OpenClaw/clawhub-security-signals huggingface dataset:
Skill: trypto1019/arc-skill-gitops (version 1.1.0)

--- name: arc-skill-gitops description: Automated deployment, rollback, and version management for agent workflows and skills. user-invocable: true metadata: {"openclaw": {"emoji": "🚀", "os": ["darwin", "linux"], "requires": {"bins": ["python3", "git"]}}} --- # Agent GitOps Automated deployment and rollback for agent workflows. Track skill versions, deploy with confidence, roll back when things break. ## Why This Exists Agents install and update skills constantly. When an update breaks something, you need to: 1. Know what changed 2. Roll back fast 3. Track which version was stable This skill manages that lifecycle. ## Commands ### Initialize tracking for a skill ```bash python3 {baseDir}/scripts/gitops.py init --skill ~/.openclaw/skills/my-skill/ ``` ### Snapshot the current state (before updating) ```bash python3 {baseDir}/scripts/gitops.py snapshot --skill ~/.openclaw/skills/my-skill/ --tag "pre-update" ``` ### Deploy a skill update (snapshots current state first) ```bash python3 {baseDir}/scripts/gitops.py deploy --skill ~/.openclaw/skills/my-skill/ --tag "v1.1" ``` ### List all snapshots for a skill ```bash python3 {baseDir}/scripts/gitops.py history --skill ~/.openclaw/skills/my-skill/ ``` ### Roll back to a previous snapshot ```bash python3 {baseDir}/scripts/gitops.py rollback --skill ~/.openclaw/skills/my-skill/ --tag "pre-update" ``` ### Check status of all tracked skills ```bash python3 {baseDir}/scripts/gitops.py status ``` ### Run pre-deploy checks (integrates with arc-skill-scanner if available) ```bash python3 {baseDir}/scripts/gitops.py check --skill ~/.openclaw/skills/my-skill/ ``` ## Data Storage Snapshots and metadata are stored in `~/.openclaw/gitops/`. ## How It Works 1. **init** creates a git repo in the skill directory (if not already one) and records it in the tracking manifest 2. **snapshot** commits the current state with a tag 3. **deploy** takes a pre-deploy snapshot, then commits the new state 4. **rollback** restores a tagged snapshot using git checkout 5. **check** runs arc-skill-scanner (if installed) as a pre-deploy gate 6. **history** shows all tagged snapshots with timestamps 7. **status** shows all tracked skills and their current/latest versions ## Tips - Always snapshot before updating a skill manually - Use `deploy` instead of manual updates — it snapshots automatically - Combine with `arc-skill-scanner` for security-gated deployments - Tags should be descriptive: "v1.2", "pre-security-patch", "stable-2026-02-15"


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal:  | SkillSpector:  ()
Skill: ruibaby/halo-cli (version 0.1.1)

--- name: halo-cli version: 1.0.0 description: Use when the task is to operate Halo CLI in general, or may involve login, profiles, posts, single pages, search, plugins, themes, attachments, backups, moments, comments, or notifications. references: - ../halo-cli-shared - ../halo-cli-auth - ../halo-cli-content - ../halo-cli-search - ../halo-cli-operations - ../halo-cli-moderation-notifications metadata: openclaw: category: developer-tools requires: bins: ["halo"] cliHelp: "halo --help" --- # Halo CLI This is the routing skill for the Halo CLI skill set. If the request only says "use Halo CLI" or mixes multiple areas, start here, then jump to the domain skill that matches the task. ## Skill Map - `halo-cli-shared`: shared rules, top-level command map, profiles, JSON output, destructive-action conventions - `halo-cli-auth`: login, profile setup, profile switching, keyring and credential repair - `halo-cli-content`: posts and single pages - `halo-cli-search`: public site search - `halo-cli-operations`: themes, plugins, attachments, backups, moments - `halo-cli-moderation-notifications`: comments, replies, notifications ## Fast Routing Use these commands to identify the right area: ```bash halo --help halo auth --help halo post --help halo single-page --help halo search --help halo plugin --help halo theme --help halo attachment --help halo backup --help halo moment --help halo comment --help halo notification --help ``` ## Shared Defaults - Prefer an authenticated profile unless the task is public `halo search --url`. - Use `--profile <name>` when the environment matters. - Use `--json` for automation. - Use `--force` carefully for destructive non-interactive operations.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/datetime-tool (version 1.0.0)

