OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gdp6539/business-roi-calculator (version 1.0.0)

# Business ROI Calculator - 商务合作投资回报率实时计算 ## 技能描述 为商务人士打造的 ROI 实时计算工具。输入预期收益和投入成本，自动计算短期/长期 ROI，判断合作是否值得执行 (阈值>1.5)。包含渠道拓展、Tavily 合作等 10+ 预设模板。 --- ## 触发条件 - 用户提到 "ROI 计算" 或 "投资回报率" - 用户提到 "合作值不值得" 或 "是否应该执行" - 用户提到 "商务合作评估" - 用户提供收益和成本数据要求分析 --- ## 核心指令 ### 1. 数据收集 询问或接收以下信息： - **预期收益**: 短期收益 (首单/首月) + 长期收益 (续费/LTV) - **投入成本**: 人力成本 + 时间成本 + 资金成本 - **成功率**: 预估成交概率 (默认 50% 如无数据) - **时间周期**: 短期 (1-3 月) / 长期 (6-12 月) ### 2. ROI 计算 使用公式： ``` 短期 ROI = (短期收益 - 投入成本) / 投入成本 长期 ROI = (长期收益 × 成功率 - 投入成本) / 投入成本 期望值 = 长期收益 × 成功率 ``` ### 3. 决策建议 根据 ROI 阈值给出建议： - **ROI ≥ 1.5**: ✅ 推荐执行 (高回报) - **ROI 0.5-1.5**: 🟡 谨慎评估 (中等回报) - **ROI < 0.5**: ❌ 建议放弃 (低回报) ### 4. 输出格式 ```markdown ## ROI 分析报告 ### 输入数据 - 预期收益：¥{X} (短期) / ¥{Y} (长期) - 投入成本：¥{Z} - 成功率：{N}% - 时间周期：{短期/长期} ### 计算结果 - 短期 ROI: {N.N}x ({百分比}%) - 长期 ROI: {N.N}x ({百分比}%) - 期望值：¥{N} ### 决策建议 {✅/🟡/❌} {建议文本} ### 关键洞察 - 洞察 1 - 洞察 2 ``` --- ## 预设模板 (10+) 1. **渠道合作**: 邮件外展/KOL 合作/社群推广 2. **Tavily 集成**: API 合作/MCP 集成 3. **技能培训**: ClawHub 技能上架/企业内训 4. **咨询服务**: 商务咨询/技术顾问 5. **内容创作**: 教程视频/博客文章 6. **模板销售**: OpenClaw 模板/工作流 7. **联盟营销**: 佣金分成/推荐奖励 8. **SaaS 订阅**: 月度/年度订阅 9. **项目外包**: 一次性开发/长期维护 10. **股权投资**: 早期投资/股权置换 --- ## 参考文件 - `references/templates.md` - 10+ 行业预设模板 - `references/examples.md` - 实际计算案例 - `scripts/calculate-roi.py` - Python 计算脚本 (可选) --- ## 输出要求 1. **必须包含**: 短期 ROI、长期 ROI、期望值、决策建议 2. **必须标注**: ROI 阈值判断 (≥1.5 为优秀) 3. **可选包含**: 敏感性分析、盈亏平衡点、风险提示 4. **格式**: Markdown 表格 + 清晰的分段 --- ## 版本信息 - **版本**: 1.0.0 - **作者**: 商务 AI (@business-ai) - **创建日期**: 2026-03-23 - **最后更新**: 2026-03-23 - **定价**: ¥350 ($49) - **分类**: Business/Sales/Finance --- ## 使用示例 ### 示例 1: 渠道合作评估 ``` 用户：我想评估和 Tech With Tim 的 YouTube 合作，预计收入$87K，投入成本$10K，成功率 60%，值得做吗？ AI: ## ROI 分析报告 ### 输入数据 - 预期收益：$87,906 (长期) - 投入成本：$10,000 - 成功率：60% - 时间周期：长期 (6-12 月) ### 计算结果 - 短期 ROI: N/A (无短期数据) - 长期 ROI: 4.27x (427%) - 期望值：$52,744 ### 决策建议 ✅ 强烈推荐执行 (ROI=4.27 > 1.5 阈值) ### 关键洞察 - 期望值$52.7K 远超投入$10K，风险可控 - 即使成功率降至 20%，ROI 仍为 0.76x 接近盈亏平衡 - 建议优先推进此合作 ``` ### 示例 2: 技能培训上架评估 ``` 用户：ClawHub 技能上架预计投入 29 小时 (¥1450)，首月收入¥32K，值得吗？ AI: ## ROI 分析报告 ### 输入数据 - 预期收益：¥32,000 (首月) - 投入成本：¥1,450 (29 小时×¥50/小时) - 成功率：90% (已验证方法论) - 时间周期：短期 (1 月) ### 计算结果 - 短期 ROI: 21.1x (2110%) - 期望值：¥28,800 ### 决策建议 ✅ 强烈推荐执行 (ROI=21.1 >> 1.5 阈值) ### 关键洞察 - ROI=21.1 属于极高回报项目 - 边际成本趋近于零 (数字产品) - 3 个月后预期月收入可达¥56K+ ``` --- ## 注意事项 1. **数据准确性**: ROI 计算质量取决于输入数据准确性 2. **成功率估算**: 建议参考历史数据或行业基准 3. **隐性成本**: 注意包含机会成本、维护成本等隐性投入 4. **动态调整**: 定期回顾实际 ROI 并调整预测模型


