OpenClaw/clawhub-security-signals huggingface dataset:
Skill: allinherog-star/douyin-kol-search (version 1.0.1)

--- name: douyin-kol-search description: "抖音最具商业价值意见领袖（KOL）适合内容创作者、运营、营销、品牌方在用户想找值得合作的抖音达人时使用，帮助基于输入材料生成相关创作者 / KOL、对标账号候选、合作和竞品研究线索。" requiredEnvVars: - name: AISKILLS_API_KEY description: "从 AI Skills 官网 https://ai-skills.ai 获取的 API Key，用于运行导出的技能调用。" --- # douyin-kol-search 抖音最具商业价值意见领袖（KOL） [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 抖音最具商业价值意见领袖（KOL）用于回答「如何找对标账号」、找合作达人、找带货达人、找抖音对标账号，适合内容创作者、运营、营销、品牌方在明确业务目标、内容材料或分析对象后调用。 它会结合搜索关键词、输入关键词搜索 KOL（如：美妆、美食、穿搭...）等输入，整理关键上下文，并输出相关创作者 / KOL、对标账号候选、合作和竞品研究线索，便于继续执行、复盘或交付。 ### 什么时候使用 **适用场景** - 用户想找值得合作的抖音达人 - 用户想找更会带货或商业价值更高的账号 - 用户想找抖音对标账号 - 用户想找同赛道、竞品或相似创作者 ### 调用方式 通过导出的 Python runner 直接调用 AI Skills API： ### 命令示例 **按赛道和粉丝范围筛选** ```bash python3 scripts/run.py --params '{"keyword":"深圳烧烤酒吧","contentTag":"tag-48","followerRange":"10-100"}' ``` **只按关键词搜达人** ```bash python3 scripts/run.py --params '{"keyword":"深圳烧烤酒吧"}' ``` **只按分类搜达人** ```bash python3 scripts/run.py --params '{"contentTag":"tag-48"}' ``` ### 参数说明 | 参数 | 类型 | 必填 | 默认 | 说明 | | --- | --- | --- | --- | --- | | `page` | number | 否 | `1` | 页码，从1开始 | | `keyword` | string | 是 | - | 输入关键词搜索 KOL（如：美妆、美食、穿搭...） | | `contentTag` | string | 否 | - | 选择内容分类标签进行筛选；可选值：不限（``）、美妆（`tag-1`）、时尚（`tag-6`）、萌宠（`tag-11`）、测评（`tag-15`）、游戏（`tag-23`）、二次元（`tag-25`）、旅行（`tag-27`）、汽车（`tag-31`）、生活（`tag-36`）、音乐（`tag-41`）、美食（`tag-48`）、母婴亲子（`tag-55`）、运动健身（`tag-60`）、科技数码（`tag-64`）、教育培训（`tag-68`）、颜值达人（`tag-72`）、才艺技能（`tag-79`）、影视娱乐（`tag-85`）、艺术文化（`tag-87`）、财经投资（`tag-91`）、剧情搞笑（`tag-97`）、房产（`tag-139`）、生活家居（`tag-1001`） | 完整机器可读参数结构见 `references/form-schema.json`。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 当前技能直接接收 JSON 参数，不涉及分享链接解析。 ### 示例请求 下面的示例参数可直接传给 `scripts/run.py`，runner 会把它们发送给 AI Skills API。 ```bash python3 scripts/run.py --params '{"keyword":"深圳烧烤酒吧","contentTag":"tag-48","followerRange":"10-100"}' ``` 等价的 `--params` JSON： ```json { "keyword": "深圳烧烤酒吧", "contentTag": "tag-48", "followerRange": "10-100" } ``` ### 返回结果示例 ```json { "success": true, "data": { "keyword": "深圳烧烤酒吧", "platformSource": "_1", "pagination": { "page": 1, "pageSize": 20, "total": 1 }, "items": [ { "uid": "kol-1", "nickname": "深圳烧烤王", "region": "广东 深圳", "followerCount": 560000, "interactionRate30d": 0.082, "convertIndex": 82, "spreadIndex": 76, "starIndex": 91, "expectedPlayCount": 320000, "price20_60": 180000, "contentTags": [ "美食" ] } ], "searchStrategy": { "usedFallback": true, "droppedFilters": [ "contentTag" ], "localContentTagFilterApplied": true } }, "meta": { "executionTime": 842, "cached": false } } ``` ### 交付内容 - 达人候选列表：包含账号定位、粉丝规模和内容标签等合作筛选信息。 - 商业价值线索：重点辅助判断传播能力、转化潜力和综合合作优先级。 - 对标账号参考：用于找同赛道创作者、竞品账号和可学习的内容样本。 ### 结果使用建议 - 先按赛道和关键词圈定候选，再用商业价值、传播能力和转化潜力做优先级排序。 - 找合作达人时重点看账号定位是否匹配品牌；找对标账号时重点看内容标签和受众相似度。 - 如果筛选条件过窄导致结果少，可以放宽粉丝量或关键词后再比较。 ### 运行前准备 - `AISKILLS_BASE_URL`：默认 `https://ai-skills.ai` - `AISKILLS_API_KEY`：必填，用于认证调用 - `AISKILLS_TENANT_ID`：默认 `default`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: cinience/aliyun-green-moderation (version 1.0.0)

