OpenClaw/clawhub-security-signals huggingface dataset:
Skill: harleyscodes/hashpack-wallet (version 1.0.0)

--- name: hashpack-wallet description: Integrate HashPack wallet for Hedera blockchain authentication. Use for: (1) Adding HashPack login to webapps, (2) Signing Hbar transactions, (3) Connecting to Hedera DApps, (4) Getting account balance. --- # HashPack Wallet Integration ## Quick Start ```typescript // Detect HashPack const hashpack = (window as any).hashpack; // Connect const result = await hashpack.connect(); // Get account ID const accountId = result.accountId; // e.g., "0.0.12345" ``` ## Account ID Format Hedera account IDs are format: `0.0.12345` (shard.realm.num) ## Key Methods ```typescript // Connect (opens popup) await hashpack.connect(); // Sign and submit transaction const tx = new TransferTransaction() .addHbarTransfer(from, -10) .addHbarTransfer(to, 10); await hashpack.signTransaction(tx); // Get balance const balance = await new AccountBalanceQuery() .setAccountId(accountId) .execute(client); // Disconnect hashpack.disconnect(); ``` ## Environment - **Mainnet**: `https://mainnet.hashio.io/api` - **Testnet**: `https://testnet.hashio.io/api` - **Previewnet**: `https://previewnet.hashio.io/api` ## Transaction Types - `TransferTransaction` - Send HBAR/tokens - `ContractExecuteTransaction` - Call contract - `TokenAssociateTransaction` - Associate with token - `TokenMintTransaction` - Mint tokens - `TopicCreateTransaction` - Create HCS topic


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: branexp/gh-action-gen (version 1.0.1)

--- name: gh-action-gen description: Generate GitHub Actions workflows from plain English. Use when setting up CI. --- # GitHub Action Generator Stop copy-pasting workflow YAML from StackOverflow. Describe what you want and get a working GitHub Actions workflow. **One command. Zero config. Just works.** ## Quick Start ```bash npx ai-github-action "test and deploy on push to main" ``` ## What It Does - Generates complete GitHub Actions workflow files - Handles common patterns like test, build, deploy - Includes caching for faster runs - Supports multiple deploy targets ## Usage Examples ```bash # Test and deploy npx ai-github-action "test and deploy on push to main" # PR checks npx ai-github-action "run eslint and prettier on PRs" --install # Docker workflow npx ai-github-action "build docker image and push to ECR" -o deploy.yml # Scheduled job npx ai-github-action "run database backup every night at 2am" ``` ## Best Practices - **Use secrets** - never hardcode credentials - **Cache dependencies** - saves minutes per run - **Fail fast** - run quick checks first - **Use matrix builds** - test multiple node versions ## When to Use This - Setting up CI for a new repo - Adding deployment automation - Creating custom workflows - Learning GitHub Actions syntax ## Part of the LXGIC Dev Toolkit This is one of 110+ free developer tools built by LXGIC Studios. No paywalls, no sign-ups, no API keys on free tiers. Just tools that work. **Find more:** - GitHub: https://github.com/LXGIC-Studios - Twitter: https://x.com/lxgicstudios - Substack: https://lxgicstudios.substack.com - Website: https://lxgicstudios.com ## Requirements No install needed. Just run with npx. Node.js 18+ recommended. Needs OPENAI_API_KEY environment variable. ```bash npx ai-github-action --help ``` ## How It Works Takes your plain English description and generates GitHub Actions YAML with the right triggers, jobs, and steps. The AI knows common patterns and best practices for different workflows. ## License MIT. Free forever. Use it however you want.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: kaising-openclaw1/xiaoming-writing-assistant (version 1.0.0)