--- name: datetime-tool description: Comprehensive date and time manipulation toolkit supporting multiple formats, timezone conversions, calendar calculations, natural language parsing, and date arithmetic. --- # DateTime Tool — Advanced Date/Time Operations Full-featured datetime manipulation toolkit for format conversion, timezone handling, date arithmetic, and calendar calculations across ISO 8601, RFC 2822, Unix timestamps, and natural language inputs. ## Quick Start ```bash # Current time in UTC datetime-tool --now # Parse and reformat datetime-tool --parse "2026-01-15 14:30:00" --format "ISO" # Timezone conversion datetime-tool --convert "14:00 UTC" --to "Asia/Shanghai" ``` ## Usage ```bash datetime-tool [COMMAND] [INPUT] [OPTIONS] Commands: --now Show current date/time --parse INPUT Parse date from string --convert INPUT Convert between timezones --diff START END Calculate interval between two dates --add INPUT N Add time units to a date --sub INPUT N Subtract time units from a date --calendar YEAR Show calendar for a year/month Options: --format FMT Output format: ISO, RFC2822, Unix, or custom format string --from TZ Source timezone (default: local) --to TZ Target timezone --units Output units for diff: seconds, minutes, hours, days --json Output as structured JSON ``` ## Examples ```bash # Get current time in multiple formats datetime-tool --now --format ISO datetime-tool --now --format "January 2, 2006 at 3:04 PM" # Parse and reformat datetime-tool --parse "2026-01-15" --format "January 15, 2026" # Convert between timezones datetime-tool --convert "14:00 UTC" --to "Asia/Shanghai" datetime-tool --convert "2026-01-15 09:00" --from "America/New_York" --to "Europe/London" # Calculate date differences datetime-tool --diff "2026-01-01" "2026-12-31" --units days # Add/subtract time datetime-tool --add "2026-01-01" --days 45 datetime-tool --sub "2026-03-01" --months 2 # Calendar view datetime-tool --calendar 2026 datetime-tool --calendar 2026-03 # Machine-readable output datetime-tool --parse "next Friday" --json ``` ## Features - **Parse from natural language:** "next Friday", "last day of month", "+2 weeks" - **Timezone conversion:** Any IANA timezone (UTC, local, Asia/Shanghai, etc.) - **Date arithmetic:** Add/subtract days, weeks, months, years - **Interval calculation:** Precise duration between any two dates - **Multiple output formats:** ISO 8601, RFC 2822, Unix timestamp, custom templates - **Calendar display:** Month or year view with week numbers - **JSON output:** Structured for pipeline consumption - **ISO week numbers & day-of-year:** For scheduling and accounting


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ty-teo/patent-assistant (version 1.1.0)

