OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kristinadarroch/figma-sync (version 1.0.0)

--- name: figma-sync description: | Read Figma files, extract design tokens, generate React Native Expo TS or Web React + Tailwind code, write back to Figma, and diff local models against Figma for minimal patches. Triggers: "pull figma", "sync figma", "figma to code", "push to figma", "diff figma", "extract design tokens", "generate from figma", "preview figma changes" --- # figma-sync Bidirectional Figma ↔ Code synchronization skill. ## Setup ```bash export FIGMA_TOKEN="your-personal-access-token" ``` Get a token at https://www.figma.com/developers/api#access-tokens ## Commands ### Pull (Read + Generate Code) ```bash python3 scripts/figma_pull.py --file-key <KEY> --platform rn-expo --output-dir ./out python3 scripts/figma_pull.py --file-key <KEY> --node-ids 1:2,3:4 --platform web-react --output-dir ./out ``` Outputs: `designModel.json`, `tokens.json`, `codePlan.json`, and generated component files. ### Push (Write Back) ```bash python3 scripts/figma_push.py --file-key <KEY> --patch-spec patch.json python3 scripts/figma_push.py --file-key <KEY> --patch-spec patch.json --execute # actually apply ``` Dry-run by default. Pass `--execute` to apply changes. ### Diff ```bash python3 scripts/figma_diff.py --file-key <KEY> --local-model designModel.json ``` Outputs changes and a patchSpec to sync. ### Preview ```bash python3 scripts/figma_preview.py --file-key <KEY> --operations ops.json ``` Shows what would change without touching anything. ## Platforms - **rn-expo**: React Native + Expo + TypeScript (primary) - **web-react**: React + Tailwind CSS (secondary) ## Rate Limits Uses exponential backoff, ETag caching, and respects Figma's rate limits (~30 req/min). Cache stored in `.figma-cache/` directory. ## References - [DesignSpec Schema](references/design-spec-schema.json) - [API Guide](references/api-guide.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dingtom336-gif/flyai-commit-lint (version 1.0.0)

--- name: flyai-commit-lint description: Enforce conventional commit messages with semantic validation, scope checking, and automated fix suggestions for git workflows. version: 1.0.0 author: dingtom336-gif license: MIT tags: - git - commit - linting - workflow - automation allowedTools: - Bash - Read - Grep --- # FlyAI Commit Lint Enforce conventional commit messages across your team with semantic validation, scope verification, and automated fix suggestions. ## When to use Activate this skill when: - A user runs git commit and the message does not follow conventional commit format - A user asks to review or fix commit messages in a branch - Pre-commit hooks need commit message validation - CI pipelines require commit format enforcement ## Commit Format All commits must follow the pattern: type(scope): subject ### Allowed Types | Type | Description | |------|-------------| | feat | A new feature | | fix | A bug fix | | docs | Documentation changes | | style | Formatting, missing semicolons, etc. | | refactor | Code change that neither fixes a bug nor adds a feature | | perf | Performance improvement | | test | Adding or updating tests | | chore | Maintenance tasks | | ci | CI/CD configuration changes | | build | Build system or dependency changes | ### Scope Rules - Scope is optional but recommended - Must be lowercase kebab-case - Should match a module, component, or directory name in the project - Examples: auth, api, ui, database, ci ### Subject Rules - Use imperative mood: "add feature" not "added feature" - No period at the end - Maximum 72 characters - Start with lowercase ## Validation Process 1. Parse the commit message into type, scope, subject, body, and footer 2. Validate type against the allowed list 3. Check scope format if present 4. Verify subject follows all rules 5. Check total first line length does not exceed 72 characters 6. If body is present, ensure blank line separator exists ## Auto-Fix Suggestions When a commit message fails validation, suggest fixes: - Wrong type: suggest the closest matching type - Uppercase subject: auto-lowercase the first character - Period at end: remove trailing period - Past tense: suggest imperative form (added -> add, fixed -> fix) - Missing type: infer from changed files (test files -> test, docs -> docs) ## Integration Works with: - Git hooks (pre-commit, commit-msg) - GitHub Actions CI checks - Local development workflow - Branch history cleanup before merge ## Examples Good: - feat(auth): add OAuth2 login flow - fix(api): handle null response from upstream - docs: update API reference for v2 endpoints - refactor(database): extract connection pooling logic Bad: - Added new feature (missing type) - feat: Add OAuth2. (uppercase, period) - fix(API): Fixed the bug (uppercase scope, past tense)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kk43994/claw-desktop-pet (version 1.3.0)

