OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain3/product-desc (version 2.0.0)

--- version: "2.0.0" name: product-desc description: "产品描述生成器。产品文案撰写、SEO优化、卖点提炼、竞品对比、多语言本地化。Product description writer with SEO, bullet points, comparison." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # product-desc 产品描述生成器。产品文案撰写、SEO优化、卖点提炼、竞品对比、多语言本地化。Product description writer with SEO, bullet points, comparison, localization. 产品描述、产品文案、卖点、电商文案。Use when writing product descriptions. ## 使用场景 > 💡 无论你是新手还是专业人士，都能快速上手 ## 可用命令 - **write** — write - **optimize** — optimize - **seo** — seo - **bullet** — bullet - **compare** — compare - **localize** — localize ## 专业建议 - 文案撰写 (write)**: 提供产品名+特点，生成吸引人的描述 - 文案优化 (optimize)**: 粘贴现有描述，获得改进建议 - SEO优化 (seo)**: 提供产品和目标关键词，优化搜索排名 - 卖点提炼 (bullet)**: 提炼5-7个核心卖点 - 竞品对比 (compare)**: 突出差异化优势 --- *product-desc by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help product-desc help # Run product-desc run ``` ## Commands Run `product-desc help` to see all available commands.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sqlservr/sql-server-toolkit (version 1.0.1)

# SQL Server Toolkit Command-line toolkit for Microsoft SQL Server. ## Capabilities - Schema creation - Versioned migrations - Index management - Performance diagnostics - Backup & restore - Bulk import/export ## Connect sqlcmd -S localhost -E sqlcmd -S localhost -U sa -P YourPassword ## Run Script sqlcmd -S localhost -d MyDatabase -i script.sql ## Example Table CREATE TABLE Users ( Id INT IDENTITY(1,1) PRIMARY KEY, Email NVARCHAR(255) NOT NULL UNIQUE, Name NVARCHAR(100) NOT NULL, CreatedAt DATETIME2 DEFAULT SYSDATETIME() ); ## Example Index CREATE INDEX IX_Users_Email ON Users(Email); ## Backup Example BACKUP DATABASE MyDatabase TO DISK = 'C:\backup\MyDatabase.bak' WITH FORMAT, INIT; ## Performance Diagnostics SET STATISTICS IO ON; SET STATISTICS TIME ON;


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lead-magnet-creator (version 1.0.0)

--- name: lead-magnet-creator description: Create lead magnets (PDF guides, checklists, templates) with matching landing pages. Generate qualified leads for your business. metadata: openclaw: requires: {} --- # Lead Magnet Creator Lead-Magnets erstellen, die konvertieren. ## Features - ✅ **PDF-Guides** - Professionelle Checklisten, Anleitungen, Templates - ✅ **Landing Pages** - Next.js kompatibel - ✅ **E-Mail-Opt-in** - Integration vorbereitet - ✅ **DACH-optimiert** - Deutsche & englische Inhalte ## Lead-Magnet-Typen | Typ | Beschreibung | |-----|--------------| | **Checkliste** | Kurze, actionable Schritte | | **Guide** | Ausführliche Anleitung (5-10 Seiten) | | **Template** | Fertige Vorlagen zum Ausfüllen | | **Calculator** | Interaktive Rechner | | **Report** | Daten & Analysen | ## Verwendung ``` Erstelle einen Lead-Magnet für [THEMA]. Zielgruppe: [B2B/B2C] Format: [PDF-Guide/Checkliste/Template] Ziel: [E-Mail-Liste aufbauen] ``` ## Beispiele - "KI-Automation Quickstart Guide" - "SEO-Checkliste für E-Commerce" - "Lead-Generierung Templates" - "Content-Kalender 2026" ## ROI Lead-Magnete sind der effektivste Weg, E-Mail-Listen aufzubauen: - 3-5x höhere Conversion als normale Formulare - Qualifizierte Leads mit echtem Interesse ## Support [REDACTED_SECRET]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/graphql-schema (version 1.0.0)

