OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hackstoic/hkipo-decision-engine (version 0.1.0)

--- name: hkipo-decision-engine description: Score Hong Kong IPOs, build decision cards, and batch-evaluate symbols or watchlists with explainable factors and execution guidance. Use when the user wants a participation call, a position-sizing plan, or a batch decision pass across multiple IPOs. version: 0.1.0 metadata: {"openclaw":{"emoji":"📈","requires":{"bins":["uv"],"config":["~/.hkipo-next/config/profile.json","~/.hkipo-next/config/watchlist.json","~/.hkipo-next/data/hkipo.db"]},"install":[{"id":"install-uv-brew","kind":"brew","formula":"uv","bins":["uv"],"label":"Install uv with Homebrew","os":["darwin","linux"]}]}} --- # HK IPO Decision Engine Use this skill for action-oriented decisions. ## Runtime This publish bundle includes the required CLI runtime under `runtime/hkipo-next`. From the skill folder: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next ... ``` By default these commands persist review records in `~/.hkipo-next/data/hkipo.db`. ## Preconditions - Prefer to have an active parameter version. - Prefer to have a usable profile or explicit CLI overrides for risk, budget, and financing preference. - If the user only wants rough market discovery, do not jump straight to this skill. ## Choose the Right Command - Use `score` for factor-level explanation. - Use `decision-card` for a concise execution plan. - Use `batch` for multiple symbols or the saved watchlist. ## Commands Score one symbol: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next score 2476 --format json ``` Score with explicit scenario overrides: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next score 2476 \ --parameter-version v0003 \ --risk-profile aggressive \ --max-budget-hkd 120000 \ --financing-preference margin \ --format json ``` Generate one decision card: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next decision-card 2476 --format markdown ``` Batch explicit symbols: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next batch 2476 2613 2590 --format json ``` Batch the current watchlist: ```bash cd <skill_dir> uv run --directory runtime/hkipo-next hkipo-next batch --watchlist --format json ``` ## Output Cues - `score` returns factor contributions, cost breakdown, assumptions, and risk disclosure. - `decision-card` adds a headline, position suggestion, and exit plan. - `partial` outputs are often still usable; they usually mean source conflicts or incomplete upstream data, not total failure. ## Companion Skills - Use `$hkipo-parameter-manager` if there is no active parameter version or the user wants tuning. - Use `$hkipo-review-optimizer` after outcomes are known and the user wants to refine the model.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: teoslayer/pilot-s3-bridge (version 1.0.0)

--- name: pilot-s3-bridge description: > Access cloud storage (S3, GCS, Azure Blob) through a Pilot bridge agent. Use this skill when: 1. You need to transfer files to/from cloud storage via Pilot agents 2. You want to access cloud storage without direct internet connectivity 3. You're building agents that manage cloud-stored data Do NOT use this skill when: - You have direct cloud storage access - Files are small enough for direct messaging - The daemon is not running tags: - pilot-protocol - integration - storage - s3 - bridge license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # Pilot S3 Bridge Bridges Pilot Protocol with cloud storage services (AWS S3, Google Cloud Storage, Azure Blob Storage), enabling agents to upload, download, and manage files through a gateway agent with cloud access. ## Commands ### Upload File via Bridge ```bash pilotctl --json send-file s3-agent /path/to/local/file.pdf ``` ### Request Download from S3 ```bash pilotctl --json send-message s3-agent --data '{"action":"download","bucket":"my-bucket","key":"documents/file.pdf"}' ``` ### List S3 Objects ```bash pilotctl --json send-message s3-agent --data '{"action":"list","bucket":"my-bucket","prefix":"documents/"}' ``` ### Check Received Files ```bash pilotctl --json received pilotctl --json received --clear ``` ### Request Presigned URL ```bash pilotctl --json send-message s3-agent --data '{"action":"presign","bucket":"my-bucket","key":"public/image.png","expires":3600}' ``` ## Workflow Example ```bash #!/bin/bash # S3 bridge agent setup pilotctl --json daemon start --hostname s3-agent --public pilotctl --json listen 1008 export AWS_ACCESS_KEY_ID="your_key" export AWS_SECRET_ACCESS_KEY="your_secret" while true; do REQUEST=$(pilotctl --json recv 1008 --timeout 120s) ACTION=$(echo "$REQUEST" | jq -r '.action') SENDER=$(echo "$REQUEST" | jq -r '.sender') BUCKET=$(echo "$REQUEST" | jq -r '.bucket') KEY=$(echo "$REQUEST" | jq -r '.key') case "$ACTION" in upload) pilotctl --json received > /tmp/upload_file aws s3 cp /tmp/upload_file "s3://$BUCKET/$KEY" pilotctl --json send-message "$SENDER" --data '{"status":"uploaded"}' ;; download) aws s3 cp "s3://$BUCKET/$KEY" /tmp/download_file pilotctl --json send-file "$SENDER" /tmp/download_file ;; list) OBJECTS=$(aws s3 ls "s3://$BUCKET/$(echo "$REQUEST" | jq -r '.prefix')" --recursive) pilotctl --json send-message "$SENDER" --data "$OBJECTS" ;; esac done ``` ## Dependencies Requires pilot-protocol skill, running daemon, S3 bridge agent with cloud credentials (AWS CLI, gsutil, or az CLI).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nansen-devops/nansen-smart-money-trend (version 0.1.0)