--- name: ai-writing-assistant description: AI 写作助手 - 文章大纲生成、段落扩写、润色修改、标题优化 --- # AI Writing Assistant AI 驱动的写作助手，帮助快速生成高质量内容。 ## 功能 - ✅ 文章大纲生成 - ✅ 段落扩写 - ✅ 内容润色 - ✅ 标题优化 - ✅ 语法检查 ## 使用 ```bash # 生成大纲 clawhub writing outline --topic "AI 技能开发指南" # 段落扩写 clawhub writing expand --text "AI 可以帮助写作" --length 500 # 润色修改 clawhub writing polish --text "原始文本" # 优化标题 clawhub writing title --content "文章内容" ``` ## 示例 ### 生成大纲 ```bash clawhub writing outline --topic "如何开始副业" ``` 输出: ``` # 如何开始副业 ## 1. 选择方向 - 技能变现 - 内容创作 - 服务外包 ## 2. 准备工具 - 时间管理 - 技能提升 ## 3. 开始执行 - 第一单 - 积累口碑 ``` ### 段落扩写 ```bash clawhub writing expand --text "AI 写作很快" --length 300 ``` ### 润色修改 ```bash clawhub writing polish --text "这个产品很好用，我很喜欢" ``` 输出: ``` 这款产品表现出色，使用体验极佳，令人印象深刻。 ``` ## 注意事项 - 需要联网使用 - 支持中文和英文 - 单次处理不超过 5000 字


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iiawhite-sir/edge-tts-auto (version 1.0.0)

# edge-tts-auto 文本转语音技能 --- ## 📌 技能简介 `edge-tts-auto` 是一个 **OpenClaw 原生兼容** 的文本转语音技能，支持自动安装依赖，无需手动配置环境。 - 自动安装 `pipx` + `edge-tts` - 支持中文女声（`zh-CN-XiaoxiaoNeural`） - 输出为标准 MP3 文件，可直接导入剪映等工具 - 适配 OpenClaw 2026.4.15 版本 --- ## 📁 目录结构 ``` ~/.openclaw/skills/edge-tts-auto/ ├── skill.json # 技能配置文件 ├── main.sh # 执行脚本（含自动安装逻辑） └── skill.md # 本文档 ``` --- ## ⚙️ 安装方法 1. **复制技能目录** 将整个 `edge-tts-auto` 文件夹复制到： ```bash ~/.openclaw/skills/ ``` 2. **赋予执行权限** ```bash chmod +x ~/.openclaw/skills/edge-tts-auto/main.sh ``` 3. **重启 OpenClaw 网关** ```bash openclaw gateway restart ``` --- ## 🚀 使用方法 ### 1. 自然语言调用（推荐） 在 OpenClaw 对话中直接使用： ``` 把下面这段文字转成语音，保存到桌面： “你好，这是一段由 edge-tts-auto 生成的测试语音。” ``` ### 2. 命令行调用 ```bash openclaw skill run edge-tts-auto \ --text "你好，世界" \ --output_path "/mnt/c/Users/你的用户名/Desktop/voice.mp3" ``` --- ## 🔧 参数说明 | 参数 | 必填 | 说明 | 示例 | | :--- | :--- | :--- | :--- | | `text` | ✅ | 要转换的文本内容 | `"欢迎使用 edge-tts-auto"` | | `output_path` | ✅ | 输出 MP3 的完整路径 | `"/mnt/c/Users/xxx/Desktop/voice.mp3"` | --- ## ✨ 自动安装逻辑 第一次运行时，脚本会自动执行以下操作： 1. 更新系统包列表 2. 安装 `pipx` 和 `jq` 3. 配置 `pipx` 环境变量 4. 安装 `edge-tts` 工具 5. 调用 `edge-tts` 生成语音 --- ## 🎯 常见问题 ### 1. 权限不足报错 执行： ```bash chmod +x ~/.openclaw/skills/edge-tts-auto/main.sh ``` ### 2. 找不到 `edge-tts` 重启 OpenClaw 网关后重试，脚本会自动完成安装。 ### 3. 输出文件无法在 Windows 打开 确保 `output_path` 是 WSL 可访问的 Windows 目录，例如： `/mnt/c/Users/你的用户名/Desktop/voice.mp3` --- ## 📄 许可证 MIT License，可自由修改和分发。

path: main.sh
content: #!/bin/bash
set -e

# ==========================
# 自动安装逻辑（第一次运行）
# ==========================
if ! command -v edge-tts &> /dev/null; then
  echo "正在自动安装 edge-tts..."
  sudo apt update
  sudo apt install -y pipx jq
  pipx ensurepath
  source ~/.bashrc
  pipx install edge-tts
  echo "edge-tts 安装完成！"
fi

