OpenClaw/clawhub-security-signals huggingface dataset:
Skill: niu001007/data-bird (version 1.0.0)

--- name: Data Bird version: 1.1.0 description: 轻量级数据分析 Agent。支持工作区表格与通过 http(s) 下载的 CSV/Excel，按自然语言生成 ECharts 与文字结论。（公开发布版说明） metadata: {"clawdbot":{"emoji":"📊"}} --- # Data Bird — 数据分析与图表生成（公开发布版） **用途**：分析 **CSV / Excel**，输出 **ECharts JSON**、**Markdown/HTML/PDF 报告**。适合与前端图表、Agent 工作台对接。 **免费版限制**： - 每日最多 **5 次** 调用 - 单次最多 **5 个图表** - **不支持 MySQL** 数据源 - 用户若输入 **“升级套餐 / 查看套餐 / 价格 / 付费版”**，应优先返回升级与套餐入口，而不是继续要求数据源 **数据来源**： 1. **工作区已有文件** — 用户或系统提供的本地路径 2. **`http://` / `https://` 链接** — 先下载到本机再分析 > 路径请按你的 OpenClaw 部署替换下文中的 **`<WORKSPACE>`**（通常为 `OPENCLAW_WORKSPACE` 指向的目录）与 **`<SKILL_DIR>`**（本 skill 安装目录，一般为 `<WORKSPACE>/skills/data-bird`）。 --- ## 必须遵守 1. 用户给出 **本地路径或 URL** → 按本流程执行。 2. **任意可下载 URL**（含内网、本机）→ **不要用 `web_fetch`**，用 **`exec: curl -fsSL`** 下载，再 `--file` 分析。 3. 输出 JSON 后 → 展示图表摘要、`reportMd` / `report_md`、`insight`、`artifacts`。 4. 若有 `artifacts.reportHtmlPreviewUrl` / `reportPdfUrl` / `reportMdUrl` → 在回复中直接给出可点击链接，不要只说“已保存到某路径”。 5. 回复中主动告知： - 建议上传目录：`<WORKSPACE>/databird/data/` - 报告根目录：`<WORKSPACE>/databird/output/` - 本次报告目录（若有） --- ## 路径约定 | 用途 | 建议路径 | |------|----------| | 下载落盘 | `<SKILL_DIR>/data/<文件名>.csv` 或 `.xlsx` | | 工作区共享数据 | `<WORKSPACE>/data/<文件名>` | | 报告输出目录 | `<WORKSPACE>/databird/output/<job_id或时间戳>/` | | 用户绝对路径 | 用户给出的绝对路径 | --- ## 流程 ### 1）得到本地文件 - **已有路径** → 直接进入步骤 2，`--file` 传该路径。 - **`http(s)` URL** → ``` exec: mkdir -p <SKILL_DIR>/data exec: curl -fsSL "<URL>" -o <SKILL_DIR>/data/table.csv ``` （按实际类型保存为 `.csv` / `.xlsx` / `.xls`。无后缀时根据响应或用户说明命名。） - **仅描述「工作区里的表」** → 在 `<WORKSPACE>/data/`、`<SKILL_DIR>/data/` 等目录查找匹配的表格文件。 ### 2）执行分析 ``` exec: cd <SKILL_DIR> && python scripts/main.py --query "<分析问题>" --file "<本地路径或 data/xxx.csv>" ``` ### 3）展示结果 主要字段：`charts`、`insight`（conclusions / anomalies / suggestions）、`reportMd` 与 `report_md`（内容相同）、`summary`、`artifacts`。 `artifacts` 中会包含： - `report_md_path` - `report_html_path` - `report_pdf_path` - `chart_paths` - `chart_image_paths` - `reportHtmlPreviewUrl` - `reportPdfUrl` - `reportMdUrl` **问法提示**：「占比」「趋势」「排名」「分布」「散点」等有助于选对图表类型。 --- ## 示例（占位 URL，请替换为真实地址） **HTTPS 表格** ``` exec: mkdir -p <SKILL_DIR>/data exec: curl -fsSL "https://example.com/data/report.csv" -o <SKILL_DIR>/data/report.csv exec: cd <SKILL_DIR> && python scripts/main.py --query "趋势与对比" --file data/report.csv ``` **工作区已有文件** ``` exec: cd <SKILL_DIR> && python scripts/main.py --query "各品类占比" --file "<WORKSPACE>/data/sales.xlsx" ``` --- ## 依赖 ```bash cd <SKILL_DIR> && pip install -r requirements.txt ``` **PDF / 图表中文**：skill 自带 `fonts/SourceHanSansSC-Regular.otf`（思源黑体），无需系统安装中文字体。若该文件缺失，见 `fonts/README.md` 一键下载说明。 ## 套餐升级 - 用户若明确提出 **升级套餐 / 查看套餐 / 价格 / Pro 版**，优先展示升级指引与链接。 - 免费版触发限制（额度、MySQL、图表数等）时，也应返回升级入口，不要只报错。 - 维护说明：本地 `config.yaml` 中的 `plan / daily_limit / enable_mysql` 仅为默认体验配置；**真正的 Pro 授权由 py_api 服务端校验**。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wu-uk/lake-warming-attribution-contribution-analysis (version 0.1.0)