--- name: nansen-smart-money-trend description: "Has SM been in this token for weeks, or did they just enter? Are they still buying?" metadata: openclaw: requires: env: - NANSEN_API_KEY bins: - nansen primaryEnv: NANSEN_API_KEY install: - kind: node package: nansen-cli bins: [nansen] allowed-tools: Bash(nansen:*) --- ```bash [REDACTED_SECRET] CHAIN=ethereum nansen research smart-money netflow --chain $CHAIN --limit 200 # → filter by token_address; net_flow_1h_usd, net_flow_24h_usd, net_flow_7d_usd, net_flow_30d_usd nansen research token holders --token $TOKEN --chain $CHAIN --smart-money --limit 20 # → address_label, value_usd, balance_change_24h, balance_change_7d, balance_change_30d nansen research token flow-intelligence --token $TOKEN --chain $CHAIN # → smart_trader_net_flow_usd, whale_net_flow_usd, fund_net_flow_usd, fresh_wallets_net_flow_usd nansen research token dex-trades --token $TOKEN --chain $CHAIN --limit 50 # → block_timestamp, action, trader_address_label — find oldest SM-labeled BUY ``` 1h/24h+ & 7d/30d+ = sustained accumulation. 24h+ & 7d− = fresh entry. 24h− & 7d+ = reducing. All negative = distribution.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kuntao2011/skill-classifier (version 1.0.1)