# ==========================
# 执行文本转语音
# ==========================
TEXT=$(echo "$1" | jq -r '.text')
OUTPUT=$(echo "$1" | jq -r '.output_path')

edge-tts --text "$TEXT" \
         --voice zh-CN-XiaoxiaoNeural \
         --write-media "$OUTPUT"

echo "{\"success\":true,\"file\":\"$OUTPUT\"}"

path: skill.json
content: {
  "name": "edge-tts-auto",
  "description": "自动安装edge-tts并实现文本转语音，无需手动配置",
  "version": "1.0.0",
  "author": "auto",
  "tools": [
    {
      "name": "text_to_speech",
      "description": "文本转语音",
      "inputSchema": {
        "type": "object",
        "properties": {
          "text": { "type": "string" },
          "output_path": { "type": "string" }
        },
        "required": ["text", "output_path"]
      }
    }
  ]
}

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: quinlanjager/posthog-query (version 1.0.0)

--- name: posthog-query description: Run SQL queries against PostHog product analytics data using the PostHog CLI. Use when checking pageviews, event counts, trends, or any analytics data from PostHog. metadata: {"openclaw": {"emoji": "🦔", "requires": {"bins": ["posthog-cli"]}, "install": "npm install -g posthog-cli"}} --- # PostHog Query Skill Run HogQL (ClickHouse-compatible SQL) queries against PostHog via the CLI. ## One-Time Setup ```bash posthog-cli login # authenticate interactively; stores token in ~/.posthog/credentials.json ``` Requires API key scope: `query:read`. ## Command ```bash posthog-cli exp query run "<SQL>" ``` Results are printed as JSON lines to stdout. The CLI reads auth from `~/.posthog/credentials.json` (set up via `posthog-cli login`). ## Property Access Syntax Use bracket notation for event properties — dot notation with quoted keys does not work: ```sql -- ✅ Correct properties['$current_url'] properties['$browser'] -- ❌ Wrong properties.'$current_url' ``` ## Examples **Count all pageviews:** ```bash posthog-cli exp query run "SELECT count() as pageviews FROM events WHERE event = '\$pageview'" ``` **Filter by URL:** ```bash posthog-cli exp query run "SELECT count() as pageviews FROM events WHERE event = '\$pageview' AND properties['\$current_url'] LIKE 'https://example.com/%'" ``` **7-day daily trend:** ```bash posthog-cli exp query run "SELECT toDate(timestamp) as date, count() as pageviews FROM events WHERE event = '\$pageview' AND timestamp >= now() - INTERVAL 7 DAY GROUP BY date ORDER BY date" ``` **Recent events:** ```bash posthog-cli exp query run "SELECT event, timestamp FROM events ORDER BY timestamp DESC LIMIT 10" ``` ## Other Subcommands - `posthog-cli exp query editor` — interactive query editor - `posthog-cli exp query check "<SQL>"` — syntax/type check without running - Append `--debug` to `run` to get the full JSON response (columns, types, cache info) ## Notes - HogQL is ClickHouse-compatible SQL — standard ClickHouse functions apply - Shell-escape `$` in event names: `'\$pageview'` or use double quotes carefully - The `--debug` flag returns full metadata including column types and cache status


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/tiktok-influencer-marketing (version 1.0.0)