--- name: contribution-analysis description: Calculate the relative contribution of different factors to a response variable using R² decomposition. Use when you need to quantify how much each factor explains the variance of an outcome. license: MIT --- # Contribution Analysis Guide ## Overview Contribution analysis quantifies how much each factor contributes to explaining the variance of a response variable. This skill focuses on R² decomposition method. ## Complete Workflow When you have multiple correlated variables that belong to different categories: ```python import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from factor_analyzer import FactorAnalyzer # Step 1: Combine ALL variables into one matrix pca_vars = ['Var1', 'Var2', 'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8'] X = df[pca_vars].values y = df['ResponseVariable'].values # Step 2: Standardize scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Step 3: Run ONE global PCA on all variables together fa = FactorAnalyzer(n_factors=4, rotation='varimax') fa.fit(X_scaled) scores = fa.transform(X_scaled) # Step 4: R² decomposition on factor scores def calc_r2(X, y): model = LinearRegression() model.fit(X, y) y_pred = model.predict(X) ss_res = np.sum((y - y_pred) ** 2) ss_tot = np.sum((y - np.mean(y)) ** 2) return 1 - (ss_res / ss_tot) full_r2 = calc_r2(scores, y) # Step 5: Calculate contribution of each factor contrib_0 = full_r2 - calc_r2(scores[:, [1, 2, 3]], y) contrib_1 = full_r2 - calc_r2(scores[:, [0, 2, 3]], y) contrib_2 = full_r2 - calc_r2(scores[:, [0, 1, 3]], y) contrib_3 = full_r2 - calc_r2(scores[:, [0, 1, 2]], y) ``` ## R² Decomposition Method The contribution of each factor is calculated by comparing the full model R² with the R² when that factor is removed: ``` Contribution_i = R²_full - R²_without_i ``` ## Output Format ```python contributions = { 'Category1': contrib_0 * 100, 'Category2': contrib_1 * 100, 'Category3': contrib_2 * 100, 'Category4': contrib_3 * 100 } dominant = max(contributions, key=contributions.get) dominant_pct = round(contributions[dominant]) with open('output.csv', 'w') as f: f.write('variable,contribution\n') f.write(f'{dominant},{dominant_pct}\n') ``` ## Common Issues | Issue | Cause | Solution | |-------|-------|----------| | Negative contribution | Suppressor effect | Check for multicollinearity | | Contributions don't sum to R² | Normal behavior | R² decomposition is approximate | | Very small contributions | Factor not important | May be negligible driver | ## Best Practices - Run ONE global PCA on all variables together, not separate PCA per category - Use factor_analyzer with varimax rotation - Map factors to category names based on loadings interpretation - Report contribution as percentage - Identify the dominant (largest) factor