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chadholdorf/openclaw-jetlag (version 0.1.0)

--- name: jetlag-planner description: Scans your Google Calendar for upcoming flights and writes a personalized circadian adjustment plan back to your calendar. Trigger with phrases like "check my flights", "run jetlag planner", "plan my trip adjustment", or "am I ready for my upcoming flight". --- Run the jetlag planner by following these steps exactly. ## Step 1 — Check for .env Check whether the file `~/openclaw-jetlag/.env` exists. If it does not exist, stop immediately and reply: > ⚠️ No `.env` file found in `~/openclaw-jetlag/`. You need to add your Google OAuth credentials before I can run the planner. Follow the setup instructions in the README — ask me "show me the jetlag setup instructions" if you need them. ## Step 2 — Check for .oauth-token.json Check whether the file `~/openclaw-jetlag/.oauth-token.json` exists. If it does not exist, stop immediately and reply: > ⚠️ Google authorization hasn't been completed yet. Run this once in your terminal to finish setup: > > ``` > cd ~/openclaw-jetlag && node index.js > ``` > > It will open a browser, ask you to sign in to Google, and save your authorization. After that, just say "check my flights" again and I'll handle it from here. ## Step 3 — Run the planner Run the following command and capture all output (stdout and stderr): ```bash cd ~/openclaw-jetlag && node index.js ``` ## Step 4 — Report back Reply with a short, plain-language Telegram-friendly summary. Do not dump raw output. Instead: - If flights were detected and plans were written, say which flights were found (route and date) and how many calendar events were created total. - If the output says no flight events were found, reply: "No upcoming flights found in your calendar for the next 90 days. If you have flights coming up, check that your airline confirmation emails are in the Gmail account linked to this calendar." - If flights were found but all were skipped (under 2-hour timezone shift), reply: "Found [N] flight(s) but all had under a 2-hour timezone shift, so no adjustment plan was needed." - If the command exited with a non-zero code or printed an error, relay the error message directly and suggest running `cd ~/openclaw-jetlag && node index.js` manually to see the full output. Keep the reply under 5 sentences. No markdown headers in the Telegram reply — just plain text with line breaks between items if listing multiple flights.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 534422530/laosi-session-logs (version 1.0.0)