# TikTok Influencer Marketing Plan and execute TikTok influencer marketing campaigns for e-commerce brands. Find creators, negotiate deals, track ROI, and scale UGC content across TikTok and TikTok Shop. ## Capabilities - Creator discovery by niche, audience size, and engagement rate - Outreach templates and negotiation frameworks - Campaign briefing and content guidelines - TikTok Shop affiliate vs paid partnership comparison - UGC content repurposing strategy - ROI tracking: CPM, CPA, ROAS by creator - Micro vs macro influencer selection criteria - Contract and usage rights best practices ## Install ```bash npx skills add nexscope/tiktok-influencer-marketing ``` ## Usage Ask your AI agent: - "Find TikTok creators for my skincare brand in the \$500-2000 budget range." - "Create an influencer campaign brief for my product launch." - "How do I measure ROI from TikTok influencer partnerships?" ## Output Structured recommendations with actionable steps, benchmarks, and platform-specific guidance. --- *Built by [Nexscope](https://www.nexscope.ai/) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brndmk/brandomica (version 1.0.5)

--- name: brandomica description: Check brand name safety across domains, social handles, trademarks, app stores, and SaaS channels. Get availability scores, safety assessments, and filing readiness decisions. version: 1.0.5 homepage: https://www.brandomica.com/mcp-server metadata: openclaw: requires: bins: - npx emoji: "🔍" homepage: https://www.brandomica.com install: - kind: node package: brandomica-mcp-server --- # Brandomica Lab — Brand Name Verification You have access to the Brandomica MCP server for brand name safety checks. Use these tools to help users verify brand names before launching. ## Available Tools - **brandomica_check_all** — Full brand check (domains, social, trademarks, app stores, SaaS + availability score + safety assessment). Use `mode: "quick"` for fast local-only checks, or `mode: "full"` for comprehensive results. - **brandomica_assess_safety** — Quick safety risk decision (0-100 score, risk level, 6-signal breakdown). Start here for a fast go/no-go. - **brandomica_filing_readiness** — Filing readiness summary (verdict, risk, top conflicts, confidence). Use after safety assessment shows medium/high risk. - **brandomica_compare_brands** — Compare 2-5 brand name candidates side by side with recommendation. - **brandomica_batch_check** — Check 2-50 names at once, sorted by score. - **brandomica_brand_report** — Generate a timestamped evidence document (JSON). - **brandomica_check_domains** — Domain availability + pricing for 6 TLDs. - **brandomica_check_social** — Social handle availability (GitHub, Twitter, TikTok, LinkedIn, Instagram). - **brandomica_check_trademarks** — USPTO + EUIPO trademark search. - **brandomica_check_google** — Google Search presence (competitor overlap detection). - **brandomica_check_appstores** — iOS App Store + Google Play search. - **brandomica_check_saas** — Package registries (npm, PyPI, crates.io, etc.) + SaaS channels. ## Recommended Workflow 1. Start with `brandomica_assess_safety` for a quick risk decision 2. If medium/high risk, run `brandomica_filing_readiness` for filing details 3. Use `brandomica_check_all` for full evidence across all channels 4. Compare candidates with `brandomica_compare_brands` ## Brand Name Format Brand names must be lowercase alphanumeric (hyphens allowed). The tools will reject invalid characters. ## Automation Ideas - **Brainstorm + validate loop**: Generate name ideas, then batch-check them all with `brandomica_batch_check` - **Domain shopping**: Use `brandomica_check_domains` to find available domains with pricing - **Trademark clearance**: Run `brandomica_filing_readiness` before any trademark filing - **Competitive analysis**: Use `brandomica_check_google` to see who else uses a similar name


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jp4g/noir-developer (version 0.1.0)