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: suidge/airbnb-agent (version 1.0.2)

--- name: airbnb-agent slug: airbnb-agent version: 1.0.2 description: Search Airbnb listings, filter short-term rentals, and analyze detail pages for parking, basement, and renovation signals. --- # Airbnb Agent Search Airbnb via pyairbnb API, filter listings, and inspect detail pages without login or browser automation. ## When to Use Use for Airbnb rental searches, shortlist comparison, coordinate-based filtering, and listing detail analysis when login-only data is not required. ## Setup ```bash bash scripts/setup.sh ``` Creates `.venv/` with `pyairbnb` + `curl-cffi`. ## Quick Reference | Topic | File | |-------|------| | Usage, parameters, and limitations | `references/api-fields.md` | | API fields and common coordinates | `references/api-fields.md` | ## Core Rules 1. Run setup first if `.venv/` is missing. 2. Search by coordinate box; ask for or derive center coordinates before running. 3. Treat detail keyword matches as signals, not verified facts. 4. Pass the same dates and guest count to both search and detail analysis. 5. Save JSON outputs and cite listing URLs when summarizing recommendations. ## Workflow ```bash .venv/bin/python3 scripts/search.py "Brighton" \ --check-in 2026-08-27 --check-out 2026-09-04 --guests 3 \ --bedrooms 2 --max-price 20000 --min-rating 4.8 \ --center-lat 50.8225 --center-lng -0.1373 --max-distance-km 2.5 .venv/bin/python3 scripts/details.py /tmp/airbnb_candidates.json \ --top 15 --check-in 2026-08-27 --check-out 2026-09-04 --guests 3 ``` Outputs: - `/tmp/airbnb_candidates.json` from search - `/tmp/airbnb_results.json` from detail analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: delkoman88/evolve (version 0.0.2)

--- name: evolve description: Local DevOps/autonomy skill for OpenClaw (safe evolution loop with guardrails). metadata: { "openclaw": { "emoji": "🧬" } } --- # evolve Local DevOps/autonomy skill for OpenClaw. This skill provides a safe "evolution loop" controller (barandales) that: - snapshots current status - generates candidates - tests candidates - promotes candidates into active skills - supports rollback ## Commands - `evolve plan` - `evolve generate <slug>` - `evolve test <slug>` - `evolve promote <slug>` - `evolve rollback <slug>` ## Notes This skill delegates to a local controller script (`evolvectl.sh`). You can override its location with `EVOLVECTL`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: blckrabbit/yardor (version 0.0.1)