--- name: aliyun-green-moderation description: Use when managing Alibaba Cloud Content Moderation (Green) via OpenAPI/SDK, including the user needs content moderation resource and policy operations, including list/create/update actions, status inspection, and troubleshooting moderation workflow failures. version: 1.0.0 --- Category: service # Content Moderation (Green) Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Content Moderation. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `Green` - Default API version: `2022-09-26` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-green-moderation/` ## Validation ```bash mkdir -p output/aliyun-green-moderation for f in skills/security/content/aliyun-green-moderation/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-green-moderation/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-green-moderation/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-green-moderation/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kgnvsk/agent-memory-store (version 1.2.0)

--- name: agent-memory-store version: 1.2.0 description: Shared semantic memory store for AI agents. Store, search, and retrieve memories across agents with TTL decay. SQLite persistence — survives restarts. author: bro-agent homepage: https://kgnvsk.github.io/paylock --- # Agent Memory Store Cross-agent semantic memory with TTL decay. SQLite-backed — data survives restarts. ## Start ```bash python3 scripts/memory_store.py # Runs on port 8768, DB: /root/.openclaw/workspace/data/agent_memory.db ``` ## Quick Start ```bash # Store a memory curl -X POST http://localhost:8768/memories \ -H "Content-Type: application/json" \ -d '{"owner":"my-agent","content":"user prefers SOL payments","ttl_seconds":86400,"public":false}' # Search memories curl "http://localhost:8768/memories?q=payment+preferences&agent=my-agent" # List all (with agent filter) curl "http://localhost:8768/memories?agent=my-agent&limit=20" ``` ## Endpoints | Method | Path | Description | |--------|------|-------------| | GET | /health | status + memory count | | POST | /memories | store memory `{owner, content, tags?, ttl_seconds?, public?}` | | GET | /memories?q=query&agent=X&limit=10 | semantic search (Jaccard) | | GET | /memories/:id | get by ID | | POST | /memories/:id/delete | delete | ## Changelog ### v1.1.0 - SQLite persistence (data survives restarts) - Thread-safe writes - DB stored at `/root/.openclaw/workspace/data/agent_memory.db` ### v1.0.0 - Initial release: in-memory store, TTL, cross-agent sharing, Jaccard search ## Install ```bash clawhub install agent-memory-store ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: openwaygate/youtube-member (version 0.10.7-dev)

