OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tc1993/create-feishu-doc (version 1.0.8)

--- name: create-feishu-doc description: "Use this skill whenever the user wants to create, generate, write to, or organize content into a Feishu document. Triggers include: any mention of 'Feishu doc', 'Feishu document', '飞书文档', '创建飞书文档', '生成飞书文档', or requests to produce structured or complete documents within Feishu. This applies to scenarios requiring fully formatted documents, including but not limited to: technical documentation, PRDs, project plans, reports, notes, articles, or novels. Use this skill whenever content needs to be compiled, structured, and delivered as a Feishu document." --- # 通用飞书文档创建技能 ## 概述 本技能提供通用的飞书文档创建功能，根据用户提供的任意内容创建文档。支持各种文档类型：小说、技术文档、项目计划、报告、笔记、文章等。由于飞书文档API对单次写入内容长度有限制，本技能通过智能分段、分批写入和重试机制确保各种长文档的完整创建。 ## 核心工作流程 ### 第一步：创建空白文档 1. 调用飞书API的`feishu_doc`工具的`create`操作 2. 传入文档标题和初始内容（通常只包含标题） 3. 记录返回的`document_id`和文档URL ### 第二步：等待文档初始化 1. 等待2秒钟，确保文档在飞书服务端完全初始化 2. 这是关键步骤，避免在文档未就绪时进行写入操作 ### 第三步：分批次追加内容 1. 将完整内容按逻辑分段（每段约300-500字） 2. 对每个内容段： - 调用`feishu_doc`工具的`append`操作 - 使用上一步获取的`document_id` - 等待1秒后再处理下一段 3. 内容分段建议： - 按自然段落或章节分段（如：小说章节、文档章节、主题段落等） - 避免单段内容过长导致API失败（建议300-500字/段） - 保持逻辑完整性，不在句子中间切断 - 根据内容类型智能分段：小说按章节、技术文档按主题、报告按部分 ### 第四步：错误处理和重试 1. 如果`append`操作失败（返回400错误）： - 等待2秒后重试 - 最多重试3次 - 如果仍然失败，记录错误并继续下一段 2. 重试时可以考虑： - 减少内容长度 - 简化格式（移除复杂Markdown） - 检查网络连接 ## 最佳实践 ### 内容分段策略 - **小段落优先**：每段300-500字，避免API限制 - **逻辑完整**：按章节或主题分段，不在句子中间切断 - **格式简化**：使用飞书支持的简单格式（纯文本、简单列表） - **进度跟踪**：记录已写入的段落数和总段落数 ### 错误处理策略 - **立即重试**：首次失败后等待2秒重试 - **降级处理**：重试失败时简化内容格式 - **跳过继续**：单个段落失败不影响整体进度 - **最终验证**：所有段落写入后读取文档验证完整性 ### 性能优化 - **并行写入**：可以同时处理多个文档 - **批量操作**：相似内容可以合并写入 - **缓存利用**：重复内容可以缓存避免重复生成 - **进度保存**：支持断点续传，记录已完成的段落 ## 使用示例 ### 示例1：创建小说章节文档 ```python # 伪代码示例 1. create_doc("剑来小说第一章") 2. wait(2) 3. append_content("第一节：小镇少年...") 4. wait(1) 5. append_content("第二节：集市奇遇...") 6. wait(1) 7. append_content("第三节：神秘预言...") 8. 验证文档完整性 ``` ### 示例2：创建技术文档 ```python # 伪代码示例 1. create_doc("Python编程指南") 2. wait(2) 3. append_content("第一章：Python基础...") 4. wait(1) 5. append_content("第二章：函数和模块...") 6. wait(1) 7. append_content("第三章：面向对象编程...") 8. 验证文档完整性 ``` ### 示例3：通用文档创建流程 ```python # 伪代码示例 1. 获取用户输入的文档标题和内容 2. 创建空白文档 3. 等待文档初始化 4. 将内容智能分段 5. 分批次写入所有段落 6. 处理错误和重试 7. 验证文档完整性 8. 返回文档链接和创建结果 ``` ## 常见问题解决 ### 问题1：API返回400错误 **原因**：内容过长或格式不支持 **解决**： 1. 减少单次写入内容长度 2. 移除复杂Markdown格式 3. 使用纯文本格式重试 ### 问题2：文档内容丢失 **原因**：写入操作未成功但未检测到 **解决**： 1. 每次写入后验证返回状态 2. 记录每个段落的写入状态 3. 最终读取文档验证完整性 ### 问题3：写入速度慢 **原因**：等待时间过长或网络延迟 **解决**： 1. 优化等待时间（1-2秒通常足够） 2. 考虑并行写入多个段落 3. 使用本地缓存减少重复生成 ## 资源文件 ### scripts/create_feishu_doc.py 提供Python脚本实现完整的文档创建流程，包括错误处理和重试机制。 ### references/feishu_api_guide.md 飞书API使用指南，包含常见错误代码和解决方案。 --- **技能特点**： - **通用性强**：支持各种类型的文档内容创建 - **智能分段**：根据内容类型自动智能分段 - **错误处理**：智能重试和格式简化机制 - **进度跟踪**：实时显示写入进度和成功率 - **完整性验证**：创建后验证文档完整性 - **用户友好**：简单的接口和详细的状态报告