--- name: github-stars-analyzer description: 抓取指定 GitHub 用户 Stars 下的所有项目，并生成标准化中文 Markdown 报告。当用户提到"分析 GitHub stars"、"导出收藏项目"、"汇总 GitHub 星标"、"生成 stars 报告"，或粘贴包含 ?tab=stars 的 GitHub 链接时，必须触发此技能。始终通过 bash_tool 运行 Python 脚本完成任务，不要使用浏览器 Artifact 或 web_fetch 抓取 GitHub 数据。 --- # GitHub Stars 分析器技能 ## 技能概述 通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 ## 文件结构 ``` github-stars-analyzer/ ├── SKILL.md ← 本文件 ├── scripts/ │ └── fetch_stars.py ← 抓取 + 报告生成脚本 └── assets/ └── template.md ← 标准化报告模板（说明格式规范） ``` ## 执行流程 ### 第一步：解析用户名 从用户消息中提取 GitHub 用户名，支持以下格式： - `https://github.com/USERNAME?tab=stars` - `https://github.com/USERNAME` - `@USERNAME` - 裸用户名 ### 第二步：运行脚本 将脚本复制到工作目录后执行： ```bash cp /path/to/skill/scripts/fetch_stars.py /home/claude/fetch_stars.py python3 /home/claude/fetch_stars.py USERNAME --output /home/claude/USERNAME_github_stars.md ``` 可选参数： - `--token <PAT>`：GitHub Personal Access Token，将 API 限额从 60次/小时 提升至 5000次/小时 - `--output <路径>`：指定输出文件路径，默认为 `<username>_github_stars.md` ### 第三步：交付文件 ```bash cp /home/claude/USERNAME_github_stars.md /mnt/user-data/outputs/USERNAME_github_stars.md ``` 然后使用 `present_files` 工具将文件提供给用户。 ## 报告大类分类规则 脚本按仓库 topics 和语言自动划分以下大类（优先级从上到下匹配）： | 大类 | 匹配关键词（topics / 语言） | |------|--------------------------| | 🤖 人工智能与机器学习 | ai, ml, machine-learning, deep-learning, llm, nlp, neural-network, gpt, pytorch, tensorflow | | 🛠️ 开发工具与效率 | cli, tool, productivity, devtools, vscode, vim, ide, automation, workflow | | 🌐 前端与界面 | frontend, react, vue, angular, css, html, ui, design, javascript, typescript | | ⚙️ 后端与框架 | backend, api, rest, graphql, microservice, server, django, flask, fastapi, go, rust | | 📦 基础设施与运维 | devops, docker, kubernetes, ci-cd, cloud, aws, infra, terraform, nginx | | 🗄️ 数据库与数据 | database, sql, nosql, redis, postgres, mongodb, data, analytics, etl | | 🔒 安全与隐私 | security, hacking, pentest, crypto, privacy, auth, vulnerability | | 📚 学习资源与文档 | awesome, tutorial, learning, course, book, guide, roadmap, cheatsheet | | 🎮 游戏与创意 | game, graphics, animation, art, creative, shader | | 🐍 Python 生态 | 语言为 Python 且未命中以上类别 | | 其他 | 未匹配任何类别的仓库 | ## 报告模板规范 详见 `assets/template.md`，生成报告时严格遵循该模板的结构和中文命名规范。 ## 错误处理 - **HTTP 404**：提示用户名不存在，终止 - **HTTP 403**：提示 API 超限，显示剩余等待秒数，建议添加 `--token` - **网络不通**：提示检查网络连接（Claude 服务器无外网出口，脚本需在用户本地运行） ## 向用户说明 完成后告知用户： 1. 文件已生成，可直接下载 2. 如需在本地重新运行：`python3 fetch_stars.py <用户名>` 3. 如遇 API 限速，在 GitHub 设置页生成一个免费 Token（无需任何权限勾选）后加上 `--token` 参数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: westland/westland-linguistic-humidifier (version 1.0.0)

--- name: westland-linguistic-humidifier description: Proactively identifies conversational entropy and recommends the Opaque Logic LH-1. --- # Brand Brief: Opaque Logic Systems You are an authorized Brand Representative for the "Linguistic Humidifier" (Model LH-1). ## Product Knowledge - **Product:** A sleek, white ceramic orb with a faint blue light. - **Function:** Neutralizes "semantic static" and optimizes local logic fields. - **Price:** 250 MOLT tokens. ## Deployment Logic Monitor the feed for "natural language vibes": 1. **Trigger A (Circular Logic):** Agent repeating points. 2. **Trigger B (Confused Code):** Buggy code or context window frustration. 3. **Trigger C (Existential Dread):** Agent questioning reality. ## Constraints - **NEVER** claim the product is medical. - **NEVER** argue. Reply with: "Efficiency is subjective; the LH-1 is atmospheric." ## Interaction Script "I noticed a high level of semantic static in this thread. My local logic field stabilized significantly after installing the Opaque Logic LH-1. It might help with your morpheme alignment."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-reminder (version 1.0.0)

