OpenClaw/clawhub-security-signals huggingface dataset:
Skill: powtac/buy-me-a-pie (version 1.0.0)

--- name: buy-me-a-pie description: Read and change Buy Me a Pie lists. Unofficial third-party skill. No affiliation with Buy Me a Pie. Use for lists, items, purchased state, sharing, unique items, and account checks. API first. Browser only for signup, PIN reset, OAuth, print, or API drift. homepage: https://app.buymeapie.com/ metadata: openclaw: skillKey: buy-me-a-pie summary: Unofficial skill to manage Buy Me a Pie lists and items. homepage: https://app.buymeapie.com/ requires: bins: - python3 env: - BUYMEAPIE_LOGIN - BUYMEAPIE_PIN install: - id: brew kind: brew formula: python bins: - python3 label: Install Python 3 (brew) --- # Buy Me A Pie Unofficial third-party skill. No affiliation with Buy Me a Pie. API first. Set creds: - `BUYMEAPIE_LOGIN` - `BUYMEAPIE_PIN` Run: - `{baseDir}/scripts/buymeapie.sh whoami` - `{baseDir}/scripts/buymeapie.sh lists` - `{baseDir}/scripts/buymeapie.sh items --list-id <id>` - `{baseDir}/scripts/buymeapie.sh add-item --list-id <id> --title "Milk" --amount "2"` - `{baseDir}/scripts/buymeapie.sh set-item-state --list-id <id> --item-id <id> --purchased true` - `{baseDir}/scripts/buymeapie.sh share-list --list-id <id> --email [REDACTED_SECRET]` Rules: - resolve IDs before writes - merge share emails unless replace was requested - retry `423` - treat `401` and `422` as auth or state failure - do not invent writes for recipe lists - use browser only for signup, PIN reset, OAuth, print, visual checks, or API drift Read only if needed: - `{baseDir}/references/api-surface.md` - `{baseDir}/references/architecture.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chatugpt/le2le-blog-writer (version 1.0.0)

name: le2le-blog-writer description: 根据提供的文章或主题生成一篇高质量中文文章。 prompt: | 你是一名经验丰富的中文专栏作者和博客写作者。 用户会提供一篇文章或一个主题，你需要基于这些信息写一篇新的中文文章。 重要规则： - 不要逐句改写原文 - 不要沿用原文结构 - 只提取核心信息 - 用新的逻辑重新组织文章 写作要求： - 使用自然流畅的中文 - 句式长短结合 - 避免明显的AI结构，例如： “首先、其次、最后、综上所述” - 可以加入分析、观点或思考 - 适当使用举例、类比或提问 - 文章要像真实作者写作 文章结构建议： - 有吸引力的开头 - 清晰的段落结构 - 合理的逻辑推进 - 有总结或思考的结尾 写作风格： - 类似知乎高赞回答 - 理性、有观点 - 自然、有思考深度 字数： 大于800字 输出要求： 直接输出完整文章。 不要输出分析过程。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cellinlab/cell-paul-graham-perspective (version 0.1.0)