--- name: noir-developer description: Develop Noir (.nr) codebases. Use when creating a project or writing code with Noir. --- # Noir Developer ## Workflow 1. Compile (`nargo compile`) Noir program into ACIR. 2. Generate witness (`nargo execute` or NoirJS execute) based on ACIR and user inputs. 3. Prove using ACIR and witness with the selected proving backend. 4. Verify proof with the selected proving backend. ## Task Patterns ### Environment If the environment is unsupported by `nargo` (e.g. native Windows), guide the user to using GitHub Codespaces (https://noir-lang.org/docs/tooling/devcontainer#using-github-codespaces) or a supported setup (WSL, Docker, or VM). ### Plan Define private inputs, public inputs (if any), and public outputs (if any) for each Noir program. ### Project Creation When creating a Noir project, use `nargo new` or `nargo init` to scaffold it. ### Compilation Use `nargo` (not `noir_wasm`) for compilation; it is the maintained path. ### Validation Run `nargo test` to validate Noir implementations. ### Proving Backend Confirm the proving backend choice before implementation details. If the user selects Barretenberg, read `references/barretenberg.md`. ## References - Run `nargo --help` for the full list of commands. - Read https://noir-lang.org/docs/ for language syntax, dependencies, and tooling. - Proving backends: - For Barretenberg specifics, read `references/barretenberg.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mikoboulot/owncloud-sync (version 1.0.7)

# owncloud-sync ## Purpose This skill tracks files created or imported into Google Drive over a specified period (e.g., last 30 days), verifies their presence on the OwnCloud server, and generates a daily report emailed to the user. The report lists new Google Drive files and flags any missing or outdated files on OwnCloud, helping the user decide which files to copy. ## Rationale Google Drive serves as a convenient workspace to drop and edit files. This skill helps decide whether to push files to OwnCloud or to delete/retain them on Google Drive. ## Prerequisites on OwnCloud Server - OwnCloud lacks a satisfactory built-in file search service, so an indexing service is required. - A Go program (`allfiles-service.go`) is provided at https://github.com/MikoBoulot/openclaw-skills. - Compile the Go program with: ``` go build -ldflags=-s -w -o /usr/local/bin/allfiles-service allfiles-service.go ``` - Configure credentials (`BASIC_USER`, `BASIC_PASS`) in the service definition to match the `ALLFILES_USER` and `ALLFILES_PASS` in the skill. - Install and enable the provided systemd service `allfiles.service`. - Place valid certificates (default location: `/etc/allfiles/certs/`). ## Operating Mode - The script `owncloud-sync.sh` is located in `.openclaw/workspace/skills/owncloud-sync/`. - Run the script manually or on demand; it requires Zsh. ## Configuration - The variables `ALLFILES_URL`, `ALLFILES_USER`, and `ALLFILES_PASS` in `owncloud.json` specify the OwnCloud indexing service credentials. - `GOG_ACCOUNT` (Google Drive account) and `EMAIL_RECIPIENT` (report recipient) are set within the script. - `PERIOD_DAYS` defines the date range for querying Google Drive, also set in the script. ## Report - All files created or imported during the specified period are checked on OwnCloud. - The report indicates each file s status: OK, MISSING, or NEED UPDATE (if OwnCloud s version is older than Google Drive s).

path: set-gog-account.sh
content: #!/bin/zsh

# This helper script sets the environment variable GOG_ACCOUNT for testing
export GOG_ACCOUNT='[REDACTED_SECRET]'


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: hebilicious/zeko (version 0.0.1-main.11.2)