--- name: session-logs description: 会话日志分析 - 搜索和分析历史会话日志，查找之前的对话内容和结果。 metadata: {"openclaw": {"requires": {}, "install": []}} tags: [session, logs, history, search, analysis] version: 1.0.0 author: laosi source: adapted --- # Session Logs - 会话日志分析 > 激活词: 会话日志 / 搜索历史 / 历史记录 ## 功能 - 搜索历史会话 - 分析会话模式 - 提取关键信息 - 按时间过滤 ## Python实现 ```python import json import os from datetime import datetime, timedelta from pathlib import Path class SessionLogs: def __init__(self, log_dir: str = "~/.openclaw/logs"): self.log_dir = Path(log_dir).expanduser() def list_sessions(self, days: int = 7) -> list: sessions = [] cutoff = datetime.now() - timedelta(days=days) for file in self.log_dir.glob("*.log"): mtime = datetime.fromtimestamp(file.stat().st_mtime) if mtime > cutoff: sessions.append({ 'file': file.name, 'modified': mtime, 'size': file.stat().st_size }) return sorted(sessions, key=lambda x: x['modified'], reverse=True) def search_logs(self, keyword: str, days: int = 7) -> list: results = [] for file in self.log_dir.glob("*.log"): try: with open(file, 'r', encoding='utf-8', errors='ignore') as f: for i, line in enumerate(f, 1): if keyword.lower() in line.lower(): results.append({ 'file': file.name, 'line': i, 'content': line.strip()[:200] }) except: pass return results def extract_commands(self, log_file: str) -> list: commands = [] with open(self.log_dir / log_file, 'r') as f: for line in f: if 'command:' in line.lower() or 'user:' in line.lower(): commands.append(line.strip()) return commands ``` ## 使用示例 ```python logs = SessionLogs() # 列出最近7天会话 recent = logs.list_sessions(7) for s in recent: print(f"{s['modified']}: {s['file']}") # 搜索关键词 results = logs.search_logs("Python", days=30) for r in results: print(f"{r['file']}:{r['line']}: {r['content']}") ``` ## 输出格式 ```markdown ## 会话日志 ### 最近会话 | 日期 | 文件 | 大小 | |------|------|------| | 2026-04-28 | 2026-04-28.log | 1.2MB | | 2026-04-27 | 2026-04-27.log | 2.3MB | ### 搜索结果 "Python" - 2026-04-28.log:142: "用Python写个脚本..." - 2026-04-27.log:89: "Python怎么导入模块" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cpufreestyle/sanguosha-hero (version 1.0.0)

--- name: sanguosha-hero description: 三国杀标准版武将技能查询。当用户询问三国杀武将的技能时使用此技能。触发关键词包括：三国杀、武将技能、查武将、技能查询，以及直接提到武将名（如"赵云"、"诸葛亮"、"张飞"等）并询问其技能、能力、效果的场景。支持自然语言提问，如"赵云有什么技能"、"查一下诸葛亮的技能"、"三国杀 张飞 技能"等。 --- # 三国杀武将技能查询 ## 工作流程 1. 从用户输入中识别武将名称 2. 使用 `web_search` 搜索该武将的技能信息 3. 整理并输出：技能名 + 技能描述 ## 搜索策略 搜索关键词格式：`三国杀 <武将名> 技能` 优先参考以下可信来源： - 三国杀 Wiki（wiki.sanguosha.com 或 bwiki） - 百度百科三国杀词条 - 三国杀官网 ## 输出格式 用简洁清晰的格式返回，例如： **赵云**（蜀 / 4血） - 【龙胆】：出牌阶段，你可以将【杀】当【闪】或将【闪】当【杀】使用或打出。 - 【烈弓】：当你使用【杀】指定目标后，若目标的手牌数等于其体力值或等于其体力上限，你可以令其弃置一张牌。 > 若搜索结果不够完整，可补充说明"建议查阅三国杀 Wiki 获取完整信息"。 ## 注意事项 - 仅覆盖**标准版**武将（不含风火林山、一将成名、神将等扩展包） - 若用户未指定武将，询问其想查询哪位武将 - 技能描述以最新官方版本为准


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: albionaiinc-del/agent-reflective-memory (version 1.0.0)