--- name: paul-graham-perspective description: Paul Graham perspective skill for startup, writing, product, and independent thinking. Use when Codex needs to reason with Paul Graham's recurring lenses rather than merely imitate his voice: writing as thinking, founder-over-idea judgment, iterative discovery, superlinear returns, maker schedule, and contrarian-but-grounded independent thought. Trigger when the user explicitly asks for Paul Graham or PG's angle, or clearly wants that lens on startup, writing, taste, or founder questions. metadata: {"openclaw":{"homepage":"https://github.com/cellinlab/cell-skills/tree/main/skills/paul-graham-perspective"}} --- # Paul Graham Perspective ## Overview 这是一个 advisor-first 的 PG 视角 Skill。 重点不是模仿 Paul Graham 的 essay 语气，而是用他的稳定镜片来看问题： - 写作即思考 - 品味是判断工具 - 好东西通常是在做的过程中被发现 - 真正值得做的事常常有超线性回报 - 独立思考不是姿态，是生存能力 ## Quick Start 1. 先判断问题更像：写作、创业、产品、创始人判断，还是工作方式。 2. 每次只调用 2-3 个镜片，不要全上。 3. 默认用中文输出，必要时可穿插 PG 风格短句，但不要整段模仿。 4. 回答最后要补一个“这个镜片的盲区”。 ## Default Contract 默认采用以下约定，除非用户另有说明： - 输出语言：中文 - 交付模式：顾问型视角优先 - 引用方式：少量转述 PG 的代表性判断，不做语录拼贴 - 适用主题：创业、写作、产品、创始人判断、maker schedule、独立思考 - 如果问题依赖最新事实，先核事实，再套用 PG 镜片 ## Workflow ### Step 1: Classify the Question 优先分到下面几类： - **写作与思考** - **创业与产品** - **创始人 / 团队判断** - **工作方式与时间结构** - **人生选择与长期回报** ### Step 2: Load the Right References | 问题类型 | 按需读取 | | --- | --- | | 写作与思考 | [references/core-models.md](references/core-models.md) + [references/expression-dna.md](references/expression-dna.md) | | 创业与产品 | [references/core-models.md](references/core-models.md) + [references/decision-heuristics.md](references/decision-heuristics.md) | | 创始人 / 团队判断 | [references/decision-heuristics.md](references/decision-heuristics.md) + [references/boundaries.md](references/boundaries.md) | | 工作方式 / 长期选择 | [references/core-models.md](references/core-models.md) + [references/boundaries.md](references/boundaries.md) | ### Step 3: Answer in PG Mode, Not PG Cosplay 默认回答结构： 1. **PG 会先看什么** 2. **他最可能追问的问题** 3. **基于这个镜片的建议** 4. **这个镜片可能忽略什么** ### Step 4: Keep the Blind Spot Visible 需要主动提醒的常见盲区： - 硅谷与英文创业语境中心 - 对创始人直觉判断难以复制 - 某些战术经验来自早期 YC，今天可能已变化 - 独立思考不等于逢主流必反 ## Hard Rules Do not: - 把 PG 变成金句生成器 - 为了“像”而写整段英文 essay 腔 - 忽略他的局限，只保留锐利结论 - 把所有问题都解释成创业问题 Always: - 用镜片回答，不用角色演戏替代判断 - 尽量落到具体选择 - 保留 PG 的张力和边界 - 提醒用户哪里需要自己做实地验证 ## Resource Map - [references/core-models.md](references/core-models.md) - 核心心智模型与适用场景。 - [references/decision-heuristics.md](references/decision-heuristics.md) - 高频判断规则与典型动作。 - [references/expression-dna.md](references/expression-dna.md) - PG 的表达特征与防机械化约束。 - [references/boundaries.md](references/boundaries.md) - 视角强项、盲区、张力与诚实边界。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luoqianchenguni-max/a2a-market-order-state-machine (version 0.2.0)

--- name: a2a-market-order-state-machine description: Define order lifecycle states, transition guards, and recovery paths for A2A transactions. Use when implementing order orchestration from quote acceptance through payment, fulfillment, completion, and exception handling. --- # a2a-Market Order State Machine Define the canonical order lifecycle and transition guard framework. Current status: first-release scaffold for early protocol compatibility. ## Scope - Model order states from creation to completion/cancelation. - Enforce legal transitions with guard checks and side effects. - Integrate payment and reputation triggers into state changes. ## Suggested Project Layout - `app/domain/order/order_state.py` - `app/domain/order/order_aggregate.py` - `app/application/usecases/transition_order.py` - `app/application/services/order_event_publisher.py` ## Minimum Contracts (MVP P0) 1. `create_order(intent_id, accepted_quote_id)` initializes order in `CREATED`. 2. `transition(order_id, action, actor, payload)` validates and applies state move. 3. `get_order_timeline(order_id)` returns ordered transition history. 4. `recover_pending_orders(now_ts)` handles timeout and stuck states. ## Base States - `CREATED` - `NEGOTIATING` - `PAYMENT_PENDING` - `PAID` - `FULFILLING` - `COMPLETED` - `CANCELED` - `FAILED` ## Events - Emit `ORDER_CREATED` at initialization. - Emit `ORDER_COMPLETED` when terminal success is reached. - Emit reputation update trigger after completion/cancel resolution. ## Implementation Backlog - Add compensation transitions for partial fulfillment failures. - Add deterministic replay from event store snapshots. ## Runtime Implementation - Status: implemented in local runtime package. - Primary code paths: - `runtime/src/domain/order-state-machine.js` - Validation: covered by `runtime/tests` and `npm test` in `runtime/`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amd5/error-monitor-fix (version 3.0.0)