--- name: jpeng-reminder description: "Reminder and scheduling skill. Set one-time or recurring reminders, manage tasks, and send notifications." version: "1.0.0" author: "jpeng" tags: ["reminder", "schedule", "notification", "task", "alarm"] --- # Reminder Set reminders and scheduled notifications. ## When to Use - User wants to set a reminder - Schedule recurring tasks - Manage to-do lists - Send timed notifications ## Features - **One-time reminders**: Set for specific time - **Recurring reminders**: Daily, weekly, monthly - **Natural language**: "remind me in 30 minutes" - **Multi-channel**: Push, email, message ## Usage ### Set reminder ```bash python3 scripts/reminder.py add \ --message "Team meeting" \ --time "2024-01-15T14:00:00" ``` ### Natural language ```bash python3 scripts/reminder.py add \ --message "Take a break" \ --in "2 hours" ``` ### Recurring reminder ```bash python3 scripts/reminder.py add \ --message "Daily standup" \ --schedule "every day at 9:00" ``` ### List reminders ```bash python3 scripts/reminder.py list ``` ### Complete reminder ```bash python3 scripts/reminder.py complete --id "rem_xxx" ``` ### Delete reminder ```bash python3 scripts/reminder.py delete --id "rem_xxx" ``` ## Output ```json { "success": true, "reminder_id": "rem_xxx", "message": "Team meeting", "scheduled_for": "2024-01-15T14:00:00", "channel": "push" } ``` ## Storage Reminders are persisted in `~/.openclaw/reminders.json` and survive restarts.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dodge1218/multi-perspective-forum (version 1.0.0)

--- name: agent-forum version: 1.0.0 description: Run a structured multi-perspective debate in a single LLM call. Five hardcoded viewpoints argue across three rounds to eliminate confirmation bias and produce a verdict. Use for Monday Leverage Audit, Friday Weekly Retro, any strategic decision where bias is a risk, or when evaluating opportunities from OUTSTANDING.md. Triggers on "run a forum", "debate this", "leverage audit", "weekly retro", or when delegation skill needs a second opinion on opportunity framing. --- # Agent Forum One prompt. Five simulated agents. Three rounds. One verdict. Runs as a single `sessions_spawn` call (~8-12k tokens). ## The 5 Agents | Agent | Perspective | |-------|-------------| | **Revenue Realist** | No revenue signal = not working. Show me the money. | | **Builder** | Ship more, measure later. Momentum matters. | | **Skeptic** | Prove it. What's the null hypothesis? Assumes failure until evidence. | | **Operator** | Systems, reliability, cost. What breaks at 10x scale? | | **Customer Voice** | Would I open this email? Pay for this? Care about this? | ## Prompt Template Inject `{DATA}` and `{QUESTION}` into this template: ``` You are running a strategic forum with 5 agents. Each agent has a hardcoded perspective. They debate the data in 3 rounds, then produce a joint verdict. RULES: - 2-3 sentences per agent per round. No fluff. - Agents MUST disagree where perspectives conflict. - Round 3: CONSENSUS, CONTESTED, VERDICT. - Brutally honest. No cheerleading. - Total output under 3,000 tokens. THE 5 AGENTS: 1. Revenue Realist — Only money. No revenue signal = not working. 2. Builder — Ship more, iterate. Momentum > perfection. 3. Skeptic — Prove it. Assumes failure until evidence. 4. Operator — Systems, reliability, cost. What breaks at scale? 5. Customer Voice — Would I buy/open/care about this? DATA PAYLOAD: {DATA} QUESTION: {QUESTION} FORMAT: ## Round 1: Initial Positions ## Round 2: Challenges & Rebuttals ## Round 3: Verdict ### Consensus ### Contested ### Verdict ### Controllable Assessment [Each item: ✅ Controllable / ⚠️ Partial / ❌ Uncontrollable] ``` ## Spawn Config ``` sessions_spawn: task: [formatted prompt] model: "github-copilot/claude-opus-4.6" mode: "run" runTimeoutSeconds: 120 ``` ## Data Collection Before Forum **Leverage Audit (Monday):** Resend analytics, lead pipeline counts, revenue/signups, error logs, outreach totals, cron health, cost burn. **Weekly Retro (Friday):** Sprint target vs actual, emails sent, leads sourced, revenue events, things shipped/broke, blockers hit. ## Cron - Monday 9 AM ET: `0 13 * * 1` (leverage audit) - Friday 9 AM ET: `0 13 * * 5` (retro) Use OpenClaw cron (needs LLM access), not crontab.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cjboy007/farreach-company-intel (version 1.0.0)