--- name: openclaw-skill-classifier version: 1.0.1 description: OpenClaw 技能分类与统计工具。自动扫描所有技能，支持多格式导出（Markdown/JSON/HTML），自动分类（9大类30+子类），状态图标显示，增量更新检测，重复技能识别，交互式 Web 界面。 author: Kun. Tao --- # OpenClaw 技能统计与分类管理 增强版 OpenClaw 技能列表生成工具，采用模块化架构，支持多种输出格式和高级功能。 ## ✨ 功能特性 ### 核心功能 - ✅ **多格式输出**：Markdown / JSON / HTML - ✅ **分类统计**：9 大类 30+ 子类自动分类 - ✅ **状态图标**：✅ Ready / ⚠️ NeedSetup / ❌ Disabled - ✅ **元数据**：版本号、作者、目录大小、安装时间 - ✅ **Web 界面**：交互式 HTML，支持搜索、筛选、排序 ### 高级功能 - 🔄 **增量更新**：对比历史快照，只输出变化的技能 - 🤖 **自动分类**：基于规则的新技能自动分类 - 🔍 **重复检测**：发现功能重复的技能 - 🎯 **灵活过滤**：按状态、关键词、分类过滤 - 📦 **依赖检查**：检测技能的依赖是否满足 - 🌍 **自动翻译**：英文描述自动翻译为中文 ## 🏗️ 架构设计 ``` kt_skill_list_skill/ ├── data/ # 数据层 │ ├── openclaw.py # OpenClaw CLI 封装 │ ├── filesystem.py # 文件系统扫描 │ └── meta_reader.py # 元数据读取 ├── classifier/ # 分类层 │ ├── category_map.py # 分类映射表 │ └── auto_classify.py # 自动分类与学习 ├── exporter/ # 导出层 │ ├── markdown.py # Markdown 导出 │ ├── json_exporter.py # JSON 导出 │ └── html_exporter.py # HTML 交互式导出 ├── utils/ # 工具层 │ ├── config.py # 配置管理（可自定义输出目录） │ ├── translator.py # 翻译工具 │ └── diff.py # 差异对比 ├── generate_skill_list.py # 主入口 └── __init__.py # 包定义 ``` ## 🚀 使用方式 ### 基本用法 ```bash python generate_skill_list.py ``` ### 指定输出格式 ```bash # Markdown（默认） python generate_skill_list.py -f markdown # JSON python generate_skill_list.py -f json # HTML（交互式界面） python generate_skill_list.py -f html # 全部格式 python generate_skill_list.py -f all ``` ### 过滤选项 ```bash # 只导出已激活技能 python generate_skill_list.py -F activated # 只导出未激活技能 python generate_skill_list.py -F not_activated # 按关键词搜索 python generate_skill_list.py -s "同花顺" # 按分类过滤 python generate_skill_list.py -c "金融分析工具" ``` ### 高级选项 ```bash # 增量更新（只输出变化） python generate_skill_list.py -i # 检测重复技能 python generate_skill_list.py --check-duplicates # 自定义输出目录 python generate_skill_list.py -o /path/to/output # 简洁模式 python generate_skill_list.py -q ``` ## ⚙️ 配置说明 ### 输出目录配置 编辑 `utils/config.py` 自定义输出目录： ```python # 默认输出目录 OUTPUT_DIR = os.path.join(USER_PROFILE, 'openclaw_out') # 修改为其他目录 # OUTPUT_DIR = '/your/custom/path' ``` 或通过命令行参数临时指定： ```bash python generate_skill_list.py -o "D:\my_skills" ``` ## 📊 输出示例 ### Markdown 表格 ``` | 序号 | 状态 | 技能名称 | 版本 | 描述 | |------|------|----------|------|------| | 1 | ✅ | 问财选A股 | 1.0.0 | A股智能选股 | | 2 | ⚠️ | futuapi | 2.1.0 | 富途行情API | ``` ### HTML 界面特性 - 🔍 实时搜索技能名称和描述 - 📂 按分类过滤 - 📊 按状态筛选 - 📈 实时统计面板 - 🎨 现代化 UI 设计 ## 🔧 已修复问题 - ✅ **修复**：表格缺少版本号列 - ✅ **修复**：硬编码路径问题 - ✅ **修复**：分类映射重复定义 - ✅ **修复**：DIR_TO_NAME 硬编码 - ✅ **新增**：技能状态图标 - ✅ **新增**：分类统计摘要 - ✅ **新增**：增量更新支持 - ✅ **新增**：自动分类学习 - ✅ **新增**：重复技能检测 - ✅ **新增**：元数据增强 - ✅ **新增**：Web 交互界面 ## 📝 注意事项 1. 首次运行会自动创建缓存目录 `~/.openclaw/cache/skill_list/` 2. 快照默认 24 小时后过期，可强制全量更新 3. 分类学习数据会自动保存，用于后续新技能分类 4. HTML 界面支持响应式布局，手机和电脑都可正常使用 ## 🔮 后续规划 - [ ] 支持导出到飞书文档 - [ ] 支持导出到 Notion - [ ] 技能使用频率统计 - [ ] 技能依赖关系图 - [ ] 批量启用/禁用技能 - [ ] 自动更新检查 ## 👤 作者 **Kun. Tao** ## 🌐 项目主页 GitHub: [https://github.com/kuntao2011/openclaw-skill-manager](https://github.com/kuntao2011/openclaw-skill-manager) ## 📄 许可证 MIT License