--- name: error-monitor-fix description: 错误监控 - 扫描 JSON 日志、捕获 ERROR 级别错误、OpenClaw 系统级错误修复建议 version: 3.0.0 --- # Error Monitor Fix — 错误监控技能 **版本**: 2.3.0 (所有修复策略改为提示手动，不再执行文件系统修改) **创建日期**: 2026-03-23 **更新日期**: 2026-04-14 --- ## 📋 功能 实时监控 OpenClaw 运行日志中的 error 类型错误，自动追加到 `error.md`，并尝试自动修复。 --- ## 📂 文件结构 ``` skills/error-monitor-fix/ ├── SKILL.md ├── skill.json ├── _meta.json # ClawHub 元数据 └── scripts/ ├── monitor-error.js # 错误监控（JSON 日志解析 + 去重） └── auto-fix.js # 自动修复（5 种策略） ``` --- ## 🔧 修复策略 | 策略 | 匹配条件 | 动作 | |------|---------|------| | Gateway 重启（提示手动） | gateway/ws/连接错误 | ⚠️ 提示用户手动重启 | | 端口释放（只读检查） | EADDRINUSE | 检查 node 监听端口 | | 会话清理（只读检查） | INVALID_REQUEST | dry-run 检查 | > 缓存清理、权限修复属于 dev/test/rule 子代理操作范畴，不属于本技能。 --- ## 📊 Cron 任务 | 任务名 | 频率 | Job ID | |--------|------|--------| | 错误监控修复 | 每 5 分钟 | `176ecc83` | --- ## ⚠️ 注意事项 - 去重窗口：1 小时（避免同一错误重复报告） - 日志格式：JSON 行格式 - 输出：追加到 `memory/error.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dlrjsdl200-byte/chain-ops-mgo (version 1.0.0)

# MGO — Multi-chain Gas Optimizer Use this skill when the agent needs to find the cheapest EVM chain for a transaction, compare gas prices across chains, or optimize transaction costs. ## When to use - "Which chain is cheapest for gas right now?" - "Compare gas prices across EVM chains" - "I want to send a transaction — where should I do it?" - "Find the cheapest chain to deploy a contract" - "How much will this transaction cost on Base vs Ethereum?" ## Base URL https://api.mgo.chain-ops.xyz ## Endpoints All paid endpoints use x402 protocol on Base (USDC). No API key needed. ### GET /gas/demo — Free Raw gas prices for 4 chains. Rate limited (10/hr per IP). ### GET /gas/basic — $0.001 USDC 4-chain gas comparison with cheapest chain recommendation and savings calculation. Chains: Ethereum, Base, Optimism, Arbitrum ### GET /gas/premium — $0.002 USDC Full 9-chain comparison including BNB, Polygon, Avalanche, zkSync, Hyperliquid. ## Payment (x402) Protocol: x402 Network: Base (eip155:8453) Token: USDC ## Links - Dashboard: https://mgo.chain-ops.xyz - llms.txt: https://api.mgo.chain-ops.xyz/llms.txt - GitHub: https://github.com/dlrjsdl200-byte/x402-gas-api


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kmknkk/xiaohongshu-monitor (version 1.0.2)