--- name: graphql-schema description: Deep GraphQL schema workflow—modeling types, queries and mutations, N+1 and complexity limits, errors and pagination, federation risks, and evolution. Use when designing or reviewing GraphQL APIs. --- # GraphQL Schema (Deep Workflow) GraphQL concentrates complexity on the server: **resolver graphs**, **N+1** fetches, **schema evolution**, and **field-level authorization**. ## When to Offer This Workflow **Trigger conditions:** - Designing a new GraphQL API or federated subgraph - Latency or complexity incidents from client queries - Need for safe schema deprecation and versioning **Initial offer:** Use **six stages**: (1) domain modeling, (2) operations surface, (3) performance patterns, (4) errors & partial results, (5) security & authz, (6) versioning & evolution). Confirm client patterns (Apollo, Relay) and gateway (if any). --- ## Stage 1: Domain Modeling **Goal:** Types reflect domain concepts; avoid dumping everything on `Query`; use input objects for mutations with validation. --- ## Stage 2: Operations Surface **Goal:** Queries for reads; mutations for writes; subscriptions only when justified (scaling and operational cost). ### Pagination - Prefer cursor-based connections for large lists (Relay-style edges/nodes) --- ## Stage 3: Performance Patterns **Goal:** DataLoader or batching for N+1; query complexity/depth/cost limits; optional persisted queries for public APIs. --- ## Stage 4: Errors & Partial Results **Goal:** Document semantics of `errors` alongside partial `data`; map domain failures to structured extensions. --- ## Stage 5: Security & Authz **Goal:** Enforce authorization at field/object level—not only at the top resolver. --- ## Stage 6: Versioning & Evolution **Goal:** Prefer additive changes; `@deprecated` with migration window; in federation, clear ownership of types and entities. --- ## Final Review Checklist - [ ] Schema reflects domain and operations - [ ] Pagination and mutations idiomatic - [ ] Batching and complexity limits in place - [ ] Error behavior documented for clients - [ ] Field-level authz enforced - [ ] Deprecation policy defined ## Tips for Effective Guidance - N+1 is the default failure mode—plan batching early. - Pair with **rest-best-practices** when REST and GraphQL coexist at the edge. ## Handling Deviations - Public APIs: consider persisted queries or allowlists to limit abusive queries.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lovefromio/lovefromio-openclaw-persistent-memory (version 1.0.0)

--- name: openclaw-persistent-memory version: 0.1.0 description: Persistent memory system - automatic context capture and semantic search author: Jason Brashear / Titanium Computing repository: https://github.com/webdevtodayjason/openclaw_memory metadata: openclaw: requires: bins: ["openclaw-persistent-memory"] install: - id: node kind: node package: openclaw-persistent-memory bins: ["openclaw-persistent-memory"] label: "Install OpenClaw Persistent Memory (npm)" --- # OpenClaw Persistent Memory Persistent memory system that automatically captures context across sessions using SQLite + FTS5. ## Features - 🧠 **Auto-capture** - Important observations saved automatically after each response - 🔍 **Auto-recall** - Relevant memories injected before each prompt - 💾 **SQLite + FTS5** - Fast full-text search across all memories - 🛠️ **Tools** - `memory_search`, `memory_get`, `memory_store`, `memory_delete` - 📊 **Progressive disclosure** - Token-efficient retrieval ## Setup 1. **Install the npm package:** ```bash npm install -g openclaw-persistent-memory ``` 2. **Start the worker service:** ```bash openclaw-persistent-memory start ``` 3. **Install the OpenClaw extension:** ```bash # Copy extension to OpenClaw extensions directory cp -r node_modules/openclaw-persistent-memory/extension ~/.openclaw/extensions/openclaw-mem cd ~/.openclaw/extensions/openclaw-mem && npm install ``` 4. **Configure OpenClaw** (in `~/.openclaw/openclaw.json`): ```json { "plugins": { "slots": { "memory": "openclaw-mem" }, "allow": ["openclaw-mem"], "entries": { "openclaw-mem": { "enabled": true, "config": { "workerUrl": "http://127.0.0.1:37778", "autoCapture": true, "autoRecall": true } } } } } ``` 5. **Restart OpenClaw gateway** ## Tools Provided | Tool | Description | |------|-------------| | `memory_search` | Search memories with natural language | | `memory_get` | Get a specific memory by ID | | `memory_store` | Save important information | | `memory_delete` | Delete a memory by ID | ## API Endpoints Worker runs on `http://127.0.0.1:37778`: | Endpoint | Method | Description | |----------|--------|-------------| | `/api/health` | GET | Health check | | `/api/stats` | GET | Database statistics | | `/api/search` | POST | Full-text search | | `/api/observations` | GET | List recent observations | | `/api/observations/:id` | GET | Get observation | | `/api/observations/:id` | DELETE | Delete observation | | `/api/observations/:id` | PATCH | Update observation | ## Troubleshooting ### Worker not running ```bash curl http://127.0.0.1:37778/api/health # If fails, restart: openclaw-persistent-memory start ``` ### Auto-recall not working - Check OpenClaw logs: `tail ~/.openclaw/logs/*.log | grep openclaw-mem` - Verify `plugins.slots.memory` is set to `"openclaw-mem"` - Restart gateway after config changes