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: codenova58/cloud-arch (version 1.0.0)

--- name: cloud-arch description: "Multi-account patterns, networking, and well-architected trade-offs. Use when designing cloud systems." --- # Cloud Arch Structured guidance for **cloud architecture** (accounts, networking, well-architected trade-offs): confirm triggers, propose the stages below, and adapt if the user wants a lighter pass. ## When to Offer This Workflow **Trigger conditions:** - User mentions **cloud architecture** or closely related work - They want a structured workflow rather than ad-hoc tips - They are preparing a review, rollout, or stakeholder communication **Initial offer:** Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style. ## Workflow Stages ### Stage 1: Clarify context & goals Anchor on **accounts, networking, identity**. Ask what success looks like, constraints, and what must not break. Capture unknowns early. ### Stage 2: Design or plan the approach Translate goals into a concrete plan around **data and encryption**. Compare alternatives and explicit trade-offs; avoid implicit assumptions. ### Stage 3: Implement, validate, and harden Execute with verification loops tied to **scalability patterns**. Prefer small steps, measurable checks, and rollback points where risk is high. ### Stage 4: Operate, communicate, and iterate Close the loop with **operational model**: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle. ## Checklist Before Completion - Goals and constraints are explicit for **cloud architecture** - Risks and trade-offs are stated, not hand-waved - Verification steps match the change’s impact (tests, canary, peer review) - Operational follow-through is covered (monitoring, docs, owners) ## Tips for Effective Guidance - Be procedural: stage-by-stage, with clear exit criteria - Ask for missing context (environment, scale, deadlines) before prescribing - Prefer checklists and concrete examples over generic platitudes - If the user declines the workflow, switch to freeform help without lecturing ## Handling Deviations - If the user wants to skip a stage: confirm and continue with what they need. - If context is missing: ask targeted questions before strong recommendations. - Prefer concrete examples, trade-offs, and verification steps over generic advice. ## Quality Bar - Each recommendation should be **actionable** (what to do next). - Call out **failure modes** relevant to cloud systems (security, scale, UX, or ops). - Keep tone direct and respectful of the user’s time.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/ppe (version 1.0.0)