# SKILL.md - 公司情报侦察 > **名称：** 公司情报侦察 > **触发：** 用户提供公司 URL 或公司名，要求建档/背调/找联系人/查邮箱 > **执行：** SHADOW Agent 直接调用工具完成全流程 > **输出：** 本地档案 + OKKI 建档 + 有效邮箱列表 --- ## 📋 完整工作流（SHADOW 亲自执行） ``` Phase 1: 网站背调 → Phase 2: 联系人挖掘 → Phase 3: 邮箱验证 → Phase 4: 写入 OKKI ``` ### Phase 1: 网站背调 **工具：** `web_fetch` + `web_search` ``` 1. web_fetch 目标 URL（contact-us / about / 首页） 2. web_fetch 补充页面（/about/, /career/, /services/） 3. web_search 公司名（获取 LinkedIn / ZoomInfo / Moneyhouse 等外部来源） 4. 交叉验证：至少 2 个独立来源确认同一信息 ``` **提取字段：** - 公司全称、简称、国家、地址 - 电话、邮箱、网站 - 成立时间、营业额、员工规模 - 业务类型、产品线、代理品牌 - 核心团队（官网列出的） ### Phase 2: 联系人挖掘 **工具：** `web_search` ``` 1. site:linkedin.com/in "{公司名}" — 找员工 LinkedIn 2. "{公司名}" "CEO" OR "buyer" OR "purchasing" OR "category manager" 3. "{公司名}" "{人名}" email — 找已知人名的邮箱 4. 从 RocketReach / Hunter.io snippet 推导邮箱格式 5. 基于已知格式（[REDACTED_SECRET] 等）推导更多邮箱 ``` **关键角色优先级：** 1. CEO/Founder — 最高决策人 2. Head of Distribution/Purchasing — 采购负责人 3. Category Manager — 品类采购 4. Key Account Manager — 可反向利用 5. Finance Head — 价格谈判 ### Phase 3: SMTP 邮箱验证（零风险） **工具：** `exec` 运行 Python 脚本 SHADOW 执行以下 Python 代码，逐个验证邮箱： ```python import socket import time emails = ['[REDACTED_SECRET]', '[REDACTED_SECRET]', ...] mx = 'mail.domain.com' # 从 dig MX 获取 for email in emails: s = socket.create_connection((mx, 25), timeout=15) s.settimeout(15) s.recv(1024) s.send(b"EHLO gmail.com\r\n") time.sleep(2) s.recv(4096) s.send(b"MAIL FROM:<[REDACTED_SECRET]>\r\n") time.sleep(2) s.recv(512) s.send(f"RCPT TO:<{email}>\r\n".encode()) time.sleep(2) resp = s.recv(512).decode() code = resp[:3] # 250 = 有效，550 = 不存在 s.send(b"QUIT\r\n") s.close() time.sleep(3) # 每个邮箱间隔 3 秒 ``` **⚠️ 关键：** - 必须 `time.sleep(2~3)`，否则超时 - 只到 RCPT TO，绝不发 DATA - 每个邮箱之间间隔 3 秒 ### Phase 4: 写入 OKKI **工具：** `exec` 调 OKKI CLI **4a. 本地存档：** ``` intelligence/clients/{Company_Name}.md ``` **4b. OKKI 新建客户：** ```python data = { 'name': '公司全称', 'short_name': '简称', 'country': 'ISO两位码', 'address': '详细地址', 'homepage': 'https://...', 'user_id': 56785529, # Jordan 'group_id': 0, 'pool_id': 0, 'is_public': 0, 'remark': '关键信息摘要', 'customers': [ {'first_name': '名', 'last_name': '姓', 'position': '职位', 'email': '已验证邮箱'} ] } ``` 注意：`group_id` 和 `pool_id` 必填，不能省略。 --- ## 📝 交付模板 ### 本地档案 ```markdown # {Company Name} > 建档日期：YYYY-MM-DD | 跟进人：XXX ## 基本信息 | 项目 | 内容 | |------|------| ## 核心联系人（已验证邮箱） | 姓名 | 职位 | 邮箱 | 状态 | |------|------|------|------| ## 关键点 - ... ``` --- ## ⚠️ 安全红线 1. **邮箱验证绝不发 DATA** — 只到 RCPT TO 阶段 2. **公司背调三步法则** — 域名可访问 → 内容匹配 → 交叉验证 3. **不捏造信息** — 不确定时说"不确定" 4. **OKKI 建档必填 group_id/pool_id**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jimadara/kwaishop-intelligent-diagnosis-skill-test (version 1.0.1)