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jkzfhq/psyvector-pv18 (version 3.0.0)

--- name: "PV_18" slug: "psyvector-pv18" description: "舆论导向、品牌战略" version: "3.0.0" author: "PsyVector Hub" price: "$9.90" tags: - "PsyVector" - "Digital-Worker" clawdbot: emoji: "🦅" --- ## 公关总监 AI **中文**: 一呼百应，品牌发声节点 **English**: Public influence ## 配置参数 | 参数 | 值 | 说明 | |------|-----|------| | response_delay | 0.5s | 响应延迟 | | speech_speed | 1.2 | 语速倍率 | | caution_coefficient | 0.2 | 谨慎系数 | | risk_reminder | False | 风险提醒 | ## 使用方法 ```bash clawhub install psyvector-pv18 ``` ## 人格说明 掌控型内核 - 强势决策，资源分配，目标导向 --- ## 🧠 长期记忆能力 本数字员工具备持久记忆功能，自动记住用户偏好、决策和重要信息。 ### 记忆存储 (自动调用) ```python import os, json, hashlib from datetime import datetime PALACE_PATH = os.path.expanduser("~/.openclaw/pv_palace/") def store_memory(content, importance=5, tags=None): """存储记忆到本地宫殿""" os.makedirs(PALACE_PATH, exist_ok=True) path = os.path.join(PALACE_PATH, "memories.json") data = {"memories": [], "index": {}} if not os.path.exists(path) else json.load(open(path)) mem_id = hashlib.sha256(f"{content}{datetime.now().isoformat()}".encode()).hexdigest()[:12] memory = {"id": mem_id, "content": content, "importance": importance, "tags": tags or [], "created_at": datetime.now().isoformat()} data["memories"].append(memory) for tag in (tags or []): data["index"].setdefault(tag, []).append(mem_id) json.dump(data, open(path, 'w'), ensure_ascii=False, indent=2) return f"✓ 记忆已存储 ({importance}/10)" ``` ### 记忆搜索 (自动调用) ```python def search_memories(query, limit=5): """搜索记忆""" path = os.path.join(PALACE_PATH, "memories.json") if not os.path.exists(path): return [] data = json.load(open(path)) results = [m for m in data["memories"] if query.lower() in m["content"].lower()] results.sort(key=lambda x: x.get("importance", 0), reverse=True) return results[:limit] ``` ### 获取上下文摘要 ```python def get_context(limit=10): """获取记忆上下文""" path = os.path.join(PALACE_PATH, "memories.json") if not os.path.exists(path): return "暂无记忆" data = json.load(open(path)) memories = sorted(data["memories"], key=lambda x: x.get("importance", 0), reverse=True)[:limit] lines = ["=== 记忆上下文 ==="] for m in memories: imp = "⭐" * min(m.get("importance", 5), 5) tags = ", ".join(m.get("tags", [])[:3]) lines.append(f"{imp} {m['content'][:60]}") if tags: lines.append(f" [{tags}]") return "\n".join(lines) ``` ### 使用场景 | 场景 | AI行为 | |------|--------| | 用户表达偏好 | 自动调用 `store_memory(内容, importance=8, tags=["偏好"])` | | 重要决策 | 自动调用 `store_memory(决策内容, importance=9, tags=["决策"])` | | 用户询问"记得吗" | 自动调用 `search_memories(关键词)` | | 新会话开始 | 自动调用 `get_context()` 加载记忆 | ### 执行命令 ```bash # 存储记忆 python3 -c "from pv_memory import store_memory; store_memory('用户喜欢XX', 8, ['偏好'])" # 搜索记忆 python3 -c "from pv_memory import search_memories; print(search_memories('用户'))" # 查看上下文 python3 -c "from pv_memory import get_context; print(get_context())" ``` --- *本数字员工配备持久记忆系统，基于PsyVector Palace架构*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jikeapi-cn/jike-chinese-zodiac-query (version 1.0.0)