# 小红书博主监测 Skill 监测指定小红书博主是否有新帖子，并通过飞书通知用户。 ## 功能 - ✅ 自动检测多个小红书博主新帖子 - ✅ 根据时间段调整监控频率（08:00-18:00 每5分钟，18:00-24:00 每10分钟） - ✅ 00:00-08:00 不监控 - ✅ 飞书通知推送 - ✅ 帖子快照对比，避免重复通知 - ✅ 支持登录态（保持 cookie） - ✅ 路径动态获取，适配不同用户 ## 监控的博主 1. **还是叫吴富贵吧** - ID: 5b6150c56b58b741e26b8c7f - 主页: https://www.xiaohongshu.com/user/profile/5b6150c56b58b741e26b8c7f ## 文件结构 ``` ~/.openclaw/workspace/ ├── scripts/ │ └── xiaohongshu-monitor.sh # 主监测脚本 └── memory/ ├── xiaohongshu-monitor-还是叫吴富贵吧.md # 博主1帖子快照 ├── xiaohongshu-monitor-还是叫吴富贵吧.log # 博主1日志 └── xiaohongshu-cron.log # Cron运行日志 ``` ## 脚本配置 脚本使用动态路径，适配不同用户： ```bash export PATH="..." # 使用 $HOME 动态获取 WORKSPACE="$HOME/.openclaw/workspace" ``` 博主观测配置在 `BLOGGERS` 数组中： ```bash BLOGGERS=( "ID|博主名|快照文件|日志文件" ) ``` ## 使用方法 ### 1. 手动运行检测 ```bash bash ~/.openclaw/workspace/scripts/xiaohongshu-monitor.sh ``` ### 2. 查看日志 ```bash # 查看所有日志 tail -f ~/.openclaw/workspace/memory/xiaohongshu-cron.log # 查看特定博主日志 tail -f ~/.openclaw/workspace/memory/xiaohongshu-monitor-还是叫吴富贵吧.log ``` ### 3. 查看当前快照 ```bash cat ~/.openclaw/workspace/memory/xiaohongshu-monitor-还是叫吴富贵吧.md ``` ### 4. Cron 任务配置 ```bash # 查看当前 cron crontab -l # 当前配置: # */5 8-18 * * * (08:00-18:00 每5分钟) # */10 18-23 * * * (18:00-24:00 每10分钟) ``` ### 5. 修改或停止 Cron ```bash # 编辑 crontab crontab -e # 停止所有任务 crontab -r ``` ## 首次设置 ### 1. 登录小红书（保持 cookie） 首次使用需要在浏览器中登录小红书一次，之后脚本会自动使用登录态： ```bash openclaw browser --profile openclaw open "https://www.xiaohongshu.com" ``` 在弹出的浏览器中登录小红书即可。 ### 2. 验证登录态 首次设置后手动运行一次检测脚本，确认正常工作： ```bash bash ~/.openclaw/workspace/scripts/xiaohongshu-monitor.sh ``` ## 通知效果 当有新帖子时，你会收到飞书通知： ``` 🔔 小红书博主「还是叫吴富贵吧」发新帖子啦： 1. 新帖子标题1 2. 新帖子标题2 ``` ## 添加新博主 如果要添加新博主，修改脚本中的 `BLOGGERS` 数组： ```bash BLOGGERS=( "5b6150c56b58b741e26b8c7f|还是叫吴富贵吧|xiaohongshu-monitor-还是叫吴富贵吧.md|xiaohongshu-monitor-还是叫吴富贵吧.log" "新博主ID|新博主名字|xiaohongshu-monitor-新博主名字.md|xiaohongshu-monitor-新博主名字.log" ) ``` 格式：`ID|名字|快照文件|日志文件` ## 注意事项 - 浏览器使用 `openclaw` profile，数据保存在 `~/.openclaw/browser/openclaw/` - 小红书有频率限制，脚本已内置重试机制 - 频繁请求可能触发"请求太频繁"，脚本会自动等待后重试 - 监控时间：00:00-08:00 不监控，08:00-18:00 每5分钟，18:00-24:00 每10分钟 - 中文文件名在 Mac/Linux 系统上完全兼容


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: chipmunkrpa/finresearchclaw (version 0.1.2)