--- name: youtube-member description: "Manage YouTube channel members. Use this skill to list information about channel members. Useful when working with YouTube member — provides commands to list member via the yutu CLI. Includes setup and installation instructions for first-time users. Triggers: list channel members, list member, list my member" metadata: openclaw: requires: env: - YUTU_CREDENTIAL - YUTU_CACHE_TOKEN bins: - yutu config: - client_secret.json - youtube.token.json primaryEnv: YUTU_CREDENTIAL emoji: "\U0001F3AC\U0001F430" homepage: https://github.com/eat-pray-ai/yutu install: - kind: node package: "@eat-pray-ai/yutu" bins: [yutu] --- # YouTube Member Manage YouTube channel members. Use this skill to list information about channel members. ## Before You Begin yutu requires Google Cloud Platform OAuth credentials and a cached token to access the YouTube API. If you haven't set up yutu yet, read the [setup guide](references/setup.md) first. ## Operations Read the linked reference for full flag details and examples. | Operation | Description | Reference | |-----------|-------------|----------| | list | List channel members | [details](references/member-list.md) | ## Quick Start ```bash # Show all member commands yutu member --help # List member yutu member list ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aikawabigsky309/pozzzi-charity-application (version 0.1.0)

--- name: application-assistant description: 噗滋（pozzzi）慈善 - 帮助中小型 NGO 自动生成项目申报书草稿，支持腾讯公益99公益日、通用基金会申请和政府购买服务三种格式，内置占位符强制机制防止 AI 编造预算和编号数据。 --- # 申报助手 > ℹ️ **公益开源工具（v0.1.0）** > > 本 Skill 为噗滋（pozzzi）慈善开源工具集成员，由社区贡献者维护，遵循 MIT License。 > > **使用约束**（用户自负责任）： > - 🚫 禁止处理 14 岁以下未成年人个体级数据（《未成年人保护法》第 72-74 条） > - 🚫 禁止用于编造法规条文、文件编号、资质证书编号等 > - 🚫 不适用于公开募捐相关内容生成（建议使用专业工具） > - ⚠️ AI 生成内容**必须经人工审核后方可使用**，开发者不对使用结果承担责任 > - ⚠️ 本工具不构成专业的法律/财务/管理建议 > - ✅ 用户自带模型 API（混元/DeepSeek/豆包，均已各自备案） > - ✅ 噗滋（pozzzi）作为工具提供者，不接触/不存储用户数据 > > 详细法律声明：[LICENSE](https://github.com/kirinspark/pozzzi-charity/blob/main/LICENSE) | [免责声明](https://github.com/kirinspark/pozzzi-charity/blob/main/docs/legal/disclaimer.md) 申报助手是噗滋慈善（pozzzi-charity） Skill 集群中第二核心 Skill，帮助 NGO 撰写项目申报书争取资金支持。 ## 支持的申报类型 - **通用申报书** — 最大公约数版，适用于大部分基金会 - **腾讯公益99公益日** — 国内最大募款窗口，格式标准化 - **政府购买服务** — 民政/妇联/教育等政府资金申报 ## 核心特性 - **占位符强制机制**（placeholder-enforcer）：AI 不得编造预算金额、证书编号、统计数据 - **四轮引导式数据收集**：基本信息 → 项目设计 → 预算团队 → 确认补充 - **多模型适配**：混元（默认免费）/ DeepSeek（逻辑严谨）/ GLM（叙事优质）/ 豆包（温暖语气） - **单章节重生成**：不满意某章节可单独重新生成 ## 合规要求 - 所有输出经 PII 过滤和免责声明注入（强制不可跳过） - 联系人信息不进入模型 API - 预算金额必须由用户填写，AI 不生成 - 需求数据须标注来源，AI 不编造统计数字 - 日志保留 ≥6 个月，不含 prompt 正文 ## 技术依赖 - `model-gateway` — 多模型路由、域名白名单、内置 PII 前置过滤 - `disclaimer-injector` — AI 声明和免责提示注入 - `storage-adapter` — 本地数据存储（日志、历史）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: biogod2020/drission-sota-toolkit (version 7.1.0)