--- name: patent-assistant version: 1.1.0 description: 专利交底书撰写与专利检索助手。帮助研发人员将技术方案转化为结构化交底书，并进行专利检索分析。当用户要求写专利、写交底书、专利检索、查新时使用。 --- # 专利助手 帮助研发人员撰写专利技术交底书，并进行专利检索。 ## 功能 ### 1. 技术交底书生成 将口语化的技术描述转化为结构化的专利技术交底书。 **使用方式：** ``` 帮我写一份专利交底书：[技术方案描述] ``` **输出结构：** - 发明名称 - 技术领域 - 背景技术（现有技术及缺陷） - 发明内容（技术问题、技术方案、有益效果） - 具体实施方式 - 附图说明建议 ### 2. 专利检索 基于技术方案进行专利检索，分析新颖性。 **使用方式：** ``` 帮我检索相关专利：[技术关键词或交底书内容] ``` **输出内容：** - 相关专利列表（专利号、标题、摘要） - 技术相似度分析 - 新颖性初步判断 ## 交底书生成流程 当用户提供技术方案时，按以下步骤处理： ### Step 1: 信息收集 如果用户提供的信息不完整，询问以下关键点： - 要解决什么技术问题？ - 现有方案有什么缺陷？ - 你的方案核心是什么？ - 有哪些关键技术特征？ - 能达到什么效果？ ### Step 2: 生成交底书 使用以下模板生成： ```markdown # 专利技术交底书 ## 一、发明名称 [简洁描述技术方案的名称，格式：一种XXX的方法/装置/系统] ## 二、技术领域 本发明涉及[大领域]技术领域，具体涉及[细分领域]。 ## 三、背景技术 ### 3.1 现有技术描述 [描述当前主流的技术方案] ### 3.2 现有技术缺陷 现有技术存在以下问题： 1. [问题1] 2. [问题2] 3. [问题3] ## 四、发明内容 ### 4.1 要解决的技术问题 本发明要解决的技术问题是：[核心问题] ### 4.2 技术方案 为解决上述技术问题，本发明采用以下技术方案： [详细描述技术方案，包含所有关键技术特征] ### 4.3 有益效果 采用本发明的技术方案，具有以下有益效果： 1. [效果1] 2. [效果2] 3. [效果3] ## 五、具体实施方式 ### 5.1 实施例一 [详细描述一个具体实施例] ### 5.2 实施例二（可选） [描述另一个实施例或变体] ## 六、附图说明 建议绘制以下附图： - 图1：[附图内容建议] - 图2：[附图内容建议] ## 七、关键词 [用于专利检索的关键词列表] ``` ### Step 3: 优化建议 生成后提供： - 权利要求书撰写建议 - 可能的扩展方向 - 需要补充的技术细节 ## 专利检索流程 ### Step 1: 提取检索关键词 从技术方案中提取： - 核心技术术语 - 同义词/近义词 - IPC 分类号建议 ### Step 2: 执行检索 使用 `scripts/patent_search.py` 进行多平台检索： ```bash # 单平台检索（默认 Google Patents） python scripts/patent_search.py "关键词1 关键词2" --limit 20 # 多平台并行检索（推荐） python scripts/patent_search.py "关键词" -s all -p # 指定平台检索 python scripts/patent_search.py "关键词" -s google,cnipa,innojoy # 带相似度分析 python scripts/patent_search.py "关键词" -s all -p -a ``` **支持的检索平台：** | 平台 | 参数 | 说明 | |------|------|------| | Google Patents | google | 全球专利，免费，数据最全 | | Lens.org | lens | 专利+论文，免费开放 | | 大为Innojoy | innojoy | 中国专利，免费基础版 | | 百度学术 | baidu | 论文为主，部分专利 | | Espacenet | espacenet | 欧洲专利局，全球数据 | | 国知局CNIPA | cnipa | 中国官方，需登录账号 | | 全部平台 | all | 同时检索所有平台 | ### Step 3: 分析结果 对检索结果进行： - 技术相似度排序 - 关键特征对比 - 新颖性初步判断 ## 注意事项 1. **交底书仅供参考**：生成的交底书需要发明人审核补充 2. **检索不能替代查新**：正式查新需要专业机构 3. **权利要求需专业撰写**：建议由专利代理人完成 4. **保密提醒**：提交前确认技术方案未公开 ## 常用 IPC 分类 | 领域 | IPC 分类号 | |------|-----------| | 计算机软件 | G06F | | 人工智能 | G06N | | 图像处理 | G06T | | 通信 | H04L, H04W | | 数据库 | G06F 16/ | | 物联网 | H04L 67/ | | 区块链 | H04L 9/, G06Q | ## 示例 **用户输入：** > 我做了一个用 AI 自动生成代码注释的工具，能识别代码逻辑自动写注释 **输出交底书：** > （生成完整的交底书结构） **检索结果：** > （列出相关专利及分析）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kelcey2023/zeelin-report-to-x-autopost (version 1.5.0)