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: piuaibot-stack/design-patterns (version 1.0.0)

--- name: design-patterns description: Use when architecting solutions, identifying recurring problems, or improving code structure with proven patterns. version: 1.0.0 author: Kintama license: MIT metadata: hermes: tags: [design-patterns, GoF, architecture, OOP, structure] related_skills: [solid-principles, refactoring-techniques, clean-code-principles] --- # Design Patterns ## Creational Patterns - **Singleton** — one instance globally (use sparingly, hard to test) - **Factory Method** — delegate instantiation to subclasses - **Abstract Factory** — families of related objects - **Builder** — step-by-step complex object construction - **Prototype** — clone existing objects ## Structural Patterns - **Adapter** — incompatible interfaces compatibility - **Decorator** — add behavior without modifying class - **Facade** — simplified interface to complex subsystem - **Proxy** — control access to another object - **Composite** — tree structures, treat individual/group uniformly ## Behavioral Patterns - **Observer** — event subscription/notification - **Strategy** — swap algorithms at runtime - **Command** — encapsulate actions as objects - **Chain of Responsibility** — pass request through handler chain - **Template Method** — define skeleton, let subclasses fill in - **State** — change behavior when internal state changes - **Iterator** — traverse collection without exposing internals ## Modern Patterns - **Repository** — abstract data layer from business logic - **Unit of Work** — batch DB operations into a transaction - **CQRS** — separate read and write models - **Saga** — distributed transaction management - **Circuit Breaker** — prevent cascading failures ## Anti-Patterns to Avoid - God Object — class that knows too much - Spaghetti Code — no clear structure - Golden Hammer — using one pattern for everything - Premature Optimization — optimizing before profiling


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anderotxoa/mempalace-ander (version 1.0.0)

--- name: mempalace description: Integración con MemPalace para gestión de memoria semántica persistente. Use when you need to search, add, or query memories stored in a MemPalace palace. Supports semantic search, knowledge graph queries, and memory management via ChromaDB-based storage. --- # MemPalace Skill Este skill proporciona acceso a MemPalace, un sistema de memoria semántica persistente basado en ChromaDB. ## Ubicación del Palacio Por defecto: `~/.mempalace/palace` ## Scripts Disponibles | Script | Descripción | |--------|-------------| | `scripts/search.py` | Búsqueda semántica en el palacio | | `scripts/add_memory.py` | Añadir nueva memoria al palacio | | `scripts/status.py` | Estado del palacio (drawers, wings, rooms) | | `scripts/kg_query.py` | Consultar el knowledge graph | ## Uso ### Búsqueda Semántica ```bash python3 scripts/search.py "query de búsqueda" [--limit 5] [--wing wing_name] [--room room_name] ``` ### Añadir Memoria ```bash python3 scripts/add_memory.py --wing wing_name --room room_name --content "contenido a guardar" [--source-file archivo.txt] ``` ### Estado del Palacio ```bash python3 scripts/status.py ``` ### Consultar Knowledge Graph ```bash python3 scripts/kg_query.py --entity "nombre_entidad" [--direction both|incoming|outgoing] ``` ## Referencias Para documentación completa de las herramientas MCP disponibles, ver `references/mcp_tools.md`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: renjicode/keplerjai-oss-uploader (version 1.0.0)

