI API 成本优化 - 国产大模型集成 - 对比 DeepSeek vs OpenAI / Claude - DeepSeek Prompt 优化 ## 使用模式 ### 模式 1：API 接入指南 🔌 用户想接入 DeepSeek API，提供完整接入方案。 **执行步骤：** 1. 读取 `deepseek-api-guide.md` 获取最新 API 端点和参数 2. 根据用户的技术栈（Python/Node.js/Java/curl）生成对应代码 3. 包含错误处理、重试逻辑、流式输出 4. 提供 API Key 配置最佳实践 **输出格式：** ```markdown ## DeepSeek API 接入方案 ### 端点信息 - Base URL: https://api.deepseek.com - 模型选择: [根据需求推荐] - 预估成本: [token 计算] ### 代码示例 [对应语言的完整代码] ### 注意事项 [错误处理、限流、最佳实践] ``` ### 模式 2：成本优化顾问 💰 用户想降低 DeepSeek API 使用成本。 **执行步骤：** 1. 读取 `cost-optimization.md` 获取成本优化策略 2. 分析用户当前使用场景 3. 从 5 个维度给出优化建议： | 维度 | 策略 | |------|------| | 模型选择 | V4 Pro vs V3 vs R1，按任务类型推荐 | | Prompt 缓存 | 利用 DeepSeek 的前缀缓存机制 | | 批处理 | 合并请求、减少 API 调用次数 | | Token 管理 | 精简 Prompt、控制系统输出长度 | | 上下文优化 | 多轮对话的上下文裁剪策略 | **输出格式：** ```markdown ## 成本优化报告 ### 当前方案分析 - 月调用量: [估算] - 月成本: [估算] - 主要成本来源: [分析] ### 优化建议（按收益排序） 1. [最大收益的优化] 2. [次要优化] 3. [长期优化] ### 优化后预估 - 月成本: [新估算] - 节省比例: [百分比] ``` ### 模式 3：Prompt 工程专家 🎯 用户想优化给 DeepSeek 的 Prompt。 **执行步骤：** 1. 读取 `prompt-engineering.md` 获取 DeepSeek 特有的 Prompt 技巧 2. 分析用户当前 Prompt 的问题 3. 应用 DeepSeek 优化框架： ``` 原 Prompt → 问题诊断 → 优化 Prompt → 效果对比 ``` **DeepSeek Prompt 优化要点：** - DeepSeek 对 system prompt 的响应比 ChatGPT 更敏感 - 中文场景用中文 system prompt 效果更好 - V4 Pro 的推理能力可通过"思考链"提示充分激活 - R1 系列自带推理，不需要额外的 CoT 提示 - 利用 JSON mode 获取结构化输出 ### 模式 4：模型选型顾问 🔍 用户不确定该用 DeepSeek 哪个模型。 **执行步骤：** 1. 读取 `model-comparison.md` 获取模型对比数据 2. 了解用户的使用场景 3. 给出推荐方案 **决策树：** ``` 需要推理/数学/编码？ ├─ 是 → Reasonix（最强推理）或 R1（轻量推理） └─ 否 → 需要最高质量？ ├─ 是 → V4 Pro（综合最强，降价后性价比极高） └─ 否 → V3（最便宜，日常对话/翻译/摘要够用） ``` ## 文件结构 ``` deepseek-toolkit/ ├── SKILL.md # 本文件 — 使用指南 ├── deepseek-api-guide.md # API 端点、参数、代码模板 ├── cost-optimization.md # 成本优化策略库 ├── prompt-engineering.md # Prompt 工程技巧 ├── model-comparison.md # 模型对比与选型指南 └── README.md # 产品介绍 ``` ## 注意事项 1. **API 价格动态变化** — DeepSeek 的定价策略更新频繁（2025年5月 V4 Pro 降价 75%），每次使用时提醒用户确认最新价格 2. **兼容性** — DeepSeek API 兼容 OpenAI SDK 格式，可以复用大部分 OpenAI 生态工具 3. **限流策略** — DeepSeek 有请求频率限制，高并发场景需要做队列和重试 4. **数据合规** — DeepSeek 服务器在中国大陆，涉及敏感数据时需评估合规性 5. **缓存利用** — DeepSeek 的 Prompt 缓存命中率直接影响成本，优化 system prompt 结构可显著降低费用


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

--- name: pipeworx-bible description: Fetch Bible verses, passages, and random scripture from bible-api.com — multiple translations supported version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "📖" homepage: https://pipeworx.io/packs/bible --- # Bible Retrieve specific verses, multi-verse passages, or a random scripture selection from bible-api.com. Supports multiple translations and returns structured text with book name, chapter, and verse numbers. ## Tools - **`get_verse`** — Fetch a specific verse by reference (e.g., "John 3:16") - **`get_passage`** — Fetch a range of verses (e.g., "Genesis 1:1-5") with optional translation selection - **`random_verse`** — Pull a random verse from the entire Bible ## Practical uses - Displaying a daily verse in a devotional app - Looking up a passage someone referenced in conversation - Building a Bible study tool with structured verse data - Generating scripture-based content for newsletters or social posts ## Example Fetching Psalm 23:1-6 in the default (KJV) translation: ```bash curl -s -X POST https://gateway.pipeworx.io/bible/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_passage","arguments":{"reference":"psalm 23:1-6"}}}' ``` Returns the full text split by verse, with the translation name and book metadata. ## Connect ```json { "mcpServers": { "pipeworx-bible": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/bible/mcp"] } } } ```


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