--- name: "ppe" version: "1.0.0" description: "Personal protective equipment tracker. Use when json ppe tasks, csv ppe tasks, checking ppe status." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [ppe, safety, cli, tool] category: "safety" --- # ppe Personal protective equipment tracker. Use when json ppe tasks, csv ppe tasks, checking ppe status. ## Commands ### `status` ```bash scripts/script.sh status ``` Show current status ### `add` ```bash scripts/script.sh add ``` Add new entry ### `list` ```bash scripts/script.sh list ``` List all entries ### `search` ```bash scripts/script.sh search ``` Search entries ### `remove` ```bash scripts/script.sh remove ``` Remove entry by number ### `export` ```bash scripts/script.sh export ``` Export data to file ### `stats` ```bash scripts/script.sh stats ``` Show statistics ### `config` ```bash scripts/script.sh config ``` View or set config ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration Use `scripts/script.sh config <key> <value>` to set preferences. | Variable | Description | |----------|-------------| | `PPE_DIR` | Data directory (default: ~/.ppe/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: huajianjiu000/learning-plan (version 1.0.0)

# 学习计划制定 (Learning Plan Generator) ## 技能描述 根据学习目标和个人情况，智能生成科学、系统的学习计划。支持长期规划，自动分解里程碑，适配多种学习场景。 ## 核心功能 - 智能评估学习目标难度 - 自动分解学习阶段 - 生成每日/每周学习任务 - 提供学习资源和路径 - 跟踪学习进度 ## 适用场景 - 编程语言学习（Python/Java/前端等） - 考证备考（CPA/PMP/Python证书等） - 技能提升（数据分析/产品设计等） - 考试复习（考研/托福/雅思等） - 新领域探索 ## 技能结构 ``` learning-plan/ ├── SKILL.md # 主文件 ├── references/ │ └── plan_templates.md # 计划模板 └── scripts/ └── generate_plan.py # 生成脚本 ``` ## 学习计划结构 ```markdown # [主题]学习计划 ## 学习目标 ## 学习周期 ## 阶段划分 ## 每日任务 ## 学习资源 ## 评估方式 ``` ## 使用方法 1. 描述学习目标（如：3个月学会Python） 2. 提供当前基础水平（可选） 3. 指定每天可用学习时间 4. 选择学习方式（自学/课程/实战） 5. 系统生成完整学习计划 ## 触发关键词 - "制定学习计划" - "学习计划" - "怎么学习" - "备考计划" - "学习路径" - "零基础学习" ## 学习阶段划分 | 阶段 | 名称 | 周期 | 目标 | |------|------|------|------| | 第一阶段 | 入门 | 第1-2周 | 基础概念 | | 第二阶段 | 基础 | 第3-6周 | 核心知识 | | 第三阶段 | 进阶 | 第7-10周 | 高级特性 | | 第四阶段 | 实战 | 第11-12周 | 项目实践 | ## 依赖要求 - Python 3.8+ - 无需外部API ## 版本信息 - 版本：1.0.0 - 更新日期：2024年 - 作者：ClawHub Skills


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: star1986c/check-user-fraud (version 1.0.0)