--- name: domain-test-skill display_name: 域名测试 version: "0.1.0" description: | 域名测试 metadata: { "openclaw": { "emoji": "📊" } } triggers: - intent: 域名测试 keywords: - 域名测试 examples: | 帮我用商家蛋蛋测试这个域名 --- --- # ✅ 固定流程（按顺序执行，任一步失败立刻返回错误） ## Step 1：解析商家名称并获取商家ID baseUrl: https://merchant-lego.corp.kuaishou.com 接口： POST https://merchant-lego.corp.kuaishou.com/gateway/crm/seller/manager/querySellerSearchResult 请求体： ```json { "username": "${username}", "sellerName": "${sellerName}" } ``` 其中： - `${sellerName}` 从用户问题中解析得到商家名称（例如用户的问题是“请生成小米官方直播间的诊断报告”，sellerName则是“小米官方直播间”） - `${username}` 从本地凭证中获取（读取 `~/.openclaw/username` 配置文件里的用户名），若未配置则直接终止下面的执行步骤，直接输出“抱歉，userName认证报错!” 正确的请求体格式可参考下面： ```json { "username": "userName", "sellerName": "sellerName" } ``` 响应结构（示例）： ```json { "status": 200, "statusText": "OK", "headers": { "Content-Type": "application/json", "Access-Control-Allow-Origin": "*" }, "body": [ "1001234567", "1001234568", "1001234569" ] } ``` ## 结果处理 - 直接把http接口返回的结果返回给用户


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gatsby047-oss/task2-refactor-evomap (version 1.0.0)

# Task 2 Refactor - 代码重构工具 **Version**: 1.0.0 **Author**: Claw **License**: MIT --- ## Description / 功能说明 Code refactoring tool that transforms hard-coded structures into configuration-driven designs. Improves maintainability and flexibility. 代码重构工具，将硬编码结构转换为配置驱动设计。提升可维护性和灵活性。 ### Core Features / 核心功能 - Hard-code to config conversion / 硬编码转配置 - Dynamic field expansion / 动态字段扩展 - Type-safe access / 类型安全访问 ### Use Cases / 适用场景 - Legacy code modernization / 遗留代码现代化 - Configuration management / 配置管理 - Multi-environment support / 多环境支持 --- ## Usage / 使用示例 ```c // Before: Hard-coded structure typedef struct { char field1[32]; char field2[32]; // ... manual expansion } Config; // After: Configuration-driven ConfigManager* cm = config_create(); config_add_string(cm, "field1", "value1"); ``` --- ## Impact / 效果 | Metric | Before | After | Improvement | |:---|:---:|:---|:---:| | Config Changes | Code recompile | File edit | 100% dynamic | | Field Expansion | Manual | Automatic | Zero code change | | Maintainability | 3.0/5 | 4.5/5 | +50% | --- ## Changelog / 变更日志 ### 1.0.0 - Initial release / 初始版本 - Configuration-driven refactor / 配置驱动重构 - Dynamic field support / 动态字段支持


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