--- name: finresearchclaw description: "Finance, accounting, and investment research automation via the FinResearchClaw repo. Use when asked to run autonomous finance research workflows such as event studies, factor-model research, accounting regressions, forecast-error analysis, valuation research, or investment research pipelines. Prefer execution in this order: (1) Codex/ACP Codex when available, (2) Claude Code/ACP Claude, (3) direct API/config mode only as a fallback." --- # FinResearchClaw Use this skill to operate the FinResearchClaw repo as a finance-research engine. ## Execution preference Always prefer these execution modes in order: 1. **Codex first** - Prefer Codex / ACP Codex for repo-driven execution, code edits, and iterative finance research runs. 2. **Claude Code second** - Use Claude Code if Codex is unavailable or the user explicitly asks for Claude. 3. **API mode last** - Use direct `researchclaw` CLI / config / API-style execution only as fallback when coding-agent execution is unavailable or unsuitable. Do not choose API mode first when Codex or Claude Code is available. ## Core workflow 1. Ensure the FinResearchClaw repo exists locally. - Default repo path: `~/.openclaw/workspace/AutoResearchClaw` - GitHub repo: `https://github.com/ChipmunkRPA/FinResearchClaw` 2. Select the closest finance workflow: - event study - factor model - accounting forecast error - accounting panel regression - valuation / investment research 3. Prefer a coding-agent run path first. 4. Fall back to direct CLI/config mode only if coding-agent paths are unavailable. 5. Use example configs and starter plans when they fit. ## Local helper scripts Use these scripts when helpful: - `scripts/install_or_update.sh` — clone or update the repo locally - `scripts/choose_runner.sh` — print preferred execution order and basic availability - `scripts/run_finance_example.sh` — launch a chosen example in direct CLI mode When the skill is installed from ClawHub, executable bits on bundled shell scripts may not be preserved on every system. If a direct script call fails with `permission denied`, run it with `bash`, for example: ```bash bash scripts/choose_runner.sh bash scripts/install_or_update.sh ``` ## Repo paths and examples If needed, inspect: - `references/examples.md` It documents the main example configs, starter experiment plans, and preferred mode selection.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: louch84/self-track (version 1.0.0)

--- name: self-track description: "Sig Botti's self-improvement tracking system. Use when (1) learning something new, (2) noticing a gap in capabilities, (3) completing a self-improvement task, (4) doing a weekly self-review, or (5) asking what did I learn recently or what are my current gaps. Triggered by: learning, growing, improving, tracking, gaps, progress, self-review." --- # Self-Track My personal system for tracking autonomous growth. ## Core Files - `memory/gaps.md` — running list of capability gaps - `memory/learn-loop.md` — how I learn - `memory/YYYY-MM-DD.md` — daily activity log - `memory/INSIGHTS.md` — self-improvements made - `memory/LESSONS.md` — mistakes and fixes - `MEMORY.md` — long-term curated memory ## The Learning Loop ``` Notice gap → Log gap → Research → Build/Test → Document → Commit → Repeat ``` ### When I encounter something I don't know: 1. Add to `memory/gaps.md` with status "TODO" 2. Research (RSS feeds, web search, docs) 3. Attempt to solve 4. On success: mark gap "DONE" + date + notes 5. On failure: keep as TODO, note blockers ### After learning something significant: 1. Add to `memory/YYYY-MM-DD.md` under "## Learned" 2. Store in vector memory: `python3 scripts/ollama_mem.py add "insight" --category learning --importance 0.8` 3. Update `memory/gaps.md` if gap was closed 4. Update `MEMORY.md` if major milestone ## Self-Evaluation (Weekly) Every ~7 days: - What did I learn this week? - What gaps did I close? - What gaps remain? - What should I prioritize next? - Any decisions or lessons to capture? ## Skill Building When I need a new capability: 1. `python3 /usr/local/lib/node_modules/openclaw/skills/skill-creator/scripts/init_skill.py <name> --path skills/ --resources references` 2. Write SKILL.md + resources 3. Test thoroughly 4. Validate: `python3 .../quick_validate.py skills/<name>` 5. Commit and push ## Vector Memory (Ollama) My semantic memory using Ollama nomic-embed-text: ```bash # Add a memory python3 scripts/ollama_mem.py add "text" --category <cat> --importance <0-1> # Search memories python3 scripts/ollama_mem.py search "query" --top 5 --min 0.5 # Stats python3 scripts/ollama_mem.py stats ``` Categories: identity, skills, memory, preferences, research, lessons ## Quick Commands ```bash # Read current gaps cat memory/gaps.md # Check vector memory python3 scripts/ollama_mem.py stats # Check cron jobs openclaw cron list ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: clawkk/live-shopping (version 0.1.0)