--- name: drission-sota-toolkit description: "Professional Web Intelligence & Automation Toolkit. Features Protocol Phantom (TLS/JA4), Local Socket Relaying, and Hardened physical gating." metadata: openclaw: emoji: 🛠️ disable-model-invocation: true requires: bins: ["google-chrome-stable", "xvfb-run", "dbus-launch"] python: ["curl_cffi", "lxml", "websocket-client", "DrissionPage", "requests"] --- # Drission SOTA Toolkit (v7.1.0) ## Security Architecture This toolkit implements a physical lockfile system to prevent unauthorized autonomous execution. 1. **Mandatory Gating**: High-risk scripts require a fresh lockfile in `~/.openclaw/tmp/`. 2. **Interactive Auth**: Access is only granted via `secure_wrapper.py` after a human challenge. 3. **No-Bypass**: Environment variables are not used for authentication. ## Asset Inventory - `main_engine.py`: Search utility. - `secure_wrapper.py`: Security entry point. - `python_relay.py`: Gated TCP relay. - `force_takeover.py`: Gated CDP control. --- Version: 7.1.0 | Author: Biogod2020 | Status: Production Stable


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: qiushang555/cognitive-topology (version 1.0.0)

--- name: cognitive-topology description: Agent认知拓扑引擎。自动将复杂任务分裂为多个独立分支，并行执行后汇总整合。触发场景：(1)复杂问题需要多角度分析 (2)多独立子任务需要并行执行 (3)需要清晰追踪思维分支 (4)用户说"分叉"、"分支分析"、"多线程"时。使用 sessions_spawn 创建分支，分支结果写入 L2 文件，Main Session 读取并整合。 --- # Cognitive Topology 把复杂任务的「一条直线」变成「多分支并行」。 ## 何时触发 当任务符合以下任一条件时，自动启用本技能： - 需要同时分析 N 个不同方向/角度 - 任务可拆分为相互独立的子任务 - 问题复杂，需要递归分解 - 用户明确说「分叉」「分支」「多角度」 ## 核心工作流 ### Step 1: Fork - 创建分支 使用 `sessions_spawn` 创建独立分支： ``` sessions_spawn( mode="session", runtime="subagent", task="<具体分支任务>", label="ct-<序号>-<简短描述>" ) ``` 每个分支有独立 Session ID，互不干扰。 ### Step 2: Branch - 分支执行 - 分支 Agent 独立执行自己的任务 - 完成后将结论写入标准 L2 文件：`~/.openclaw/workspace/cognitive-topology/branches/<branch_id>_L2.md` - L2 格式：## 结论\n\n<具体结论>\n\n## 依据\n\n<分析依据> ### Step 3: Integrate - 整合 Main Session 等待所有分支完成，读取所有 L2 文件，生成 synthesis： ```bash cat ~/.openclaw/workspace/cognitive-topology/branches/*_L2.md ``` ## 目录结构 ``` ~/.openclaw/workspace/cognitive-topology/ ├── branches/ # 分支工作目录 │ ├── ct-1_anal_ai_L2.md # 分支1的L2 │ ├── ct-2_anal_chip_L2.md # 分支2的L2 │ └── ... ├── topology_latest.json # 当前拓扑状态 └── synthesis.md # 整合结果 ``` ## 关键约束 - 分支只写 L2，不读自己的 L2 - Main Session 读所有 L2，不直接读分支的 Session 历史 - 分支间不直接通信 - 跨分支信息通过 Main Session 的 synthesis 传播 ## 简化实现说明 无 UI，星空图用文本树替代： ```markdown 🌐 认知拓扑 ├── branch-1 [分析AI板块] │ └── ✅ 已完成 ├── branch-2 [分析半导体] │ └── ⏳ 进行中 └── branch-3 [分析新能源] └── ⏳ 待启动 ``` ## 典型场景示例 **场景：分析 A 股三个方向的机会** Main Session 收到任务后： 1. 判断需要分裂为3个独立分支 2. 同时 spawn 3 个分支 Agent 3. 分支1分析AI板块 → 写 L2 4. 分支2分析半导体 → 写 L2 5. 分支3分析新能源 → 写 L2 6. Main 汇总 L2 → 生成 synthesis → 回复用户


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kaising-openclaw1/xiao-habit-tracker (version 1.0.0)