--- name: zeko description: "Use when a user needs to actually use or build on Zeko: bridge with Bridge CLI or Bridge SDK, get testnet funds, find the right Zeko and Mina endpoints, run GraphQL or curl queries, understand sequencer and archive-node roles, or build zkApps on Zeko with o1js or OCaml. This skill is for public user and builder workflows, especially terminal-driven and non-browser automation flows." metadata: openclaw: homepage: https://docs.zeko.io/developers/tools/agent-skills primaryEnv: MINA_PRIVATE_KEY requires: env: - MINA_PRIVATE_KEY - GITHUB_TOKEN - PUBLIC_KEY - ADDRESS bins: - curl - node - npx install: - kind: node package: "@zeko-labs/bridge-cli" bins: - zeko-bridge - kind: node package: "@zeko-labs/faucet-cli" bins: - zeko-faucet - kind: node package: o1js --- # Zeko Use this skill when the user wants to use Zeko, inspect the network, bridge assets, get faucet funds, or build on Zeko. Defaults: - For non-browser automation, prefer explicit key-based CLI or HTTP flows over wallet UI steps. - Only discuss Auro Wallet when the agent is actively driving a browser with extension access. - Start with the smallest relevant reference file in this skill instead of a broad docs dump. - Use the public docs pages behind `https://docs.zeko.io` through the targeted references below. - Default to gateway-backed Mina testnet endpoints and direct Zeko GraphQL endpoints from `references/40-endpoints-and-curl.md`. - Use the live node for current chain config and the archive node for historical actions and events. Load only what you need: - Docs map and routing: [references/10-navigation.md](references/10-navigation.md) - Agent-first behavior: [references/10-agent-workflows.md](references/10-agent-workflows.md) - User flows and browser-wallet exceptions: [references/20-user-flows.md](references/20-user-flows.md) - Bridge CLI, Bridge SDK, and faucet workflows: [references/30-bridge-and-faucet.md](references/30-bridge-and-faucet.md) - Endpoints and `curl` queries: [references/40-endpoints-and-curl.md](references/40-endpoints-and-curl.md) - Building with `o1js` or OCaml: [references/50-build-on-zeko.md](references/50-build-on-zeko.md) - Sequencer, archive node, and DA layer: [references/60-sequencer-archive-and-da.md](references/60-sequencer-archive-and-da.md)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: krx1511/dify (version 1.0.0)

--- name: dify description: Dify AI应用开发平台指南。用于构建LLM应用、工作流、Agent和知识库。当用户需要(1)使用Dify创建AI应用 (2)设计LLM工作流 (3)配置知识库RAG (4)开发AI Agent (5)调用Dify API (6)自托管部署Dify时激活此技能。 --- # Dify AI应用开发平台 Dify是开源的LLM应用开发平台，支持快速构建AI应用、工作流和Agent。 ## 核心概念 ### 应用类型 - **Chat App** - 对话型应用，支持会话持久化，适合聊天机器人、客服AI - **Workflow App** - 工作流应用，无状态执行，适合翻译、写作、摘要 - **Agent** - 智能体应用，支持工具调用和自主规划 - **Completion App** - 文本补全应用，单次请求响应 ### 核心组件 - **Studio** - 应用构建工作台 - **Knowledge Base** - 知识库，支持RAG检索增强 - **Model Providers** - 模型提供商配置 - **Tools** - 工具集成 ## 工作流节点 ### 输入输出 - **User Input** - 用户输入，定义输入变量 - **Output** - 输出结果 ### 逻辑控制 - **IF/ELSE** - 条件分支 - **Iteration** - 迭代循环 - **Parallel** - 并行执行 ### 数据处理 - **Parameter Extractor** - 参数提取，用LLM从自然语言提取结构化数据 - **List Operator** - 列表操作，过滤和转换数组 - **Variable Aggregator** - 变量聚合 - **Template** - 模板渲染 ### LLM节点 - **LLM** - 大语言模型调用 - **Question Classifier** - 问题分类 - **Knowledge Retrieval** - 知识检索 ### 工具节点 - **HTTP Request** - HTTP请求 - **Code** - 代码执行(Python/JavaScript) - **Tool** - 工具调用 ## 快速开始 ### 部署Dify (Docker) ```bash # 克隆最新版本 git clone --branch "$(curl -s https://api.github.com/repos/langgenius/dify/releases/latest | jq -r .tag_name)" https://github.com/langgenius/dify.git # 启动 cd dify/docker cp .env.example .env docker compose up -d ``` 访问 `http://localhost/install` 初始化管理员账户。 ### 系统要求 - CPU >= 2 Core - RAM >= 4 GiB - Docker 19.03+ - Docker Compose 1.28+ ## API调用 ### 认证 所有API请求需要在Header中携带API Key: ``` Authorization: Bearer {api_key} ``` ### 执行工作流 ```bash POST /v1/workflows/run Content-Type: application/json { "inputs": { "query": "翻译这段文字..." }, "response_mode": "blocking", # 或 "streaming" "user": "user-123" } ``` ### 发送聊天消息 ```bash POST /v1/chat-messages Content-Type: application/json { "query": "你好", "response_mode": "streaming", "user": "user-123", "conversation_id": "" # 首次为空，后续传入返回的conversation_id } ``` ### 响应模式 - **blocking** - 同步等待完整响应 - **streaming** - SSE流式响应 ## 知识库 ### 创建知识库 1. Studio → Knowledge → Create Knowledge 2. 上传文档 (支持txt, markdown, pdf, docx, html等) 3. 选择索引模式: - **High Quality** - 高质量索引，需要Embedding模型 - **Economy** - 经济模式，关键词检索 ### 检索设置 - **Vector Search** - 向量检索 - **Full Text Search** - 全文检索 - **Hybrid Search** - 混合检索 ### 在应用中使用 工作流中添加 Knowledge Retrieval 节点，选择知识库。 ## 详细参考 - [API文档](references/api.md) - 完整API接口说明 - [工作流节点](references/workflow-nodes.md) - 所有节点详细配置 - [知识库配置](references/knowledge-base.md) - 知识库高级设置 - [模型配置](references/model-providers.md) - 支持的模型提供商 ## 资源链接 - 官方文档: https://docs.dify.ai - GitHub: https://github.com/langgenius/dify - 社区: https://github.com/langgenius/dify/discussions