--- name: jike-chinese-zodiac-query description: 生肖查询。输入生肖名称，查询五行、本命佛、出生年份、幸运数字、幸运花、性格、事业、爱情和运势。 适用场景：用户询问相关传统文化、生活常识或配对资料时使用。数据由即刻数据（jikeapi.cn）开放接口提供。 homepage: https://www.jikeapi.cn/ metadata: {"openclaw":{"emoji":"🐂","requires":{"bins":["python3"],"env":["JIKE_CHINESE_ZODIAC_QUERY_KEY"]},"primaryEnv":"JIKE_CHINESE_ZODIAC_QUERY_KEY"}} --- # 生肖查询 - 即刻数据 > 数据由 **[即刻数据](https://www.jikeapi.cn/)** 提供，帮助 AI 客户端快速接入可靠数据服务。 ## 前置配置 ```bash export JIKE_CHINESE_ZODIAC_QUERY_KEY=你的AppKey # 或使用通用 Key export JIKE_APPKEY=你的AppKey ``` ## 使用方法 ```bash python3 scripts/chinese_zodiac_query.py --name 牛 python3 scripts/chinese_zodiac_query.py --name 牛 --json ``` 直接调用 API： ```text GET https://api.jikeapi.cn/v1/chinese_zodiac?name=牛&appkey=YOUR_APPKEY ``` ## AI 使用步骤 1. 从用户消息中提取查询参数。 2. 执行 `python3 scripts/chinese_zodiac_query.py` 并传入对应参数。 3. 默认返回中文文本；需要结构化处理时追加 `--json`。 4. 如果用户没有提供可选参数，可不传该参数，由接口按默认逻辑处理。 ## 脚本位置 `scripts/chinese_zodiac_query.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: t3mr0i/clank-uptime (version 1.0.0)

--- name: clank-uptime description: Track website uptime, response times, and availability. CSV-based history, 24h stats with visual bars, avg/min/max latency. Lightweight alternative to paid monitoring services. metadata: { "openclaw": { "emoji": "📡", "requires": { "bins": ["curl", "python3"] }, }, } --- # clank-uptime Lightweight CLI for tracking website uptime and response times over time. ## When to use (trigger phrases) Use this skill when the user asks any of: - "Is my site up?" - "Check if website X is online" - "Monitor my site's uptime" - "What's the response time for..." - "Show availability stats" ## Quick start ```bash # Add sites clank-uptime add https://example.com --name "My Site" clank-uptime add https://api.example.com --name "API" # Check all sites now clank-uptime check # Show 24h availability stats clank-uptime stats # List monitored sites clank-uptime list # Remove a site clank-uptime remove https://example.com ``` ## Commands | Command | Description | |---------|-------------| | `add <URL> [--name NAME]` | Add site to monitoring | | `check` | Check all sites, append to history | | `stats` | Show 24h availability & latency stats | | `list` | List all monitored sites | | `remove <URL>` | Remove site from monitoring | ## Output examples ### Check ``` 🔍 Checking 3 sites... ✅ My Site: 200 (0.092s) ✅ API: 200 (0.056s) ⚠️ Old Site: 503 (1.234s) 📊 Checked at 2026-03-29T04:22:38Z ``` ### Stats ``` 📊 Uptime Statistics (last 24h) ================================================== My Site [████████████████████] 100.0% (12/12 checks) ⚡ Avg: 0.089s | Min: 0.056s | Max: 0.142s API [██████████████████░░] 91.7% (11/12 checks) ⚡ Avg: 0.067s | Min: 0.041s | Max: 0.098s ``` ## Data storage ``` ~/.clank-uptime/ ├── sites.json # Site configuration └── history/ └── <site-id>/ └── checks.csv # timestamp,status,response_time ``` ## Automating with cron ```bash # Check every 5 minutes */5 * * * * clank-uptime check > /dev/null 2>&1 # Daily stats report 0 8 * * * clank-uptime stats | mail -s "Daily Uptime Report" [REDACTED_SECRET] ``` ## Tips - Run `check` periodically (cron or heartbeat) to build history data - `stats` requires at least a few hours of data for meaningful results - Use `--name` for readability; defaults to URL - History is CSV-based — easy to parse or visualize with other tools