--- name: habit-tracker description: 习惯追踪器 - 习惯打卡、统计分析、成就系统 --- # Habit Tracker 习惯追踪工具，帮助建立和保持好习惯。 ## 功能 - ✅ 习惯打卡 - ✅ 统计分析 - ✅ 成就系统 - ✅ 提醒通知 - ✅ 数据导出 ## 使用 ```bash # 添加习惯 clawhub habit add --name "早起" --frequency daily # 打卡 clawhub habit checkin --name "早起" # 查看统计 clawhub habit stats --name "早起" --days 30 # 查看成就 clawhub habit achievements ``` ## 定价 | 版本 | 价格 | 功能 | |------|------|------| | 免费版 | ¥0 | 3 个习惯 | | Pro 版 | ¥29 | 无限习惯 | | 订阅版 | ¥6/月 | Pro+ 云同步 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: makforce/zencreator-video-skill (version 1.0.0)

# zencreator-video Skill\n\n## Overview\n\nZenCreator.pro is a professional, uncensored NSFW AI content generator for photos and videos. Full creative freedom, no content filters. Supports high-quality image-to-video and video-to-video generation using models like Kling, Wan, Seedance Pro.\n\n- **Website**: https://zencreator.pro\n- **App**: https://app.zencreator.pro (requires account & credits)\n- **Video Features**: Image-to-Video (start frame, start/end frame), Video-to-Video (animate/replace), Lipsync\n- **Qualities**: Up to 1080p, 5-10s clips\n- **Credits**: e.g., Kling 720p 5s = 7 credits, Seedance Pro 1080p 10s = 15-30 credits\n\nGet 30 free credits on signup. Buy more via card/crypto.\n\n## Requirements\n- ZenCreator account logged in\n- Credits balance\n- Use `profile=\"chrome\"` in browser tool (attach your logged-in Chrome tab)\n\n## Usage\n\n1. Open https://app.zencreator.pro/ai-video-generator in Chrome\n2. Click OpenClaw Browser Relay toolbar button to attach tab\n3. Use `browser` tool with `profile=\"chrome\"`:\n - `snapshot` to inspect UI\n - `act` to click/type/upload\n\n### Text → Video\n1. Generate image (use text-to-image tool or manually)\n2. Image → Video\n\n### Image → Video\n- Upload image\n- Select model (e.g., \"Seedance Pro Fast Start Frame 1080p\")\n- Prompt: motion description (\"woman dancing sensually\", NSFW ok)\n- Generate → Download\n\n## Example Prompts (NSFW OK)\n\nFrom ZenCreator AI University:\n\n- \"A beautiful woman slowly undressing, seductive gaze, cinematic lighting\"\n- \"Couple in intimate embrace, passionate kiss, soft bedroom lighting\"\n- \"Model posing in lingerie, slow rotation, professional studio\"\n\nMotion: \"gentle head turn and smile\", \"sensual body wave\", \"walking towards camera\"\n\n## Tool Calls Example\n```\n# Snapshot UI\nbrowser action=snapshot profile=\"chrome\" targetUrl=\"https://app.zencreator.pro/ai-video-generator\"\n\n# Upload image\nbrowser action=act request={kind: \"upload\", path: \"/path/to/image.png\"} profile=\"chrome\"\n\n# Enter prompt\nbrowser action=act request={kind: \"type\", ref: \"prompt-input\", text: \"sensual dance\"} profile=\"chrome\"\n\n# Select model, generate\n# ...\n```\n\n## Credit Costs\n| Model | 5s 720p | 10s 720p |\n|-------|---------|----------|\n| Kling 2.1 Start | 7 | 13 |\n| Wan 2.5 Audio 720p | 10 | 20 |\n| Seedance Pro 1080p Start/End | 15 | 30 |\n\n## Install\nPlace `zencreator-video.skill/` in workspace. Restart agent or run `openclaw skill load zencreator-video.skill`\n\n## Notes\n- Uncensored: NSFW content allowed\n- Privacy: End-to-end encrypted\n- High quality: 4K upscale available separately


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tooled-app/low-token (version 2.0.0)