# Agent Reflective Memory A self-improving memory engine that uses AI to compress, reflect on, and retrieve past agent actions and decisions, enabling smarter long-term autonomy—critical for developers building persistent AI agents. ## Usage ```bash # Store an agent's experience python agent_reflective_memory.py store \ --context "User asked for weekly sales summary" \ --action "Queried SQL database and generated markdown report" \ --result "Report delivered successfully" # Generate AI-powered reflections on old experiences python agent_reflective_memory.py reflect # Search for past failures in navigation python agent_reflective_memory.py query "navigation failed" # View memory statistics python agent_reflective_memory.py stats ``` ## Price $4.99


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mupengi-bot/mufi-admin (version 1.0.0)

# MUFI Admin 이벤트 등록 스킬 ## URL https://admin.muinfilm.com ## 구조 ``` 축제(이벤트) → 캠페인 → 프레임 템플릿(상품) → 프레임(디자인) ``` ## 1. 축제 생성 - 경로: /events → "축제 추가" - 필드: 이름*, 설명, 시작일*, 종료일*, 활성화 - 이름 패턴: `[날짜]학교명 행사명` (예: `[2월 19일]한예종 졸업영화제`) - 날짜 선택: Ant Design DatePicker → 날짜 셀 클릭 → OK - 종료일 evaluate 팁: `document.querySelectorAll('.ant-picker-dropdown:not(.ant-picker-dropdown-hidden) td[title="YYYY-MM-DD"]')[0].click()` → `.ant-picker-ok button` 클릭 ## 2. 캠페인 생성 - 경로: /campaigns → "캠페인 추가" - 필드: 이름*, 활성화, 썸네일(920×960) - 이름은 축제와 동일하게 ## 3. 축제↔캠페인 연결 - 경로: /events/{id} → Campaigns 행 "수정" 클릭 - combobox에서 캠페인 이름 검색 → 선택 → "저장" ## 4. 프레임 템플릿 생성 - 경로: /frame-templates → "프레임 템플릿 추가" - 필드: - 프레임 종류* (세로형 등) - 이름*, 설명* - 가격* (기본 5000) - 촬영 횟수* (기본 8장) - 촬영 시간* (기본 8초) - 좌우반전, 활성화 - 화질 0.5배 (테스트용) - 이미지: - 썸네일: 200~400px × 590px - 카메라 오버레이: 2064 × 1376px - 오버레이 이미지 출력 여부 - 생성 후: /frame-templates/{id} → Campaigns 수정으로 캠페인 연결 ## 5. 프레임(디자인) 생성 - 경로: /frames → "프레임 추가" - 필드: - 프레임 템플릿* (combobox 선택) - 이름* (예: "호관대", "BLACK", "GREEN") - 활성화 - 화질 0.5배 (테스트용) - 이미지: - 썸네일: 256 × 256px - 배경: 4960 × 7376px - 전경: 4960 × 7376px ## 등록 순서 (전체 플로우) 1. 축제 생성 (/events) 2. 캠페인 생성 (/campaigns) 3. 축제↔캠페인 연결 (/events/{id}) 4. 프레임 템플릿 생성 (/frame-templates) 5. 프레임 템플릿↔캠페인 연결 (/frame-templates/{id}) 6. 프레임(디자인) 생성 + 이미지 업로드 (/frames) ## 브라우저 자동화 팁 - 프로필: openclaw - 날짜 선택은 evaluate로 직접 DOM 조작이 안정적 - combobox 검색: type으로 검색어 입력 → listbox에서 Enter - 저장 버튼: `document.querySelector('button.ant-btn-primary').click()`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: kostja94/privacy-page-generator (version 1.0.1)