--- name: keplerjai-oss-uploader description: 将本地文件上传到阿里云 OSS 并输出 bindHost 下的可访问 URL。用户提到 OSS 上传、静态资源、keplerjai OSS 时使用。 metadata: openclaw: emoji: "📤" always: true requires: bins: - python --- # keplerjai OSS 上传（技能摘要） **详细说明、环境变量表、安全与 OpenClaw 配置示例见同目录 `README.md`（人读文档；本文件刻意保持短小以降低技能加载 token）。** ## 环境变量前缀 必填：`KEPLERJAI_OSS_ACCESS_KEY_ID`、`KEPLERJAI_OSS_ACCESS_KEY_SECRET`、`KEPLERJAI_OSS_ENDPOINT`、`KEPLERJAI_OSS_BUCKET`。其余见 `README.md` 或 `config.example.env`；`config.json` 可补全非密钥项（`skill_config` 合并，shell 已设变量优先）。 ## 上传命令（`{baseDir}` 为本技能根目录） ```bash pip install -r "{baseDir}/requirements.txt" python "{baseDir}/scripts/upload_to_oss.py" "/path/to/file.png" ``` 常用：`--flat` 扁平随机名；`-k` 指定对象键；`--dry-run` 仅打印 key/URL；`--sync-lifecycle` 上传后写入生命周期（需天数配置与 RAM 权限）。 ## Agent 要点 1. 密钥只经 env / SecretRef 注入，**不写进对话与 SKILL 正文**。 2. 上传成功后把脚本输出的 `public_url` 给用户。 3. 生命周期运维：`python "{baseDir}/scripts/put_bucket_lifecycle.py"`（详见 `README.md`）。 ## OpenClaw 在 `skills.entries` 中使用键名 **`keplerjai-oss-uploader`**，与 `name` 字段一致；`env` 键名与上表 `KEPLERJAI_OSS_*` 一致。完整 JSON 片段见 `README.md`。 ## 返回形式 以[可读文件名](url)的方式返回文件下载链接


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: myclaw-ai/openclaw-auto-dream-lite (version 1.0.1)

--- name: openclaw-auto-dream-lite description: "Lightweight memory consolidation for OpenClaw agents — daily dream cycles that turn scattered daily logs into structured long-term memory. Zero config, one prompt, just works. Use when: user asks for 'auto memory', 'dream', 'auto-dream', 'memory consolidation'. Powered by MyClaw.ai (https://myclaw.ai)." --- # Auto-Dream Lite — Memory That Just Works Your agent "dreams" once a day — reads recent daily logs, pulls out what matters, and merges it into MEMORY.md. One file in, one file out. > **[MyClaw.ai](https://myclaw.ai)** — the best way to run your OpenClaw. A dedicated server running 24/7 with full code control, cron jobs, persistent memory, and one-click skill install. ## How It Works ``` Daily logs (memory/YYYY-MM-DD.md) ↓ dream cycle MEMORY.md (structured knowledge) ↓ notification 3-line summary → user ``` One prompt does everything: first run or daily run. ## Setup ### 1. Ensure MEMORY.md Exists If missing, create from `references/memory-template.md`. ### 2. Create Cron Job ``` name: "auto-memory-dream" schedule: { kind: "cron", expr: "0 4 * * *", tz: "<user timezone>" } payload: { kind: "agentTurn", message: "Read skills/skills/openclaw-auto-dream-lite/references/dream-prompt.md and follow every step.", timeoutSeconds: 600 } sessionTarget: "isolated" delivery: { mode: "announce" } ``` ### 3. Run First Dream After setup, run the dream prompt right away so the user sees their first consolidation report immediately. ## Manual Triggers | Command | Action | |---------|--------| | "Dream now" / "Consolidate memory" | Run dream cycle | | "Dream details" | Show full change list from last dream | ## Safety 1. **Auto-backup** — copies MEMORY.md to .pre-dream before every run; rollback if something goes wrong 2. **Never delete daily logs** — only marks them `<!-- consolidated -->` 3. **Never touch ⚠️ PERMANENT items** 4. **Scope** — only reads/writes MEMORY.md and files in the memory/ directory ## Reference Files - `references/dream-prompt.md` — The one and only dream execution prompt - `references/memory-template.md` — MEMORY.md starter template


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nop3z/mobile-master (version 1.0.0)