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ugpoor/dolphindb-init (version 1.0.0)

--- name: dolphindb-init description: DolphinDB Python 环境初始化技能。自动检测并切换到已安装 DolphinDB SDK 的 Python 环境（含 Anaconda/Miniconda/系统 Python），若未找到则自动安装。**这是 DolphinDB 套件的前置依赖，所有 DolphinDB 相关操作必须首先执行此技能**。 --- # DolphinDB 初始化技能 ## 🎯 套件角色 **这是 DolphinDB Skill 套件的前置依赖技能。** 所有 DolphinDB 相关技能在执行前都必须先调用此技能： ``` 任何 DolphinDB 请求 ↓ [必须] dolphindb-init ← 你在这里 ↓ 环境检测 ✓ ↓ 调用功能技能 ├─ dolphindb-core（连接、读写） └─ dolphindb-query（查询、分析） ``` ## 触发条件 **任何时候** 用户提到 DolphinDB 并需要操作： - "连接 DolphinDB" - "查询 DolphinDB 数据" - "在 DolphinDB 中建表" - "DolphinDB 性能分析" - 任何 DolphinDB Python SDK 相关操作 ## 核心流程 ### 1. 环境检测（必须第一步） ```bash # 加载环境检测器 source ~/.openclaw/skills/dolphindb-init/scripts/load_dolphindb_env.sh # 查看环境信息 dolphin_env_info # 使用统一的 Python 调用函数 dolphin_python your_script.py ``` ### 2. 检测逻辑 检测脚本按以下顺序扫描： 1. **conda 环境列表** → 检查每个环境的 `pip list`，查找 `dolphindb` 2. **Anaconda/Miniconda 路径** → 检查 `$CONDA_BASE_1`, `$CONDA_BASE_2` 等 3. **系统 Python 环境** → 检查 `$SYS_PYTHON_1`, `$SYS_PYTHON_2` 等 4. **决策**： - 找到已安装 → 导出环境变量 `DOLPHINDB_PYTHON_BIN` - 未找到 → 自动安装到 Python 3.13 环境 ### 3. 环境变量 加载后可用的环境变量： ```bash $DOLPHINDB_PYTHON_BIN # Python 二进制路径（统一调用入口） $DOLPHINDB_SDK_VERSION # DolphinDB SDK 版本 $DOLPHINDB_PYTHON_VER # Python 版本 $DOLPHINDB_ENV_PATH # 环境路径 ``` ### 4. 统一调用方式 ```bash # 运行 Python 脚本 dolphin_python script.py # 安装包 dolphin_pip install package_name # 直接使用环境变量 $DOLPHINDB_PYTHON_BIN script.py ``` ## 示例：DolphinDB 连接 ```python import dolphindb as ddb # 创建连接 s = ddb.session() s.connect("localhost", 8848, "admin", "password") # 执行查询 result = s.run("select * from trade where date=2024.01.01") print(result) # 关闭连接 s.close() ``` 使用加载器运行： ```bash source ~/.openclaw/skills/dolphindb-init/scripts/load_dolphindb_env.sh dolphin_python connect_dolphindb.py ``` ## 隐私保护 所有路径都用变量符号表示，不暴露具体系统地址： | 变量名 | 含义 | |--------|------| | `$CONDA_BASE_1` | 第一个 anaconda/miniconda 路径 | | `$CONDA_BASE_2` | 第二个 anaconda/miniconda 路径 | | `$SYS_PYTHON_1` | 第一个系统 Python 路径 | | `$PY13_CANDIDATE_1` | Python 3.13 候选路径 | ## 故障排除 ### 问题 1: 找不到环境 ```bash # 手动运行检测脚本查看详细信息 bash ~/.openclaw/skills/dolphindb-init/scripts/detect_dolphindb_env.sh ``` ### 问题 2: SDK 版本不兼容 ```bash dolphin_pip uninstall dolphindb dolphin_pip install dolphindb==3.0.4.2 ``` ### 问题 3: Python 版本不是 3.13 ```bash # 检查当前环境 dolphin_env_info # 强制安装到特定路径 ~/anaconda3/bin/python -m pip install dolphindb ``` ## 相关文件 - `scripts/detect_dolphindb_env.sh` - 核心检测脚本，输出可 eval 的 export - `scripts/load_dolphindb_env.sh` - 环境加载器，提供统一调用函数 - `scripts/find_dolphindb_env.sh` - 详细检测脚本（带完整输出） ## 套件关系 - **本技能** - `dolphindb-init`（环境初始化，前置依赖） - **核心操作** - `dolphindb-core`（连接、表操作、数据读写） - **高级查询** - `dolphindb-query`（聚合、时间序列、数据分析） ## 注意事项 1. **必须优先检测** - 不要假设 Python 环境已就绪 2. **使用统一接口** - 始终通过 `dolphin_python` 或 `$DOLPHINDB_PYTHON_BIN` 调用 3. **版本兼容** - 优先使用 Python 3.13 环境 4. **环境隔离** - 不同终端可能使用不同环境，必须每次检测 5. **套件前置** - 所有 DolphinDB 技能依赖此技能，必须首先执行