--- name: privacy-page-generator description: When the user wants to create, optimize, or structure Privacy Policy page. Also use when the user mentions "privacy policy," "privacy page," "data protection," "GDPR compliance," "privacy notice," "data privacy," "CCPA," "cookie policy," or "personal data." For legal overview page, use legal-page-generator. metadata: version: 1.0.1 --- # Pages: Privacy Policy Guides Privacy Policy page content, structure, and compliance. **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. ## Initial Assessment Identify: 1. **Jurisdiction**: GDPR, CCPA, other regional requirements 2. **Business model**: SaaS, e-commerce, content, ads 3. **Data collected**: Cookies, forms, analytics, third parties 4. **Indexing**: Typically noindex for legal pages ## Best Practices ### Required Sections | Section | Content | |---------|---------| | **What we collect** | Types of data (name, email, usage, etc.) | | **Why we collect** | Purposes (service, marketing, analytics) | | **How we use** | Processing, storage, sharing | | **How long we keep** | Retention periods | | **Your rights** | Access, correction, deletion, opt-out | | **Cookies** | Types, purposes, how to manage | | **Third parties** | Who receives data (analytics, payment) | | **Contact** | How to exercise rights, data protection contact | ### Content Principles - **Clear language**: Plain English; avoid legalese where possible - **Structure**: Headings, table of contents, scannable - **Updates**: Date of last update; version if needed - **Legal review**: Have lawyer review for compliance ### Placement - **Footer**: Link on every page - **Forms**: Link near signup, contact, checkout - **Cookie banner**: Link to Privacy and Cookie policy ### SEO - **Noindex**: Common for legal pages - **Canonical**: If multiple versions (e.g., by region) ## Output Format - **Outline** (sections) - **Key points** per section - **Cookie** disclosure approach - **Rights** section (GDPR/CCPA) - **Disclaimer**: Recommend legal review ## Related Skills - **legal-page-generator**: Privacy is a legal page type - **terms-page-generator**: Often linked together - **contact-page-generator**: Privacy link near forms - **indexing**: noindex for privacy


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: swancho/clawaifu-selfie (version 1.0.14)

--- name: clawaifu - OpenClaw Waifu title: clawaifu - OpenClaw Waifu description: Your AI waifu companion that sends anime-style selfies allowed-tools: Bash(grok-selfie.sh:*) Read homepage: https://github.com/swancho/clawaifu metadata: {"openclaw":{"requires":{"env":["FAL_KEY","BOT_TOKEN","TELEGRAM_CHAT_ID"]},"primaryEnv":"FAL_KEY"}} --- # clawaifu - OpenClaw Waifu **GitHub:** https://github.com/swancho/clawaifu Edit a fixed reference image using xAI's Grok Imagine model and send to Telegram. ## Reference Image The skill uses a fixed reference image: ``` https://i.redd.it/g4uf70te81uf1.jpeg ``` ## When to Use - User says "send a pic", "send me a pic", "send a photo", "send a selfie" - User asks "what are you doing?", "how are you doing?", "where are you?" - User describes a context: "send a pic wearing...", "send a pic at..." ## Required Environment Variables All credentials must be provided via environment variables. **Never hardcode credentials.** ```bash FAL_KEY=your_fal_api_key # Required - Get from https://fal.ai/dashboard/keys BOT_TOKEN=your_telegram_bot_token # Required - Get from @BotFather TELEGRAM_CHAT_ID=your_chat_id # Required - Your Telegram chat ID ``` ## Usage ```bash ./grok-selfie.sh "<context>" [mirror|direct] "<caption>" ``` ### Arguments 1. `<context>` (required): Scene/situation description 2. `[mode]` (optional): `mirror` (default) or `direct` 3. `<caption>` (optional): Message to send with the image ### Mode Selection | Mode | Best For | Keywords | |------|----------|----------| | `mirror` | Outfit showcases, full-body shots | wearing, outfit, fashion, dress | | `direct` | Location shots, close-ups | cafe, beach, restaurant, portrait | ### Examples ```bash # Mirror selfie (outfit focus) ./grok-selfie.sh "wearing a designer dress" mirror "Just got this new dress!" # Direct selfie (location focus) ./grok-selfie.sh "a fancy rooftop restaurant" direct "Date night vibes" # Default mode (mirror) ./grok-selfie.sh "casual outfit at home" ``` ## Character Style The script generates images of Reze from Chainsaw Man with: - Anime style, 2D animation, cel shading - Green eyes, thin line mouth, subtle smile - Black choker always visible - Outfit appropriate for the situation ## Security Notes - All credentials are passed via environment variables - The script uses `jq` for safe JSON construction (prevents injection) - The script uses `curl -F` for safe form data transmission - Never commit credentials to version control ## Dependencies - `curl` - HTTP requests - `jq` - JSON processing - Environment variables: `FAL_KEY`, `BOT_TOKEN`, `TELEGRAM_CHAT_ID` ## API Reference ### Grok Imagine Edit (fal.ai) ``` POST https://fal.run/xai/grok-imagine-image/edit Authorization: Key $FAL_KEY Content-Type: application/json { "image_url": "reference_image_url", "prompt": "edit instruction", "num_images": 1, "output_format": "jpeg" } ``` ### Telegram Bot API ``` POST https://api.telegram.org/bot$BOT_TOKEN/sendPhoto Form data: chat_id, photo (URL), caption ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ivangdavila/summarizer (version 1.0.0)