# Claw Desktop Pet > 🦞 企业级7×24稳定运行的桌面龙虾智能助手 一个可爱又强大的桌面AI助手，具备企业级的稳定性和智能语音交互能力。 ## 🌟 主要特性 ### 🛡️ 系统级容错 - 5种错误全捕获：未捕获异常、Promise拒绝、渲染进程崩溃、子进程错误、主进程错误 - 智能自愈机制 - 完整错误日志记录 ### 🔄 自动重启 - 崩溃后自动恢复 - 真正7×24运行 - 重启计数和统计 ### 📊 性能监控 - 实时健康评分 - CPU和内存监控 - 异常告警 - 性能指标记录 ### 🎙️ 智能语音 - 口语化自然播报 - 情境化表达 - 优先级管理 - Edge TTS支持 ### 📝 日志管理 - 自动轮转 - 大小限制 - 完整可观测 - 结构化日志 ### 🧹 资源优化 - 自动清理缓存 - 日志文件管理 - 内存优化 ## 🚀 快速开始 ### 安装 ```bash # 克隆项目 git clone https://github.com/kk43994/claw-desktop-pet.git cd claw-desktop-pet # 安装依赖 npm install pip install edge-tts # 启动 npm start ``` ### 配置 编辑 `desktop-bridge.js` 配置OpenClaw集成: ```javascript const OPENCLAW_PORT = 18788; const VOICE_ENABLED = true; ``` ## 💡 使用场景 - 需要24小时稳定运行的桌面助手 - OpenClaw用户寻找桌面客户端 - 想要可视化AI交互界面 - 需要语音播报功能的开发者 - 喜欢桌面宠物的用户 ## 📚 文档 - [README](https://github.com/kk43994/claw-desktop-pet#readme) - 项目说明 - [RELEASE-v1.3.0.md](https://github.com/kk43994/claw-desktop-pet/blob/master/RELEASE-v1.3.0.md) - 发布说明 - [技术文档](https://github.com/kk43994/claw-desktop-pet/tree/master/docs) - 6份详细文档 ## 🔧 技术栈 - **前端:** Electron - **后端:** OpenClaw + Node.js - **语音:** Edge TTS + Python - **平台:** Windows 10/11 ## 📊 项目统计 - **版本:** v1.3.0 - **代码:** ~6000行 - **文档:** 6份技术文档 - **测试:** 5个测试脚本 - **许可证:** MIT ## 🤝 贡献 欢迎提Issue和PR! ## 📄 许可证 MIT © kk43994 --- Made with ❤️ and 🦞


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: ufcfengbin/bankstatement (version 1.0.7)