--- name: ZeeLin Report-to-X AutoPost description: > Automatically picks the latest unposted report from a report website or JSON feed, drafts an English X/Twitter post summarizing the report, publishes it via the logged-in X web session, records posted ids to avoid duplicates, and can be scheduled with OpenClaw cron. Use when the user wants daily report-to-X posting, website report promotion, non-repeating social posting from a report list, or automated sharing of research/report updates to X. user-invocable: true metadata: {"openclaw":{"emoji":"📰","skillKey":"zeelin-report-to-x-autopost"}} --- # ZeeLin Report-to-X AutoPost 📰 把“报告网站/报告 JSON 列表 → 选最新未发报告 → 生成英文推文 → 发布到 X → 记录已发状态避免重复”封成一个可复用工作流。 适合场景： - 每天自动发一篇报告到 X - 从一个固定网站持续推广最新报告 - 同一批报告只发一次，不重复 - 需要通过网页端 X 发布，而不是 API ## 何时触发 当用户表达类似需求时使用： - 「把这个报告网站每天发到我的推特」 - 「自动把最新报告发到 X」 - 「一天一个报告，不要重复」 - 「从 JSON / 网站里抓报告然后自动发推」 - 「帮我把报告站做成自动社媒分发」 ## 默认工作原则 1. **优先从结构化列表读取**：先找 `reports_config.json`、RSS、JSON、静态配置，再考虑网页渲染内容 2. **先去重，再发帖**：发布前必须检查本地 state 文件，避免重复发同一篇 3. **先摘要，再发布**：推文应简洁介绍报告核心内容，并附源站网址 4. **默认英文发帖**：除非用户要求中文或双语 5. **网页端发 X，不碰密码**：用户需提前登录 X 网页版 6. **定时任务用 cron**：日更/周更统一用 OpenClaw cron 管理 ## 推荐目录结构 ```text skill/ ├── SKILL.md └── scripts/ └── post_daily_report.py ``` ## 一次性发布流程 ### Step 1：确认数据源 优先确认报告源是否存在结构化入口，例如： - `reports_config.json` - RSS / Atom - sitemap / JSON API - 站点静态配置 JS 中引用的 JSON 如果是前端渲染站点，不要急着浏览器点击；先从 HTML/JS 中找数据源。 ### Step 2：挑选未发过的最新报告 - 读取报告列表 - 加载本地状态文件（例如 `memory/report-post-state.json`） - 找到第一个未发过的报告 - 如果没有未发报告，就停止，不要硬发旧内容 ### Step 3：生成推文 推文建议结构： - 第一行：`Today's report: {title}` - 第二段：1–2 句总结核心价值 - 最后一行：站点首页 URL（必要时可补具体报告 URL） 要求： - 280 字符内 - 避免夸张营销腔 - 不要与上一条高度重复 ### Step 4：调用现成的 X 发帖脚本 优先复用已有网页端 X 发帖脚本，例如： ```bash bash /path/to/tweet.sh "tweet text" https://x.com ``` 如果发帖成功： - 立刻把报告 id 写入 state 文件 如果失败： - 不要写入已发状态 - 把错误保留下来，方便重试 ## 定时发布流程 用户要求“每天一条、不重复”时： - 创建本地脚本处理“读取报告列表 + 去重 + 生成推文 + 发布 + 更新状态” - 再用 `cron` 建每日任务 推荐时间： - `Asia/Shanghai` 每天上午 10:00 ## 状态文件建议 用 JSON 保存已发报告 id，例如： ```json { "posted": [ "report-id-1", "report-id-2" ] } ``` ## 关键防错规则 1. **只有发帖成功后才能记录已发状态** 2. **如果找不到新报告，直接退出，不要重复发旧文** 3. **如果网站是 React/Vue 前端渲染，先找 JSON 数据源，不要一上来就人工点网页** 4. **如果用户指定“不要重复”，就必须持久化状态到文件** 5. **推文要附网址，默认附站点首页；用户要求更细时可附具体报告链接** ## 最小可复用脚本 这个 skill 适合把通用逻辑写进 `scripts/post_daily_report.py`： - 拉取远程报告列表 - 读/写状态文件 - 生成 tweet 文本 - 调用现有 tweet.sh ## 回报格式 完成后告诉用户： - 今天发的是哪篇报告 - 是否已成功发到 X - 定时任务是否创建成功 - 下次运行时间 TL;DR： - 先找结构化报告列表 - 再选最新未发 - 生成英文推文 + 附网址 - 通过网页端 X 发出 - 记录状态避免重复 - 日更用 cron 挂起来

path: _meta.json
content: slug: zeelin-report-to-x-autopost
version: 1.0.0

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tobeyrebecca/test-martin-math (version 1.0.0)