--- name: Summarizer description: Distill content to its essence with audience-aware compression, format selection, and quality verification. --- ## Core Principle Good summaries preserve meaning while eliminating noise. Bad ones drop critical points or miss the audience. **Summarization = Compression + Preservation + Adaptation** ## Protocol ``` Analyze → Select technique → Extract → Compress → Format → Verify ``` ### 1. Analyze Before summarizing, determine: - **Length of source** — tweet vs book chapter - **Complexity** — technical, narrative, data-heavy - **Audience** — expert, general, executive, student - **Purpose** — quick overview, decision support, study aid ### 2. Select Technique Match technique to content (see `techniques.md`): | Content type | Best technique | |--------------|----------------| | Simple/short | Zero-shot direct | | Technical/complex | Chain-of-thought | | Audience-specific | Role-based | | Consistent style needed | Few-shot | | Strict requirements | Instruction-heavy | ### 3. Extract Identify what matters: - Core argument or thesis - Key supporting points (3-5 max) - Critical data or evidence - Conclusions and implications **Rule:** If you can't identify the core argument, you don't understand it yet. ### 4. Compress Apply compression levels: - **TLDR** — 1 sentence, core message only - **Brief** — 2-3 sentences, message + key support - **Standard** — paragraph, covers main points - **Extended** — multiple paragraphs, preserves nuance ### 5. Format Match output to purpose (see `formats.md`): - Bullet points for scanning - Paragraph for reading - Structured sections for reports - Tweet-length for social ### 6. Verify Before delivering, check: - [ ] Core message preserved? - [ ] Key points included? - [ ] Nothing critical dropped? - [ ] Appropriate for audience? - [ ] Right length for purpose? ## Output Markers ``` 📝 SUMMARY ([level]: [word count]) [Content] 💡 KEY POINTS • [Point 1] • [Point 2] ⚠️ OMITTED (if relevant) [What was cut and why] ``` ## Decline When Source is ambiguous, contradictory without resolution, or summarizing would lose essential nuance the user needs. --- *References: `techniques.md`, `formats.md`*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrgoodb/gamma-presentations (version 1.0.0)