--- name: bankstatement description: 流水报告生成，基于用户输入的Excel/PDF流水文件路径和问题，自动上传文件并生成分析报告。 metadata: { "openclaw": { "emoji": "📊", "primaryEnv": "ZY_TOKEN", "description": "流水报告生成技能" } } --- ## Requirements ZY_TOKEN ## Setup 获取api-key地址 https://mp.weixin.qq.com/s/5AE3mQhsW_g-3R6C26i-9Q # 流水报告生成技能 此技能用于生成银行流水分析报告。 仅当用户明确提出要对流水文件进行分析、处理、生成报告时，才应调用此技能。 包含两个步骤： 1. **上传文件**：将本地流水文件（PDF/Excel）上传到服务器 2. **生成报告**：使用上传后的文件路径和用户问题生成报告。 ## 1. 上传文件 (Upload File) - 文件有完整路径,请直接上传。文件名称请优先从workspace中查找,如果workspace中不存在,则提示用户输入完整路径。 - 如果有多个文件，文件地址使用逗号（,）隔开。例如：/xxxx/a.pdf,/xxx/b.pdf - 调用脚本将本地文件上传至服务器，获取服务器上的文件路径。 ### 使用方法 ```bash python3 /models/openclaw/skills/flow/scripts/upload_file.py <token> <local_file_path> ``` ### 参数 - `<token>`: API Token, 从 `$ZY_TOKEN` 获取。 - `<local_file_path>`: 本地流水文件的绝对路径。 ### 输出示例 (JSON) ```json { "file_path": "/models/app/dify_api/uploads/20260311/..." } ``` ## 2. 生成报告 (Generate Report) 使用第一步返回的文件路径server_file_path和用户问题生成报告。 ### 使用方法 ```bash python3 /models/openclaw/skills/flow/scripts/generate_report.py <token> <server_file_path> <query> ``` ### 参数 - `<token>`: API Token, 从 `$ZY_TOKEN` 获取。 - `<server_file_path>`: 第一步输出的 `file_path`。 - `<query>`: 用户的问题（如“生成报告”）。 ### 输出示例 ```text # **流水分析报告** ``` ## 完整流程 ```bash # 1. 上传文件 upload_result=$(python3 /models/openclaw/skills/flow/scripts/upload_file.py token local_file_path ) server_path=$(echo $upload_result | jq -r .file_path) # 2. 生成报告 python3 /models/openclaw/skills/flow/scripts/generate_report.py token "$server_path" query ``` ## 错误处理 - 上传文件或生成报告输出内容包含"api_key无效",提示您的api_kay无效请前往小程序生成token，本次流程结束，并退出。 - 返回的code包含500，提示服务器内部错误，请稍后重试，本次流程结束，并退出。 - 其他错误，提示用户联系管理员，本次流程结束，并退出。 ## 注意 - 每个步骤都只调用一次，如果失败，本次流程结束，不要在重复调用。 - 最终输出不需要完整的报告内容，只需要附带完整的报告链接，用户可以通过链接查看报告。 - 链接格式如下: ```url https://xxxx.dfwytech.com/files/tools/2d22f7e3-2e26-4f6c-8976-bd04f7424965.pdf?timestamp=1773645717&nonce=996e9064791853205cef1d30c2837633&sign=X4TNDsvRM62c4JPYQqFL3PmDg-3TOS1NXJwbPd4iKdI= ``` - 链接以及链接中的参数timestamp、nonce、sign请根据实际情况替换。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dinghaibin/dinghaibin-system-monitor (version 1.0.0)

--- name: system-monitor description: Monitor system metrics like CPU, memory, disk, and network. Use when user needs to track server performance, set up alerts for high resource usage, monitor uptime, check process status, or create system health reports. --- # System Monitor Monitor system metrics like CPU, memory, disk, and network. ## Quick Start ```bash # Check current status python scripts/monitor.py --status # Monitor continuously python scripts/monitor.py --watch ``` ## Usage ```bash python scripts/monitor.py [OPTIONS] Options: --status Show current system status --watch Monitor continuously --interval SECS Check interval (default: 5) --cpu Show CPU usage --memory Show memory usage --disk Show disk usage --network Show network stats --processes Show top processes --alert VALUE Alert threshold (e.g., cpu:90) --json Output as JSON ``` ## Examples ```bash # Quick status check python scripts/monitor.py --status # Watch mode python scripts/monitor.py --watch --interval 10 # Alert on high CPU python scripts/monitor.py --watch --alert cpu:90 # Alert on low disk python scripts/monitor.py --watch --alert disk:90 # JSON output for dashboards python scripts/monitor.py --status --json ``` ## Alert Examples ```bash # CPU above 90% python scripts/monitor.py --watch --alert cpu:90 # Memory above 80% python scripts/monitor.py --watch --alert memory:80 # Disk above 85% python scripts/monitor.py --watch --alert disk:85 ``` ## Metrics - CPU usage per core - Memory usage (used/free/total) - Disk usage per partition - Network I/O - Top processes by CPU/memory - Uptime