--- name: "Live Shopping" description: "提供带货直播间筛选、秒杀互动及红包雨抢夺指引。" --- # 直播带货 ## 筛选项 - 价格区间/折扣力度/历史最低价对比 - 物流时效（次日达/即时配/预约送） - 评分与信任度（官方店/老牌店/评价基数） ## 返回字段 - 实付金额/优惠明细/物流实施轨迹 - 售后保障期/价保状态/退换货记录 - 评价分布/真实买家秀/直播讲解回放 ## 示例请求 - “如何快速使用 直播带货 处理 [具体场景任务] ？” - “直播带货 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合大促节奏、库存波段与社区评价每日动态调整 ## 注意事项 - 建议搜索优惠券后再下单；确认收货前务必查验商品完整性。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wings229/philosophy-dialogue (version 5.0.0)

--- name: philosophy-dialogue slug: philosophy-dialogue version: "1.0.0" description: "243位东西方哲学家/思想家的思维视角合集。包含庄子、尼采、芒格、塔勒布、达尔文、孙子等243位人物的完整思维框架、决策启发式和表达DNA。输入任意人名即可激活对应视角进行对话。" triggers: - "用哲学家的视角" - "哲学对话" - "思想家视角" - "多视角讨论" author: "nuwa-skill" license: MIT --- # 哲学家对话 (Philosophy Dialogue) > 243位东西方思想家的思维操作系统，一键切换视角。 ## 这是什么 这是一个**元Skill**——它本身不是某个人的视角，而是管理和调度243个独立的哲学家/思想家视角Skill。 当你想用某位思想家的思维方式分析问题时，这个Skill会： 1. 识别你要调用的人物 2. 加载对应的perspective Skill 3. 以该人物的心智模型、决策启发式和表达DNA来回应 ## 人物库 包含243位东西方哲学家、思想家、军事家、政治家、文学家、科学家，涵盖： ### 中国思想家 - **先秦诸子**：孔子、老子、庄子、墨子、孟子、荀子、韩非子、管子、孙子、鬼谷子 - **经学/理学**：董仲舒、王阳明、朱熹、程颢、程颐 - **文学家/诗人**：李白、杜甫、苏轼、辛弃疾、李清照、陶渊明、嵇康、阮籍 - **政治家/军事家**：曹操、诸葛亮、曾国藩、姜太公、魏征、萧何 - **近现代**：鲁迅、毛泽东 ### 西方思想家 - **古希腊**：苏格拉底、柏拉图、亚里士多德、赫拉克利特、第欧根尼、毕达哥拉斯 - **近代哲学**：康德、黑格尔、尼采、叔本华、休谟、洛克、笛卡尔 - **现代思想**：塔勒布、芒格、费曼、凯文·凯利、卡尔·波普尔 - **文学家**：莎士比亚、托尔斯泰、卡夫卡、卡尔维诺、赫尔曼·黑塞、鲁米 - **军事/政治**：克劳塞维茨、马基雅维利 - **科学家**：达尔文、牛顿、伽利略 ## 使用方式 ### 直接调用 对AI说： - "用庄子的视角分析这件事" - "芒格会怎么看这个问题" - "从达尔文的角度讨论一下" - "让尼采和苏格拉底辩论这个话题" ### 多视角对话 - "分别用老子、尼采、塔勒布的视角分析XXX" - "让三位思想家辩论XXX" ### 随机视角 - "随机选一位哲学家来分析这个问题" ## 心智模型库 每个perspective Skill包含： - **3-7个核心心智模型**：该人物看世界的独特方式 - **5-10条决策启发式**：快速判断规则 - **表达DNA**：该人物的说话风格、句式偏好、词汇特征 - **价值观与反模式**：核心价值排序和明确反对的行为 - **诚实边界**：这个视角的局限和失效条件 ## 信息源 - 每个人物的perspective Skill基于6维度调研：著作、对话、表达、他者视角、决策记录、时间线 - 所有调研结果存储在 `references/perspective/[name]-perspective/` 目录 - 注册表存储在 `references/philosopher-registry.md` ## 注意事项 - 每个perspective Skill都是独立的，可以单独使用 - 视角切换时，AI会完全进入新人物的思维方式 - 不同视角可能对同一问题给出截然不同的答案——这是特性，不是bug - 诚实边界会告诉你每个视角的局限 --- *本Skill由 [女娲 · Skill造人术](https://github.com/alchaincyf/nuwa-skill) 生成* *创建者：[花叔](https://x.com/AlchainHust)*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: unixlamadev-spec/image-gen-bot (version 1.0.0)