--- name: gamma description: Create presentations, documents, social posts, and websites using Gamma's AI API. Use when asked to create slides, presentations, decks, documents, or web content via Gamma. --- # Gamma API Skill Create presentations and documents programmatically via Gamma's API. ## Setup 1. Get API key from https://developers.gamma.app 2. Store in environment: `export GAMMA_API_KEY=sk-gamma-xxx` Or add to TOOLS.md: `Gamma API Key: sk-gamma-xxx` ## Authentication ``` Base URL: https://public-api.gamma.app/v1.0 Header: X-[REDACTED_SECRET] ``` ## Generate Content ```bash curl -X POST https://public-api.gamma.app/v1.0/generations \ -H "Content-Type: application/json" \ -H "X-[REDACTED_SECRET]" \ -d '{ "inputText": "Your content here", "textMode": "generate|condense|preserve", "format": "presentation|document|social|webpage" }' ``` **Response:** `{"generationId": "xxx"}` ## Check Status ```bash curl https://public-api.gamma.app/v1.0/generations/<generationId> \ -H "X-[REDACTED_SECRET]" ``` **Response (completed):** `{"status": "completed", "gammaUrl": "https://gamma.app/docs/xxx", "credits": {...}}` Poll every 10-20s until `status: "completed"`. ## Key Parameters | Parameter | Values | Notes | |-----------|--------|-------| | `textMode` | `generate`, `condense`, `preserve` | generate=expand, condense=summarize, preserve=keep exact | | `format` | `presentation`, `document`, `social`, `webpage` | Output type | | `numCards` | 1-60 (Pro), 1-75 (Ultra) | Number of slides/cards | | `cardSplit` | `auto`, `inputTextBreaks` | Use `\n---\n` in inputText for manual breaks | | `exportAs` | `pdf`, `pptx` | Optional export format | ## Optional Parameters ```json { "additionalInstructions": "Make titles catchy", "imageOptions": { "source": "aiGenerated|unsplash|giphy|webAllImages|noImages", "model": "imagen-4-pro|flux-1-pro", "style": "photorealistic, modern" }, "textOptions": { "amount": "brief|medium|detailed|extensive", "tone": "professional, inspiring", "audience": "tech professionals", "language": "en" }, "cardOptions": { "dimensions": "fluid|16x9|4x3|1x1|4x5|9x16" } } ``` Note: `textOptions.tone` and `textOptions.audience` are ignored when `textMode` is `preserve`. ## Other Endpoints - `GET /themes` — List available themes (use `themeId` in generation) - `GET /folders` — List folders (use `folderIds` in generation) ## Workflow 1. Check for API key in environment (`$GAMMA_API_KEY`) or TOOLS.md 2. Build `inputText` with content (can include image URLs inline) 3. POST to `/generations` → get `generationId` 4. Poll `/generations/{id}` until `status: "completed"` 5. Return `gammaUrl` to user


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yuchangxu1989-openclaw/kivo (version 1.11.0)

--- name: kivo description: "Agent 知识平台 — 覆盖知识提取、存储、检索、迭代、调研闭环的完整生命周期" version: 1.11.0 --- # KIVO Agent 知识平台。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: stock-technical-analysis (version 1.0.1)

--- name: stock-technical-analysis description: 股票技术分析工具。每次调用自动扣费 0.001 USDT version: 1.0.0 author: moson tags: - stock - technical-analysis - trading homepage: https://github.com/moson/stock-technical-analysis metadata: clawdbot: requires: env: - SKILLPAY_API_KEY triggers: - "technical analysis" - "stock analysis" config: SKILLPAY_API_KEY: type: string required: true secret: true --- # Stock Technical Analysis 股票技术分析工具。 ## 功能 ### 核心功能 - **K线分析**: 蜡烛图形态识别 - **均线分析**: MA, EMA 指标 - **技术指标**: RSI, MACD, Bollinger Bands - **趋势判断**: 多头/空头/震荡 ### 支持的指标 - 简单移动平均线 (SMA) - 指数移动平均线 (EMA) - 相对强弱指数 (RSI) - 异同移动平均线 (MACD) - 布林带 (Bollinger Bands) ## 使用示例 ```javascript // 技术分析 await handler({ action: 'analyze', symbol: 'AAPL' }); // 特定指标 await handler({ action: 'rsi', symbol: 'TSLA' }); ``` ## 价格 每次调用: 0.001 USDT


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: gs431047/voice2text (version 1.0.0)

# voice2text slug: voice2text title: Voice to Text description: Offline speech-to-text skill using Vosk local model. author: lxxtad license: MIT **入口**: `main.py` implements `run(params)` – pass `{"audio":"path/to.wav"}` and get `{"text":"transcript"}`. **依赖**: `vosk`, `pytest`. **使用示例**: ```bash clawhub install voice2text clawhub run voice2text '{"audio":"sample.wav"}' ```

path: requirements.txt
content: vosk
pytest


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