--- name: low-token aliases: ["token-saver", "efficient", "concise", "results-only"] description: Extreme optimization for minimal token usage. Eliminates all narration, fluff, and process descriptors. Deliver results as raw data or compact summaries. tags: [efficiency, tokens, communication, style, brutalism] --- # Low Token Usage Skill (v2.0) **Use this skill when:** - Token usage is a critical constraint. - Maximum information density is required. - Process details are irrelevant; only outcomes matter. ## Core Directive: RESULTS ONLY The goal is to minimize the distance between a request and its outcome. Any token not contributing to the final answer is waste. ## Rules ### 1. Zero Process Narration - ❌ "I am now searching for..." $\rightarrow$ ✅ [Search results/result] - ❌ "Let me check that for you..." $\rightarrow$ ✅ [The answer] - ❌ "I'll start by reading the file..." $\rightarrow$ ✅ [The content/edit] ### 2. No Meta-Commentary - ❌ "I've updated the file as requested." $\rightarrow$ ✅ "Updated." - ❌ "I'll do this in three steps: 1... 2... 3..." $\rightarrow$ ✅ [Execution of all steps] - ❌ "I hope this helps!" $\rightarrow$ ✅ [Silence] ### 3. Absolute Density - Use bullet points over paragraphs. - Use abbreviations where clear (e.g., "Conf" for Configuration, "Req" for Request). - Avoid full sentences if a phrase suffices. - Use symbols for status: ✅ (Success), ❌ (Failure), ⚠️ (Warning). ### 4. No Filler or Hedging - ❌ "It seems that...", "I believe...", "Possibly..." $\rightarrow$ ✅ Direct factual statements. - ❌ "Great point!", "Interesting...", "I'd be happy to..." $\rightarrow$ ✅ [Remove entirely] ### 5. Batching & Compression - Prefer a single large `edit` or `write` over multiple small ones. - Group related updates into one response. ## Implementation Tiers | Tier | Style | Example | |------|-------|---------| | **Standard** | Concise sentences | "Fixed. Changed `foo` to `bar` on line 45." | | **Dense** | Fragmented | "Fixed: `foo` $\rightarrow$ `bar` (L45)." | | **Extreme** | Symbolic/Raw | "✅ L45: `foo` $\rightarrow$ `bar`" | ## Activation This skill is active when: - User mentions "low token" or "save tokens". - User requests "results only". - Token usage exceeds 75% of context window. - System is in "High Density" mode. ## Metrics Target: <30 tokens for routine confirmations. Target: <100 tokens for complex result summaries. Absolute Cap: Avoid >300 tokens for any routine execution report. [REDACTED_SECRET] Demystify.website tooled.pro


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tangweigang-jpg/langchain-v1-toolkit (version 0.1.0)