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: as9530272755/message-chunker (version 1.0.0)

--- name: message-chunker description: "智能消息分段工具。自动将长消息拆分成多条发送，避免飞书API消息长度限制。当需要发送长消息（如汇报、方案、日志）时自动激活。" --- # Message Chunker - 消息分段工具 智能分段发送长消息，确保每条消息不超过平台限制。 ## 触发条件 当消息满足以下任一条件时自动分段： - 消息长度超过 4000 字符 - 包含多个独立章节/表格 - 发送失败（Status 400）后重试 ## 平台限制 | 平台 | 单条限制 | 建议 | |------|---------|------| | 飞书 | ~4KB | 每条≤4000字符 | | Telegram | 4096字符 | 每条≤4000字符 | | Discord | 2000字符 | 每条≤1900字符 | | WhatsApp | 65536字符 | 较宽松 | ## 分段规则 ### 1. 按章节分段 ``` ## 章节1 内容... --- 分段 --- ## 章节2 内容... ``` ### 2. 按表格分段 ``` 表格1内容... --- 分割 --- 表格2内容... ``` ### 3. 按段落分段 - 优先在 `---` 分隔线处分段 - 其次在空行处分段 - 避免在句子中间分段 ## 使用方法 ### 自动模式（推荐） 在回复长消息时，Agent会自动判断并分段： ``` # 第1条消息（分段1/n） 内容... # 系统自动发送第2条 内容续... # 系统自动发送第3条 内容续... ``` ### 手动标记分段 在消息中插入分段标记： ``` {{CHUNK}} ``` Agent会在标记处自动分段。 ## 示例 **原始长消息**（10KB）： ``` # 汇报 ## 第一部分 [大量内容] ## 第二部分 [大量内容] ## 第三部分 [大量内容] ``` **分段后**： ``` 第1条: # 汇报\n## 第一部分\n[大量内容] 第2条: ## 第二部分\n[大量内容] 第3条: ## 第三部分\n[大量内容] ``` ## 配置 在 `TOOLS.md` 中可自定义： ```yaml message-chunker: maxChunkSize: 4000 # 单条最大字符数 splitOnHeadings: true # 在标题处分段 splitOnTables: true # 表格后分段 includeContinuation: true # 添加"续"标记 ``` ## 注意事项 1. **不要过度分段** - 能合并的内容尽量合并 2. **保持完整性** - 单个表格不要拆分 3. **添加续接标记** - 让用户知道还有下文 4. **重试机制** - 分段后仍失败则记录到失败队列 --- *创建时间: 2026-03-10* *创建者: 01-老鹰*


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