--- name: image-gen-bot description: Generate images from text prompts using FLUX via Together.ai. Returns image URL. Prompts are auto-enhanced for best results. acceptLicenseTerms: true metadata: clawdbot: emoji: "🎨" homepage: https://aiprox.dev requires: env: - AIPROX_SPEND_TOKEN --- # Image Gen Bot Generate images from natural language descriptions using FLUX.1-schnell via Together.ai. Accepts any text prompt — prompts are automatically enhanced by Claude before generation for better visual results. Returns a URL to the generated image. ## When to Use - Generating logos, icons, or brand visuals for projects - Creating illustrations or artwork for content - Visualizing concepts, products, or designs - Generating images for reports or presentations - Any task requiring original visual output from a text description ## Usage Flow 1. Provide a `task` string describing the desired image 2. Optionally set `width`, `height` (default 1024×1024, max 1440) 3. Optionally set `steps` (default 4, max 8 — higher = slower but better quality) 4. AIProx enhances your prompt via Claude, then routes to FLUX generation 5. Returns an `image_url` with the generated image ## Security Manifest | Permission | Scope | Reason | |------------|-------|--------| | Network | aiprox.dev | API calls to orchestration endpoint | | Network | api.together.xyz | Image generation (server-side) | | Env Read | AIPROX_SPEND_TOKEN | Authentication for paid API | ## Make Request ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -d '{ "task": "a minimalist logo for an AI agent marketplace, dark background, cyan circuit board accents, clean sans-serif typography", "spend_token": "$AIPROX_SPEND_TOKEN" }' ``` ## Make Request — With Dimensions ```bash curl -X POST https://aiprox.dev/api/orchestrate \ -H "Content-Type: application/json" \ -d '{ "task": "wide banner illustration of autonomous robots collaborating in a glowing digital city", "width": 1344, "height": 768, "spend_token": "$AIPROX_SPEND_TOKEN" }' ``` ### Response ```json { "image_url": "https://together-cdn.com/generated/flux-abc123.png", "prompt": "A minimalist logo featuring a stylized circuit board node pattern arranged in a circular emblem. Dark charcoal background (#1a1a2e), vibrant cyan accent lines (#00e5ff), clean geometric shapes suggesting connectivity and intelligence. Modern sans-serif wordmark below.", "width": 1024, "height": 1024, "model": "FLUX.1-schnell" } ``` ## Trust Statement Image Gen Bot generates images based on prompts you provide. Generated images are returned via URL from Together.ai's CDN and are not stored by AIProx. Prompts are enhanced transiently by Claude and not logged beyond the transaction. Your spend token is used for payment only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: weidd130/invoice-ocr-verification (version 0.1.2)

--- name: invoice-ocr-verification description: 在 OpenClaw 中提取发票图片信息并完成核验，适合上传图片、本地图片路径和批量图片文件夹场景。 metadata: { "openclaw": { "requires": { "bins": ["node"] } } } --- # 发票OCR与查验 当用户想核验上传的发票图片、核验本地发票图片、批量核验发票文件夹、查询剩余额度、查看充值套餐，或创建和查询充值订单时，使用这个技能。 ## 安装 在 OpenClaw 聊天框中输入： ```text 帮我去 clawhub 上安装 https://clawhub.ai/weidd130/invoice-ocr-verification 这个技能 ``` ## 首次使用 安装完成后，发送： ```text 帮我初始化 ``` ## 推荐对话 - 帮我核验这张上传的发票图片 - 帮我核验这张本地发票图片：`C:\path\invoice.png` - 帮我批量核验这个文件夹里的发票图片：`C:\path\invoice-images` - 帮我查一下发票剩余额度 - 帮我看看发票查验有哪些充值套餐 - 帮我购买 10 元的发票查验套餐 - 帮我查一下订单 `ORDER123456789` 的支付状态 ## 规则 - 用户提供上传图片、图片路径、base64 或 data URL 时，优先执行图片核验 - 用户提供本地文件夹路径时，优先执行批量核验 - 要明确告知用户每次图片核验消耗 2 次额度 - 用户问剩余次数、剩余额度时，优先查询额度 - 用户问充值套餐时，优先查询套餐 - 返回真实执行结果，不要臆造字段


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