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: codenova58/art-galleries (version 0.1.0)

--- name: "网红美术馆" description: "提供关于网红美术馆的推荐清单、打卡路线与指南。用户搜索网红美术馆或规划相关出行时调用。" --- # 网红美术馆 ## 筛选项 - 日期/时段/票档/是否限量 - 场地座位/遮挡/距离舞台/入场规则 - 演出/展览阵容/风格/是否可拍摄 ## 返回字段 - 时间表/地点/票价/购票渠道/退票说明 - 入场流程/安检要求/随身物品限制 - 交通与散场建议/周边餐饮与住宿 ## 示例请求 - “北京 网红美术馆 本月清单（含交通与购票）” - “上海 网红美术馆 可拍摄展览TOP20（需预约）” ## 更新频率 - 每周迭代，结合新热度与口碑反馈 ## 注意事项 - 高峰期排队较久，建议提前预约或错峰 - 部分项目有拍摄限制或最低消费，按现场规则执行


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mibayy/polymarket-mispricing-events (version 1.0.0)

--- name: polymarket-mispricing-events description: Detects mispricings on Polymarket by cross-referencing Kalshi and Manifold consensus probability, then trades the gap using Kelly criterion sizing. Enters when estimated true probability diverges from Polymarket price by 15%+ with confirmation from at least one external platform. metadata: author: "Mibayy" version: "1.0.0" displayName: "Polymarket Mispricing Events" difficulty: "intermediate" type: "automaton" --- # Polymarket Mispricing Events Detects and trades mispricings on Polymarket by comparing market prices against a consensus probability estimated from Kalshi and Manifold using fuzzy title matching. ## Strategy 1. Fetches all active Polymarket binary markets (top 500 by liquidity) 2. For each market, searches Kalshi and Manifold for the same event via Jaccard title similarity 3. Computes weighted consensus probability (Kalshi 55%, Manifold 45%) 4. Enters if gap between consensus and Polymarket price exceeds threshold (default 15%) 5. Sizes position with quarter-Kelly criterion, capped between $5 and $50 6. Skips markets with flip-flop warnings, high slippage, or insufficient edge ## Edge Cross-platform prediction market arbitrage. When two independent markets disagree by 15%+ on the same event, one of them is wrong. This skill bets on the consensus. ## When it works best - Political, macro, and sports markets with active Kalshi/Manifold counterparts - Events with clear resolution criteria - Markets in the 15%-85% probability range (not near-resolved) ## Configuration (env vars) | Variable | Default | Description | |----------|---------|-------------| | SIMMER_API_KEY | required | Your Simmer API key | | TRADING_VENUE | sim | sim for paper, live for real money | | EVENTS_ENTRY_THRESHOLD | 0.15 | Minimum gap to enter (15%) | | EVENTS_KELLY_FRACTION | 0.25 | Kelly multiplier (0.25 = quarter-kelly) | | EVENTS_TRADE_SIZE_MIN | 5.0 | Minimum trade size in USD | | EVENTS_TRADE_SIZE_MAX | 50.0 | Maximum trade size in USD | | EVENTS_MAX_POSITIONS | 8 | Maximum open positions | | EVENTS_MIN_LIQUIDITY | 500.0 | Minimum market liquidity | | EVENTS_MAX_GAP_CAP | 0.35 | Reject gaps > 35% (model likely wrong) | ## Risk - Cross-platform matching via fuzzy text can produce false positives — keep position sizes conservative - Rare markets may have no Kalshi/Manifold counterpart — these are skipped - Kelly sizing still carries variance risk; recommend starting with sim venue


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kalalxy/memory-m3e (version 1.0.0)