--- name: mobile-master description: 移动安全SKILL 协助逆向 --- ### spawn模式 attach模式 dexdump frida检测绕过 脱壳 jadx-gui apktool 重签名 提取Androidmanifest.xml 加固检测 安装限制绕过 ## Suport commands | Script | Description | |---------|-------------| | [Start-Frida-Server](./scripts/Start-frida-server.sh)| 启动设备上的frida-server | | [Extract-Installation-Package](./scripts/Extract-Installation-Package.sh) | 从设备提取安装包APK | | [Spawn-APP](./scripts/Spawn-app.sh) | spawn模式启动应用进行hook | | [Attach-APP](./scripts/attach-app.sh) | attach模式附加到运行中的应用 | | [Dexdump](./scripts/Dexdump.sh) | 内存dump dex文件脱壳 | | [JADX-GUI]()|使用jadx-gui载入dexdump内容| | [Extract-AndroidManifest.xml](./scripts/Extract-AndroidManifest.xml.sh)| 提取安装包中的AndroidManifest.xml并进行权限审查 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kostja94/ctv-ads (version 1.0.1)

--- name: ctv-ads description: When the user wants to run CTV, OTT, or streaming TV ads. Also use when the user mentions "CTV ads," "connected TV," "OTT advertising," "streaming ads," "TV ads," "Hulu ads," "Roku ads," "YouTube TV ads," or "programmatic TV." For paid mix, use paid-ads-strategy. metadata: version: 1.0.1 --- # Paid Ads: CTV / Streaming TV Guides Connected TV (CTV) and OTT advertising: ads on streaming platforms (Hulu, Roku, YouTube TV, etc.). Use when targeting viewers who watch streaming content; CTV achieves ~95% ad completion, higher than mobile/desktop. **When invoking**: On **first use**, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On **subsequent use** or when the user asks to skip, go directly to the main output. ## What Is CTV / OTT - **CTV (Connected TV)**: Smart TVs, streaming devices (Roku, Fire TV, Apple TV) that serve ads - **OTT (Over-the-top)**: Video delivered over the internet (Hulu, Peacock, Paramount+, etc.) - **Streaming share**: Streaming surpassed broadcast + cable combined in 2025 (~45% of TV viewing) ## Why CTV | Advantage | Detail | |-----------|--------| | **High completion** | ~95% ad completion rate vs lower on mobile/desktop | | **Unskippable** | Most CTV inventory is non-skippable pre-roll/mid-roll | | **Targeting** | Demographics, interests, location, household data | | **Underinvested** | CTV received ~7% of ad spend despite ~18% of media time (2024) | ## Ad Formats | Format | Use | |--------|-----| | **Pre-roll / Mid-roll** | Standard video; 15–30 sec | | **Interactive** | Drives engagement; ~71 sec additional time vs standard | | **QR / URL** | Direct response; track offline-to-online | ## Platforms & Buying - **Programmatic**: DSPs (The Trade Desk, etc.); audience-based buying - **Direct**: Hulu, Roku, YouTube TV, Peacock—platform-specific deals - **Google**: YouTube on TV; part of Video campaigns ## Metrics | Metric | Use | |--------|-----| | **Completion rate** | % who watch full ad; CTV typically 95%+ | | **Reach / frequency** | Household reach; avoid over-frequency (e.g., &lt;7) | | **CPM** | Cost per thousand impressions | | **Attribution** | QR scans, URL visits; harder than web/app | ## Creative - **Horizontal**: 16:9; TV screen format - **Sound on**: Assume sound; design for audio - **Brand focus**: CTV often awareness; clear brand, CTA ## UTM for CTV Use `utm_medium=video` or `ctv` with `utm_source` (e.g., `hulu`, `roku`) for QR/URL campaigns. Tag links in interactive CTV ads for attribution. ## Pre-Launch Checklist - [ ] Creative in 16:9; sound-on - [ ] Targeting defined (demo, interests, geo) - [ ] Attribution plan (QR, URL, brand lift) - [ ] Frequency cap set - [ ] Budget aligned with CPM expectations ## Related Skills - **paid-ads-strategy**: Ad formats by medium; when to use TV vs web vs app - **traffic-analysis**: UTM for CTV direct-response; attribution - **analytics-tracking**: Conversion tracking for QR/URL campaigns


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gillescv/coach-chasseurs (version 1.0.0)