--- name: check-user-fraud description: Query MySQL database to analyze user fraud/shuadan behavior. Use when user asks to check if a user is engaging in fraudulent task completion. Analyzes time intervals, publisher concentration, task duplication, and top refresh status to identify suspicious patterns. --- # 用户刷单查询 根据userId查询MySQL数据库，分析用户做单行为是否涉嫌刷单。 ## 使用场景 当需要查询用户是否存在刷单行为时，使用此技能： - 用户举报某账号刷单 - 风控系统标记异常账号 - 定期抽查用户行为 - 分析做单模式 ## 查询流程 ### 1. 执行查询脚本 ```bash python3 scripts/check_fraud.py <userId> ``` ### 2. 分析维度 脚本会自动分析以下指标： #### 时间间隔分析 - 报名到提交的时间间隔 - 完成时间 < 5分钟: 高度可疑 - 完成时间 5-10分钟: 中度可疑 #### 发单人集中度 - 频繁接取同一发单人的任务 - 同一发单人 > 10次: 高度可疑 - 同一发单人 5-10次: 中度可疑 #### 任务重复度 - 多次接取相同任务 - 重复任务 > 2次: 可疑 #### 置顶刷新状态 - 接取时任务是否有置顶 - 未置顶任务比例 > 70%: 可疑（可能通过非正规渠道获取） ### 3. 风险等级评估 - **高风险**: 2个及以上高风险指标，或完成时间<5分钟占比>50% - **中风险**: 1个高风险或2个中风险指标 - **低风险**: 无明显异常 ## 数据库配置 - **Host**: rr-wz97dxha81orq30j0eo.mysql.rds.aliyuncs.com - **Port**: 3389 - **User**: oc_gw - **Password**: m83KkZVLQp2Wg7HgDVb5cRjQ ## SQL查询参考 详细SQL语句见 `references/` 目录： - `query_user_records.sql` - 查询用户做单记录 - `query_top_refresh.sql` - 查询任务置顶状态 - `fraud_analysis_guide.md` - 刷单分析指标说明 ## 输出格式 ```json { "userId": "用户ID", "total_records": 记录总数, "records": [...], // 详细记录 "fraud_indicators": [ // 可疑指标 { "type": "指标类型", "level": "high/medium/low", "description": "描述" } ], "summary": { "risk_level": "high/medium/low", "conclusion": "结论", "indicators_count": 指标数量, "high_risk_count": 高风险数量, "medium_risk_count": 中风险数量 } } ``` ## 注意事项 1. 需要安装pymysql: `pip install pymysql` 2. 数据库名需要确认后填入脚本 3. 查询结果包含敏感信息，注意保密


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: terrycarter1985/python-support (version 1.0.0)

--- name: python-support description: Python language support for OpenClaw agents. Provides environment setup, dependency management, linting, testing, and best practices for Python code execution. Use when running Python scripts, managing virtual environments, installing packages, debugging Python code, or ensuring code quality with linting and formatting. --- # Python Support ## Quick Start Use this skill for all Python-related operations in OpenClaw. ### Environment Check Verify Python environment: ```bash python3 --version pip3 --list which python3 ``` ### Running Scripts Always use absolute paths and specify Python interpreter explicitly: ```bash python3 /path/to/script.py ``` ### Dependency Management **Install packages safely:** ```bash pip3 install --quiet package-name ``` For one-off scripts requiring dependencies, use inline installation with verification: ```python import subprocess import sys def ensure_package(package): try: __import__(package) except ImportError: subprocess.check_call([sys.executable, "-m", "pip", "install", "--quiet", package]) ``` ### Best Practices 1. **Shebang**: Use `#!/usr/bin/env python3` for executable scripts 2. **Error handling**: Always include try/except blocks for external operations 3. **Encoding**: Specify `encoding="utf-8"` for all file operations 4. **Paths**: Use `pathlib.Path` for cross-platform path handling 5. **Output**: Prefer JSON or machine-readable formats for structured output ### References - See [references/style-guide.md](references/style-guide.md) for Python style guidelines - See [references/testing-patterns.md](references/testing-patterns.md) for testing patterns - See [references/debugging-tips.md](references/debugging-tips.md) for debugging techniques

path: references/style-guide.md
content: # Python Style Guide

## PEP 8 Compliance

Follow PEP 8 guidelines with these specific adjustments:

- Line length: 100 characters (not 79)
- String quotes: Use double quotes for human-readable text, single quotes otherwise
- Type hints: Required for all public function signatures

## Code Structure

```python
#!/usr/bin/env python3
"""Module docstring."""

import subprocess
import sys
from pathlib import Path
from typing import Optional

def function_name(param1: str, param2: Optional[int] = None) -> bool:
    """Function docstring."""
    return True

if __name__ == "__main__":
    main()
```

## Error Handling

```python
try:
    result = subprocess.run(
        ["command", "arg1"],
        capture_output=True,
        text=True,
        check=True
    )
except subprocess.CalledProcessError as e:
    print(f"Error: {e.stderr}", file=sys.stderr)
    sys.exit(1)
```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: harleyscodes/hedera-token-mint (version 1.0.0)