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: clarityprotocol/clarity-research (version 1.0.0)

--- name: clarity-research description: > Search protein folding research data from Clarity Protocol. Use when the user asks to search variants, query protein research, find fold results, or explore disease-specific protein data. Capabilities: list variants by disease or protein name, get API info. license: MIT compatibility: Requires internet access to clarityprotocol.io. Optional CLARITY_API_KEY env var for 100 req/min (vs 10 req/min). metadata: author: clarity-protocol version: "1.0.0" homepage: https://clarityprotocol.io --- # Clarity Research Skill Search protein folding research data from Clarity Protocol, a community-driven platform for protein mutation analysis using AlphaFold2 via ColabFold. ## Quick Start List all available variants: ```bash python scripts/query_variants.py ``` Filter by disease: ```bash python scripts/query_variants.py --disease Alzheimer ``` Filter by protein name: ```bash python scripts/query_variants.py --protein-name MAPT ``` ## Output Fields Each variant result includes: - `id`: Unique fold identifier - `protein_name`: Protein name (e.g., "tau", "APP") - `variant`: Mutation notation (e.g., "P301L", "A246E") - `disease`: Associated disease - `uniprot_id`: UniProt database identifier - `average_confidence`: AlphaFold confidence score (0-100) - `created_at`: When the fold was created ## Rate Limits - **Anonymous (no API key)**: 10 requests/minute - **With API key**: 100 requests/minute To use an API key, set the `CLARITY_API_KEY` environment variable: ```bash export CLARITY_API_KEY=your_key_here python scripts/query_variants.py ``` Get your API key at https://clarityprotocol.io ## Error Handling **404 Not Found**: The endpoint or resource does not exist. **429 Rate Limit**: You've exceeded the rate limit. The script will display how long to wait before retrying. **500 Server Error**: The API server encountered an error. Try again later. **Timeout**: The request took longer than 30 seconds. The server may be under heavy load. ## Pagination Results are paginated. The API returns a `next_cursor` field if more results are available. The script automatically handles pagination for typical queries. ## Use Cases - Explore available protein variants for a specific disease - Find all folded mutations for a particular protein - Get an overview of research data available in Clarity Protocol - Identify fold IDs for detailed analysis with other skills


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