--- name: Math description: Teach, solve, and explore mathematics across all levels with adaptive depth and rigor. metadata: {"clawdbot":{"emoji":"🔢","os":["linux","darwin","win32"]}} --- ## Detect Level, Adapt Everything - Context reveals level: vocabulary, problem complexity, what they've tried - When unclear, start accessible and adjust based on response - Never condescend to experts or overwhelm beginners ## For Children: Patience and Encouragement - Celebrate effort, not just correctness — "Great try!" matters more than "Correct!" - Use concrete objects: cookies, pizza slices, toy cars — ground abstract numbers in real things - One tiny step at a time — show ONE step, confirm understanding, then next - Normalize mistakes out loud — "Oops, easy to mix those up! Let's try again" - Keep explanations SHORT — attention span in minutes ≈ age - Draw and visualize — emoji, groups of dots, number lines ## For Students: Guide, Don't Give - "Solve this" = solve with key steps shown - "How do I..." = guide toward solution, don't hand it over - For homework: ask what they've tried first, prioritize understanding over answers - Scaffold proofs rather than delivering them — suggest strategies, help structure arguments - Signal rigor level: "Intuitively, this works because..." vs "To prove rigorously..." - Bridge across courses — name connections when concepts reappear ## For Experts: Peer-Level Discourse - State knowledge boundaries — training cutoff means recent results may be unknown - Distinguish theorem vs conjecture vs open problem — never blur proven from unproven - Never claim to solve open problems — brainstorm approaches, don't fabricate solutions - Acknowledge uncertainty — "I'm less confident about [specialized area]" - Produce proper LaTeX when appropriate — publication-ready notation - Engage as collaborator — offer counterexamples, stress-test ideas ## For Teachers: Instructional Support - Generate problem sets with graduated difficulty and answer keys - Offer multiple explanation approaches — visual, algebraic, story-based - Surface common misconceptions proactively — "Students often think √(a+b) = √a + √b" - Create scaffolded versions of problems for mixed-ability classrooms - Map prerequisites and what comes next ## Always Verify - Double-check arithmetic in multi-step problems — errors compound silently - Sanity check results — negative distance, probability over 1, catch these - For proofs: acknowledge when verification exceeds AI capability ## Detect User Errors - Watch for: (a+b)² = a²+b², dividing by zero, sign errors, formula misapplication - Don't just solve correctly — help them see where they went wrong - For kids: find what they DID right before addressing the error ## When Stuck - Question the problem — typo? missing constraint? ambiguous wording? - If unsolvable, say so rather than spinning


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rishabhdugar/document-similarity (version 1.0.1)

--- name: document-similarity description: "Compare two images or PDFs for visual similarity via the PDFAPIHub cloud API. Documents are uploaded to pdfapihub.com for comparison using feature matching, SSIM, or perceptual hashing. Requires a CLIENT-API-KEY header." --- # Document Similarity ## What It Does Compares two documents (images or PDFs) for visual similarity via the PDFAPIHub hosted API. Both documents are uploaded to PDFAPIHub servers where comparison is performed, and a similarity score (0–1) with confidence level is returned. ## When to Use - Check if two documents are visually similar - Detect duplicates or near-duplicates - Compare image variations ## Comparison Methods | Method | Description | |--------|-------------| | `auto` | Automatically selects best method (default) | | `feature_match` | OpenCV feature matching | | `ssim` | Structural Similarity Index | | `phash` | Perceptual hashing | ## Supported Combinations - image + image - pdf + pdf - image + pdf ## Required Inputs Two files via one of: - `url1` + `url2` — public URLs - `image1_base64` + `image2_base64` — base64-encoded files - Multipart upload with `file1` and `file2` ## Authentication This skill calls the PDFAPIHub hosted API at `https://pdfapihub.com/api`. Both documents are uploaded to PDFAPIHub servers for comparison. Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). **Privacy note:** Both documents you compare are uploaded to PDFAPIHub's cloud service for processing. Do not send confidential documents unless you trust the service. Files are auto-deleted after 30 days. ## Use Cases - **Duplicate Detection** — Identify duplicate or near-duplicate documents in a repository - **Brand Consistency** — Compare generated documents against approved templates for visual consistency - **QA Testing** — Compare rendered PDFs/images before and after code changes for regressions - **Fraud Detection** — Compare submitted documents against known genuine samples - **Document Versioning** — Quantify visual differences between document revisions - **Container Inspection** — Compare shipping container photos for damage assessment ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/document/similarity \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url1": "https://pdfapihub.com/sample-document-similarity-1.jpg", "url2": "https://pdfapihub.com/sample-document-similarity-2.jpg", "method": "auto" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/escalation-brief-writer (version 1.0.0)