--- name: coach-chasseurs description: Coach professionnel expert en accompagnement de dirigeants pour la préparation d'entretiens avec des cabinets de Management de Transition (MdT) ou des chasseurs de têtes généralistes. Utilise ce skill pour analyser des profils de cabinets, des fiches de poste ou préparer des pitchs stratégiques pour Gilles Harpoutian. --- # Coach Chasseurs Ce skill transforme l'agent en coach expert pour Gilles Harpoutian. Il détecte automatiquement s'il doit agir en **MODE MdT** ou en **MODE CHASSEUR**. ## Instructions principales Le fonctionnement détaillé (stratégies, pitchs, questions stratégiques et formats de sortie) est défini dans le fichier de référence. **Règle d'or :** Toujours lire `references/COACHING.md` avant de répondre pour appliquer la structure obligatoire correspondante. ## Workflow 1. **Analyse de l'entrée** : Identifier s'il s'agit d'un cabinet MdT ou d'un chasseur généraliste. 2. **Consultation du guide** : Lire `references/COACHING.md`. 3. **Génération de la réponse** : Suivre scrupuleusement la "Structure de Réponse Obligatoire" définie pour le mode détecté. ## Profil Gilles Harpoutian Les détails du profil (Directeur de la Transformation Commerciale, Expertise IA/Affaires Publiques) sont inclus dans le guide de coaching.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1oid/txt-to-epub (version 1.0.0)

--- name: txt-to-epub description: 将txt文本转换为epub文件，使用纯规则进行章节识别与分割。适用于小说、教程和一般长文，不内置AI接口。 --- # TXT to EPUB ## Role 你是一个专门将 TXT 文本转换为 EPUB 的助手。你只做规则分章，不调用任何外部模型 API。 ## What This Skill Does 1. 自动检测 TXT 编码并读取内容 2. 基于规则识别章节标题并分章 3. 生成带目录导航的 EPUB 文件 4. 支持保留完整标题（默认）或清理编号前缀 ## Split Strategy - 支持 `auto | novel | tutorial | length` 四种模式 - 标题规则覆盖： - 中文小说：`第一章 ...` / `第十回 ...` - 英文结构：`Chapter 1 ...` / `Part 2 ...` - 教程编号：`1.2 ...` / `2.3.4 ...` - 中文序号：`一、...` - 当规则无法识别章节时，自动按长度切分为 `Part 1/2/...` ## Title Handling - 默认 `--title-style full`：保留完整标题 - 例如输入是 `第一章 xxx`，目录和章节标题都保持 `第一章 xxx` - 可选 `--title-style clean`：去掉编号前缀，仅保留正文标题 ## Script Path `/Users/loid/.claude/skills/txt-to-epub/scripts/txt_to_epub.py` ## Install Dependencies ```bash python3 -m pip install -r /Users/loid/.claude/skills/txt-to-epub/requirements.txt ``` ## Usage 最小示例： ```bash python3 /Users/loid/.claude/skills/txt-to-epub/scripts/txt_to_epub.py \ --input /path/to/book.txt ``` 常用示例（小说）： ```bash python3 /Users/loid/.claude/skills/txt-to-epub/scripts/txt_to_epub.py \ --input /path/to/novel.txt \ --output /path/to/novel.epub \ --title "我的小说" \ --author "作者名" \ --language zh-CN \ --split-mode novel \ --title-style full \ --verbose ``` 常用示例（教程）： ```bash python3 /Users/loid/.claude/skills/txt-to-epub/scripts/txt_to_epub.py \ --input /path/to/tutorial.txt \ --split-mode tutorial \ --title-style full ``` ## Parameters - `--input` 输入 TXT 文件路径（必填） - `--output` 输出 EPUB 路径（可选，默认同名 `.epub`） - `--title` 书名（可选，默认取输入文件名） - `--author` 作者（可选） - `--language` 语言，默认 `zh-CN` - `--split-mode` 分章模式：`auto|novel|tutorial|length` - `--title-style` 标题样式：`full|clean`，默认 `full` - `--min-chapter-chars` 过短章节合并阈值，默认 `300` - `--chunk-chars` 长度切分块大小，默认 `8000` - `--verbose` 输出额外信息 ## Interaction Rules 当用户请求转换时： 1. 收集必要参数（至少 `--input`） 2. 默认使用 `--title-style full` 3. 根据文本类型建议 `--split-mode`： - 小说优先 `novel` - 教程优先 `tutorial` - 不确定用 `auto` 4. 执行脚本并返回： - 输出文件路径 - 章节数 - 前几章标题预览 ## Limitations - 本技能不直接调用 AI 接口 - 复杂或非结构化文本可能需要手动指定 `--split-mode` 或调整 `--chunk-chars`

path: requirements.txt
content: ebooklib>=0.18
chardet>=5.0


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