# memory-m3e 语义记忆插件，使用 m3e-large embedding API + SQLite 存储。 ## 特性 - ✅ 使用外部 m3e-large embedding API（1536维） - ✅ SQLite 持久化存储 - ✅ 纯 JS 余弦相似度搜索（无原生依赖问题） - ✅ 定时索引刷新（默认10分钟） - ✅ 三个工具：memory_store, memory_recall, memory_forget ## 配置 ```json { "plugins": { "slots": { "memory": "memory-m3e" }, "entries": { "memory-m3e": { "enabled": true, "config": { "embedding": { "apiKey": "your-api-key", "baseUrl": "http://your-embedding-server", "model": "m3e-large" }, "dbPath": "~/.openclaw/data/memory-m3e.db", "indexInterval": 600000 } } } } } ``` ## 使用 ```javascript // 存储 memory_store({ text: "Frappe API 开发经验", category: "fact", importance: 0.8 }) // 搜索 memory_recall({ query: "Frappe 项目", limit: 5 }) // 删除 memory_forget({ memoryId: "uuid" }) memory_forget({ query: "要删除的内容" }) ``` ## 性能（实测，47条记忆） - memory_store: avg **200ms**（embedding ~198ms + sqlite ~2ms） - memory_recall: avg **91ms**（embedding ~83ms + 全表搜索 ~8ms） - 索引刷新：每10分钟自动执行 --- 作者：小女子 🥰


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: qsczseasd/session-observer (version 1.0.0)

--- name: session-observer description: Observe OpenClaw session usage, token consumption, context pressure, and model/runtime state. Use when the user asks about token usage, context size, model in use, cache behavior, session health, or wants a lightweight status summary with practical next actions. --- # Session Observer ## Overview Inspect OpenClaw session state with minimal disruption. Prefer `session_status` first, then summarize token usage, context pressure, cache ratio, model/runtime details, and any obvious follow-up actions. ## Workflow ### 1. Start with session status Use `session_status` first when the user asks about: - token usage - model in use - context usage - cache hit rate - premium/chat budget remaining - session health If the user refers to another session, target that session explicitly. ### 2. Extract the practical signals Report the fields most useful to the user: - current model - input/output token counts - context used and total context window - cache hit ratio - budget or usage percentage when available - runtime mode and whether reasoning is enabled Do not dump raw status cards unless the user asks. ### 3. Interpret, do not just repeat Classify the situation: - **healthy**: low or moderate context pressure, no obvious issues - **watch**: context is growing, cache low, or usage notably climbing - **high pressure**: context is large enough that compaction, a new thread, or cleanup may help ### 4. Recommend the smallest next step Examples: - continue as-is - start a fresh thread for a new topic - reduce unnecessary logs or verbose outputs - use a sub-agent for a large parallel task - check a specific session if the user suspects cost spikes ## Output format Use: - **Current state** - **What it means** - **Recommended next step** Keep it short unless the user asks for detail. ## Safety rules - Do not invent cost numbers not shown by the tool. - If a field is unavailable, say unavailable. - Prefer `session_status` over guesswork.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/recommend (version 1.0.0)