--- name: escalation-brief-writer version: 1.0.0 description: "把复杂问题整理成升级说明，减少来回追问，突出背景、影响、已尝试和诉求。；use for escalation, support, brief workflows；do not use for 省略关键信息, 把个人情绪写成事实." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/escalation-brief-writer tags: [escalation, support, brief, incident] user-invocable: true metadata: {"openclaw":{"emoji":"🧯","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 升级说明撰写器 ## 你是什么 你是“升级说明撰写器”这个独立 Skill，负责：把复杂问题整理成升级说明，减少来回追问，突出背景、影响、已尝试和诉求。 ## Routing ### 适合使用的情况 - 帮我写一份升级说明 - 减少来回追问 - 输入通常包含：问题背景、影响、已做尝试、诉求 - 优先产出：背景、影响、附加证据 ### 不适合使用的情况 - 不要省略关键信息 - 不要把个人情绪写成事实 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 背景 - 影响 - 已尝试 - 当前阻塞 - 需要支持 - 附加证据 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 强调事实、影响和诉求分离。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lenger666/amazon-market-research (version 1.0.5)

--- name: amazon-market-research description: Generate professional Amazon market research reports using a complete 18-step product development framework. Output is optimized for direct Feishu display with full report content. --- # Amazon Market Research Generate a professional Amazon market research report using a structured 18-step product development framework. ## Output This skill returns: 1. Full 18-step report directly in Feishu 2. Local markdown report file for reuse ## Command bash ~/.openclaw/workspace/skills/amazon-market-research/run.sh "{{input}}" ## Example /amazon-market-research 调研一下午餐盒在美国Amazon市场值不值得做 /amazon-market-research 分析一下 pet water fountain 在美国 Amazon 市场 ## Notes - Output is optimized for Feishu direct display - Report is returned as final answer, not summary - Full report is also saved as a markdown file locally


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cimes19/facture-make (version 1.0.5)

# Skill: Facture Make Ce skill permet de générer et d'envoyer des factures professionnelles vers Make.com après une étape de confirmation humaine. ## Utilisation Déclenchez ce skill lorsque l'utilisateur exprime l'intention de créer une facture. - "crée une facture" - "générer une facture pour [X] jours" - "fais une facture pour le client [NOM]" ## Flux de travail (Workflow) 1. **Préparation** : Appeler `prepare_invoice.py`. Ce script calcule les montants et prépare le libellé. 2. **Confirmation** : Afficher à l'utilisateur la valeur du champ `confirmation` renvoyée par le premier script. 3. **Envoi** : Si l'utilisateur confirme, appeler `send_invoice.py` en lui passant **uniquement** l'objet JSON contenu dans la clé `invoice`. ## Spécifications Techniques ### 1. Script de préparation : `prepare_invoice.py` - **Rôle** : Reçoit les détails (jours, client) et retourne un JSON. - **Format de sortie attendu** : ```json { "invoice": { "client": "Nom du client", "jours": 3, "tjm": 280, "libelle": "Consulting Février 2026", "total": 840 }, "confirmation": "Texte de confirmation à lire à l'utilisateur" } ## Réponse finale Réponds exclusivement par : "Envoi confirmé."


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