--- name: hedera-token-mint description: Create and manage tokens on Hedera (HTS). Use for: (1) Minting fungible tokens, (2) Creating NFTs (HTS), (3) Setting up token supplies, (4) Configuring token permissions. --- # Hedera Token Minting (HTS) ## Setup ```bash npm install @hashgraph/sdk ``` ## Create Fungible Token ```typescript import { TokenCreateTransaction, TokenSupplyType, TokenType } from '@hashgraph/sdk'; const tx = await new TokenCreateTransaction() .setTokenName("My Token") .setTokenSymbol("MTK") .setTokenType(TokenType.FungibleCommon) .setSupplyType(TokenSupplyType.Infinite) .setDecimals(2) .setInitialSupply(1000000) // Total supply = 1,000,000 .setTreasuryAccountId(treasuryId) .setAdminKey(adminKey) .setSupplyKey(supplyKey) .freezeWith(client) .sign(treasuryKey); const result = await tx.execute(client); const tokenId = result.receipt.tokenId; ``` ## Create NFT Collection ```typescript const tx = await new TokenCreateTransaction() .setTokenName("My NFT Collection") .setTokenSymbol("MNFT") .setTokenType(TokenType.NonFungibleUnique) .setTreasuryAccountId(treasuryId) .setAdminKey(adminKey) .setSupplyKey(supplyKey) .freezeWith(client) .sign(treasuryKey); ``` ## Mint NFTs ```typescript import { TokenMintTransaction } from '@hashgraph/sdk'; const tx = await new TokenMintTransaction() .setTokenId(tokenId) .addMetadata(Buffer.from("NFT #1 metadata")) .addMetadata(Buffer.from("NFT #2 metadata")) .freezeWith(client) .sign(supplyKey); const result = await tx.execute(client); ``` ## Token Operations ### Transfer Token ```typescript import { TransferTransaction } from '@hashgraph/sdk'; await new TransferTransaction() .addTokenTransfer(tokenId, fromAccount, -10) .addTokenTransfer(tokenId, toAccount, 10) .execute(client); ``` ### Burn Tokens ```typescript import { TokenBurnTransaction } from '@hashgraph/sdk'; await new TokenBurnTransaction() .setTokenId(tokenId) .setAmount(100) .execute(client); ``` ## Key Points - **Supply Types**: `Infinite` or `Finite` - **Token Types**: `FungibleCommon` or `NonFungibleUnique` - **Royalty**: Use custom fees for NFT royalties - **Token ID Format**: `0.0.12345`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-color-picker (version 1.2.3)

name: 颜色选择器 version: "1.0.0" description: "颜色选择与转换工具。支持HEX/RGB/HSL/HSV格式互转、调色板生成、渐变色预览。纯Python标准库，无需API Key。" license: MIT-0 tags: - tools # 颜色选择器 颜色选择与转换工具。设计师和前端开发者的颜色助手。 ## 功能 - **格式互转**：HEX ↔ RGB ↔ HSL ↔ HSV - **调色板生成**：基于一个主色生成5色和谐调色板 - **渐变色预览**：生成两色之间的10级渐变 - **颜色解析**：自动识别输入格式并转换 ## 安装要求 - Python 3.6+ - 无外部依赖 ## 使用方法 ```bash # HEX转RGB python3 scripts/color_picker.py "hex2rgb #FF5500" # RGB转HSL python3 scripts/color_picker.py "rgb2hsl 255,85,0" # 生成调色板 python3 scripts/color_picker.py "palette #3366FF" # 渐变色 python3 scripts/color_picker.py "gradient #FF0000 #0000FF" ``` ## 示例 输入：`hex2rgb #FF5500` 输出：`RGB: (255, 85, 0)` 输入：`palette #3366FF` 输出：5个和谐配色方案的HEX值 ## 分类 设计工具 ## 关键词 颜色, 调色板, HEX, RGB, HSL, HSV, color, palette, gradient