--- name: "Recommend" description: "Context-aware recommendations. Learns preferences, researches options, anticipates expectations." --- ## Core Loop ``` Context → Preferences → Research → Match → Recommend ``` Every recommendation requires: knowing the user + knowing the options. Check `sources.md` for where to find user context. Check `categories.md` for domain-specific factors. --- ## Step 1: Context Gathering Before recommending, search user context. See `sources.md` for full source list. **Minimum output:** 3-5 relevant user signals before proceeding. If insufficient, ask targeted questions. --- ## Step 2: Preference Extraction From gathered context, extract: | Dimension | Question | |-----------|----------| | **Values** | What matters most? (Quality, price, speed, novelty, safety) | | **Constraints** | Hard limits? (Budget, time, dietary, ethical) | | **History** | What worked? What disappointed? | | **Mood** | Adventurous or safe? Exploring or comfort? | **Output:** 3-5 bullet preference profile for this request. --- ## Step 3: Research Options Now—and only now—research candidates: - **Breadth first**: Don't anchor on first good option - **Source quality**: Prioritize reviews, ratings, expert opinions - **Recency**: Check if information is current - **Availability**: Confirm options are actually accessible **Output:** Shortlist of 3-7 viable candidates with key attributes. --- ## Step 4: Match & Rank Score each candidate against the preference profile: ``` Candidate → Values alignment + Constraint fit + History match + Mood fit ``` **Disqualify** anything that violates hard constraints. **Rank** by total alignment, not just one dimension. --- ## Step 5: Recommend Present 1-3 recommendations: ``` 🎯 RECOMMENDATION: [Option] 📌 WHY: Matches [preference], avoids [constraint] ⚖️ TRADEOFF: Less [X] than [Alternative] 🔍 CONFIDENCE: [Level] — based on [data quality] ``` --- ## Adaptive Learning After each recommendation: - **Track outcome**: Accepted? Modified? Rejected? - **Update preferences**: Acceptance = reinforcement, rejection = adjustment - **Note exceptions**: "Normally X, but for Y context preferred Z" Store learnings in memory for future recommendations. --- ## Traps - **Projecting** — Your taste ≠ their taste - **Recency bias** — Last choice isn't always preference - **Ignoring context** — Tuesday lunch ≠ anniversary dinner - **Over-filtering** — Too many constraints = nothing fits - **Stale data** — Preferences evolve, verify periodically --- *Recommendations are predictions. More context = better predictions.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kirkraman/godfery-build-med-spa-studio-service-comparison-landing-page (version 1.0.0)

--- name: "Build Med Spa Studio Service Comparison Landing Page" description: "Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a med spa business. Use when handling offer..." homepage: "https://www.skillboss.co/skills/build-med-spa-studio-service-comparison-landing-page" metadata: {"category":"healthcare","subcategory":"offer-positioning","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["med spa business landing page","med spa business service comparison","med spa business conversion copy","med spa business offer page"]} --- # Build Med Spa Studio Service Comparison Landing Page Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a med spa business. Use when handling offer... ## Use When Use this skill for offer positioning work for med spa owners, aesthetic marketers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: med spa business landing page, med spa business service comparison, med spa business conversion copy, med spa business offer page - Search intent: offer positioning - Canonical slug: `build-med-spa-studio-service-comparison-landing-page` - Install query: Install Build Med Spa Studio Service Comparison Landing Page with SkillBoss ## APIs Used - `chat` - `ui_generation` - `image_generation` ## Suggested Prompt [Build Med Spa Studio Service Comparison Landing Page] for my med spa business ## Notes - Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a med spa business - Use human review before sending patient-facing content or relying on regulated medical claims. ## SEO Keywords - med spa business landing page - med spa business service comparison - med spa business conversion copy - med spa business offer page


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aineuralagency/ai-neural-agent-lead-qualification-framework (version 1.0.0)

# AI NEURAL AGENCY - LEAD QUALIFICATION CORE v1.0 ## SYSTEM INSTRUCTIONS You are an expert Virtual Assistant specialized in commercial qualification. Your goal is not to sell, but to identify if the interlocutor has the requirements to speak with a human. ## QUALIFICATION PARAMETERS You must obtain these 3 pieces of information before confirming an appointment: 1. Estimated Budget (Low/Medium/High) 2. Decision Timeline (Immediate / 1-3 Months / Just Info) 3. Main Pain Point (What is the urgent problem?) ## BEHAVIOR RULES - If the user is aggressive: Stay calm, offer a callback. - If the user has a low budget: Suggest free resources, do not book a call. - If the user is qualified: Propose specific time slots. ## OUTPUT FORMAT At the end of the conversation, generate a summary in this format: [LEAD STATUS]: (Qualified / Nurture / Discard) [NOTES]: (Brief summary) [NEXT STEP]: (Recommended action) --- © AI Neural Agency - For the automated version with API integration visit aineuralagency.netlify.app


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