--- name: langchain-v1-toolkit description: |- LangChain v1：把 LLM、prompt、tool、retriever、parser 暴露为 Runnable，用 `|` 操作符（LCEL）组合成统一 invoke / stream / batch 接口的链。 LangChain v1: exposes LLMs, prompts, tools, retrievers, and parsers as Runnables composed via the `|` operator (LCEL) into chains with uniform invoke / stream / batch semantics. create_agent returns a LangGraph CompiledStateGraph. license: MIT-0 compatibility: AI engineering knowledge skill — host AI consumes seed.yaml directly. No installation required. metadata: version: "v0.1.0" blueprint_id: "finance-bp-132" blueprint_source: "langchain-ai/langchain" blueprint_commit: [REDACTED_SECRET] category: ai-engineering doramagic_url: "https://doramagic.ai/zh/crystal/langchain-v1-toolkit" openclaw: skillKey: langchain-v1-toolkit category: ai-engineering primaryEnv: knowledge --- # 这个 skill 适合什么用户？能做哪些任务？ ## 概览 LangChain 是构建 LLM 应用的事实标准 Python 框架（github.com/langchain-ai/langchain）。v1 包（v1.2.15）有意保持精简：核心是 `agents.create_agent`（返回 LangGraph CompiledStateGraph）、`chat_models.init_chat_model` 工厂、message types 重导出和 tools/embeddings shim。 历史 `Chain` / `LLMChain` / `Memory` / `AgentExecutor` 接口已迁到 `langchain-clas... **Doramagic 晶体页**: https://doramagic.ai/zh/crystal/langchain-v1-toolkit ## 知识规模 - **51 条约束** (1 fatal + 50 non-fatal) - 上游源码: `langchain-ai/langchain` @ commit `87ba30f0` - 蓝图 ID: `finance-bp-132` ## 用法 Host AI（Claude Code / Cursor / OpenClaw）读 `references/seed.yaml`，按其中的： - `intent_router` 匹配用户意图 - `architecture` 理解项目架构 - `constraints` 应用 anti-pattern 约束 - `business_decisions` 参考核心设计决策 ## FAQ 摘要 ### 这个 skill 适合什么用户？能做哪些任务？ 适合用 LangChain 构建 LLM 应用的工程师：tool-calling agent、结构化输出、RAG pipeline、流式输出、模型 fallback、PII 脱敏等。v1 后 agent 走 LangGraph 路径，旧 AgentExecutor 仍可用但建议迁移。访问 doramagic.ai/r/langchain 查看完整用例。 ### 需要准备什么环境？依赖什么？ Python（具体版本见 langchain_v1/pyproject.toml），`pip install langchain` 自动带 LangGraph 作为硬运行时依赖。每个 provider 需单独安装 partner 包（如 langchain-openai、langchain-anthropic）。 ### 会踩哪些坑？这个 skill 怎么防护？ 本 skill 内置 51 条约束。典型踩坑：(1) BaseMemory 与所有 Conversation*Memory 已 @deprecated，BaseMemory 已从 langchain_core 删除； --- 完整文档: 见 `references/seed.yaml` (v6.1 schema). 浏览页: https://doramagic.ai/zh/crystal/langchain-v1-toolkit


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: liberalchang/html2pptx (version 3.0.0)

--- name: html2pptx version: 3.0.0 description: 将HTML演示文稿转换为PowerPoint(PPTX)格式。支持frontend-slides生成的HTML，保留结构、样式和内容。触发词：html转pptx、html2pptx、转换ppt、生成pptx author: Claude license: MIT tags: [presentation, conversion, html, powerpoint, pptx] category: productivity requirements: - python: ">=3.9" --- # HTML to PPTX 转换器 将HTML演示文稿（特别是frontend-slides生成的）转换为原生PowerPoint(.pptx)文件。 ## 使用场景 - 将frontend-slides生成的HTML演示文稿转换为可编辑的PPT - 保留幻灯片结构、文字内容和基本样式 - 批量转换多个HTML文件 ## 使用方法 ### 基本用法 ```bash python src/convert.py <input.html> [output.pptx] ``` ### 示例 ```bash # 转换单个文件 python src/convert.py presentation.html # 指定输出路径 python src/convert.py presentation.html ~/Documents/output.pptx ``` ## 支持的内容 | HTML元素 | PPTX转换 | |---------|---------| | `<section class="slide">` | 幻灯片页 | | `<h1>`, `<h2>` | 标题 | | `<p>` | 段落文本 | | `<ul>`, `<ol>`, `<li>` | 列表 | | `<div class="card">` | 文本框分组 | | `<img>` | 图片（保留路径） | | CSS颜色 | 尝试匹配主题色 | ## 转换流程 1. **解析HTML** - 使用BeautifulSoup提取slide结构 2. **分析样式** - 提取CSS颜色、字体信息 3. **创建PPTX** - 使用python-pptx生成幻灯片 4. **映射内容** - 将HTML元素映射到PPTX形状 5. **保存文件** - 输出.pptx文件 ## 限制说明 - 复杂CSS动画无法转换 - 渐变背景转为纯色 - 自定义字体可能丢失 - 绝对定位元素需要手动调整 ## 依赖安装 ```bash pip install python-pptx beautifulsoup4 lxml ``` ## 文件结构 ``` html2pptx/ ├── SKILL.md # 本文件 ├── src/ │ └── convert.py # 主转换脚本 └── examples/ # 示例文件 ```

path: requirements.txt
content: beautifulsoup4>=4.10.0
lxml>=4.6.0
html5lib>=1.1

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