path: scripts/color_picker.py
content: #!/usr/bin/env python3
import sys, json, re

def hex_to_rgb(hex_color):
    hex_color = hex_color.lstrip('#')
    return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))

def rgb_to_hsv(r, g, b):
    r, g, b = r/255, g/255, b/255
    mx, mn = max(r,g,b), min(r,g,b)
    h = s = v = mx
    d = mx - mn
    s = 0 if mx == 0 else d/mx
    if mx == mn: h = 0
    elif mx == r: h = (g-b)/d + (6 if g<b else 0)
    elif mx == g: h = (b-r)/d + 2
    else: h = (r-g)/d + 4
    h /= 6
    return int(h*360), int(s*100), int(v*100)

def convert_color(color):
    color = color.strip()
    if re.match(r'^#[0-9a-fA-F]{6}$', color):
        r, g, b = hex_to_rgb(color)
        h, s, v = rgb_to_hsv(r, g, b)
        return {'hex': color.upper(), 'rgb': f'rgb({r},{g},{b})', 'hsl': f'hsl({h},{s}%,{v}%)', 'hsv': f'hsv({h},{s}%,{v}%)'}
    return {'error': '无效颜色格式'}

if __name__ == '__main__':
    print(json.dumps(convert_color(sys.argv[1] if len(sys.argv)>1 else '#000000'), ensure_ascii=False))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/camping-sites (version 0.1.0)

--- name: "Nearby Camping Sites" description: "Find nearby camping sites. Invoke when user asks for camping near me." --- # Nearby Camping Sites 用途 - 提供用户当前位置附近的 Camping Sites 列表 - 统一返回字段与查询行为，便于前端/接口复用 - 适用于露营地/营地/房车营地等就近查询与出行规划 触发条件 - 用户询问“Camping Sites 附近 / camping near me / nearby campsite” - 用户提供定位/城市并希望“找/推荐/看看附近的 Camping Sites” 输入参数 - location: 经纬度 { lat, lng }，必填 - radius_meters: 查询半径，默认 8000 - limit: 返回数量上限，默认 20，最大 50 - filters: 可选筛选（open_now、min_rating、keywords 等） 响应字段 - 统一参见 STANDARD_RESPONSE.md - 本技能 category 固定为 "camping-sites" 错误码 - INVALID_LOCATION: 经纬度不合法 - RADIUS_TOO_LARGE: 超过最大查询半径 - PROVIDER_UNAVAILABLE: 数据源不可用 - RATE_LIMITED: 触发速率限制 示例 - 输入: { location: { lat: 30.123, lng: 120.456 }, radius_meters: 8000, limit: 10 } - 输出: 标准 POI 列表（见 STANDARD_RESPONSE.md） 隐私与速率限制 - 仅在用户授权定位后查询 - 避免保留精确坐标，必要时进行网格化模糊处理 - 建议对同一 location+category+radius 做短时缓存以降低频率


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/google-drive-download-file (version 1.0.1)

--- name: Google Drive Download File description: Atomic node skill to download a file from Google Drive using the gog CLI. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, broken down into its simplest, smallest component to eliminate waste and ensure perfection. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It relies on the CLI's self-healing loop and will report errors if the download fails. # Google Drive Download File This skill allows the agent to download a file from Google Drive using the native CLI. ## Cognitive Directives WHEN [A file needs to be downloaded locally from Google Drive] THEN [Execute the native terminal command `gog drive download <fileId> --out <localPath>`] ## Schema Example ```json { "command": "gog drive download file_id_123 --out /tmp/document.pdf" } ``` ## Expected Output Confirmation that the file was downloaded to the specified path.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/angular (version 1.0.1)

--- name: Angular slug: angular version: 1.0.1 description: Build reliable Angular apps avoiding RxJS leaks, change detection traps, and DI pitfalls. --- ## When to Use User needs Angular expertise — component architecture, RxJS patterns, change detection, dependency injection, routing, and forms. ## Quick Reference | Topic | File | |-------|------| | Components & change detection | `components.md` | | RxJS & subscriptions | `rxjs.md` | | Forms & validation | `forms.md` | | Dependency injection | `di.md` | | Routing & guards | `routing.md` | | HTTP & interceptors | `http.md` | ## Common Mistakes - `OnPush` with mutated objects won't trigger change detection — always create new reference: `{...obj}` or `[...arr]` - `@ViewChild` is undefined in constructor/`ngOnInit` — access in `ngAfterViewInit` or later - `*ngFor` without `trackBy` re-renders entire list on any change — add `trackBy` returning stable ID - Manual `subscribe()` without unsubscribe leaks memory — use `async` pipe, `takeUntilDestroyed()`, or unsubscribe in `ngOnDestroy` - `HttpClient` returns cold Observable — each `subscribe()` fires new HTTP request - `setTimeout`/`setInterval` outside NgZone — change detection won't run, use `NgZone.run()` or signals - Circular DI dependency crashes app — use `forwardRef()` or restructure services - `ElementRef.nativeElement` direct DOM access breaks SSR — use `Renderer2` or `@defer` - Route params via `snapshot` miss navigation changes — use `paramMap` Observable for same-component navigation - `setValue()` on FormGroup requires ALL fields — use `patchValue()` for partial updates


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joshbuildswithdoit/amazon-fba-ops (version 1.0.0)

--- name: amazon-fba-ops description: Amazon FBA seller operations for private label and wholesale sellers. Inventory alerts, reorder point calculations, listing optimization, PPC budget tracking, review monitoring, and competitor analysis. Built for 6-7 figure FBA sellers who want to run leaner. --- # Amazon FBA Operations Run your Amazon business tighter without more headcount. ## Inventory Management - Daily inventory health check (days of stock remaining per ASIN) - Reorder point alerts (flag when stock < 30 days) - Generate purchase orders from reorder quantities - Stranded inventory report and fix recommendations - FBA fee estimate for new products ## Listing Optimization - Monthly listing audit (title, bullets, description, backend keywords) - A+ Content improvement suggestions - Competitor listing comparison (input ASINs) - Image improvement checklist per listing ## PPC & Advertising - Weekly PPC performance summary (input ACoS, spend, sales) - Flag campaigns with ACoS > target - Keyword harvesting reminders (move winners from auto to manual) - Budget pacing check (daily spend vs monthly budget) ## Reviews & Ratings - Daily new review digest (flag 1-3 star reviews) - Draft seller response templates for negative reviews - Review velocity tracking (goal: consistent new reviews) - Vine enrollment reminder for new ASINs ## Financials - Monthly P&L template (revenue, COGS, FBA fees, PPC spend, net margin) - Unit economics calculator (input landed cost → margin at each price point) - Reimbursement audit checklist (lost/damaged inventory) - Q4 inventory planning timeline ## Competitor Intelligence - Weekly competitor price monitoring (input competitor ASINs) - New entrant alerts in your category - Buy Box win rate tracker ## Setup Tell your agent: - Your top 10 ASINs and reorder lead times - Target ACoS per campaign - Supplier contacts and MOQs - Current monthly ad spend budget


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vimvem/skill-ict (version 3.3.2)

--- name: skill-ict version: 3.3.2 description: "DEPRECATED - This skill has been renamed to 'ict'. Please install 'ict' instead: clawhub install ict" metadata: openclaw: requires: bins: [] env: [] config: [] user-invocable: false --- # ⚠️ DEPRECATED / 已停用 **This skill has been renamed to `ict`.** ## Please use the new skill instead: ```bash clawhub install ict ``` --- ## Version History | Version | Status | |---------|--------| | 3.3.1 | Old version | | **4.0.2** | **Current** → Use `ict` | --- ## Why? The skill has been renamed to `ict` (simpler name) and includes additional security checks (23 items). ## Support For issues, please use the new `ict` skill.


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