OpenClaw/clawhub-security-signals huggingface dataset:
Skill: parkertoddbrooks/wip-repo-init (version 1.9.72)

--- name: wip-repo-init description: Scaffold the standard ai/ directory structure in any repo. license: MIT interface: [cli, skill] metadata: display-name: "Repo Init" version: "1.0.0" homepage: "https://github.com/wipcomputer/wip-ai-devops-toolbox" author: "Parker Todd Brooks" category: repo-management capabilities: - scaffold-ai-dir - template-copy requires: bins: [node] openclaw: requires: bins: [node] install: - id: node kind: node package: "@wipcomputer/wip-repo-init" bins: [wip-repo-init] label: "Install via npm" emoji: "📁" compatibility: Requires node. Node.js 18+. --- # Repo Init Scaffolds the standard `ai/` directory structure in any repo. ## Commands ``` wip-repo-init /path/to/repo # scaffold ai/ in a repo wip-repo-init /path/to/repo --dry-run # preview without changes wip-repo-init /path/to/repo --yes # skip confirmation prompt ``` ## What happens **New repo (no ai/ folder):** Creates the full standard structure with all READMEs explaining what goes where. **Existing repo (ai/ folder exists):** Shows you what will happen and asks for confirmation. If you say yes: 1. Moves your current `ai/` contents to `ai/_sort/ai_old/` 2. Scaffolds the new standard structure 3. You sort files from `ai_old/` into the new structure at your own pace Nothing is deleted. Your old files are all in `ai/_sort/ai_old/`. ## The standard ai/ structure ``` ai/ read-me-first.md <- explains everything, links to all sections _sort/ <- holding pen for files that need sorting _trash/ <- archive (never delete, move here) dev-updates/ <- engineering changelog, auto-detected by wip-release product/ readme-first-product.md <- the product bible notes/ <- freeform notes, research plans-prds/ <- plans with lifecycle stages roadmap.md <- prioritized roadmap current/ <- plans being built now upcoming/ <- plans that are next archive-complete/ <- plans that shipped todos/ <- per-agent todo files product-ideas/ <- ideas that aren't plans yet ``` Every folder has a `_trash/` subfolder. Every section has a README explaining what it is, what goes in it, and how to maintain it. ## Interfaces CLI, Skill


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leo7723/music-router (version 1.0.0)

# Music Router Skill This skill solves the problem of incompatible music sharing links between different platforms. It provides direct links for Chinese platforms and automatically resolves all major international platforms via the Odesli (song.link) API. ## Features - **Auto-Identification**: Automatically identifies the platform of the input music sharing link. - **Precise Extraction**: Automatically extracts song name, artist information, and **album artwork**. - **Multi-Platform Resolution**: - **Chinese Platforms**: Direct links for Netease Cloud Music and QQ Music. - **International Platforms**: Resolves direct links for Spotify, Apple Music, YouTube, YouTube Music, Amazon Music, Tidal, Deezer, and SoundCloud. - **Optional Logging**: Conversion details can be recorded in `music-router/data/converter.log` via a command-line flag. ## Agent Usage Guide ### 1. Basic Call (Link Only) The Agent can pass the sharing link directly. ```bash python3 music-router/scripts/converter.py "https://y.music.163.com/m/song?id=1988907896" ``` ### 2. Precise Call (Link + Song/Artist) If the Agent already knows the song name and artist, it can be passed as the second argument to improve matching accuracy for Chinese platforms. ```bash python3 music-router/scripts/converter.py "https://open.spotify.com/track/315aBOUD3xtj7sUMXtRgMV" "In The Stars Benson Boone" ``` ### 3. Enabling Logs (Optional) To enable detailed logging for debugging, add the `--log` flag. **Logging is disabled by default.** ```bash python3 music-router/scripts/converter.py "https://open.spotify.com/track/123" --log ``` ## Output Example (JSON) ```json { "original_platform": "Spotify", "detected_song": "In The Stars Benson Boone", "thumbnail": "https://m.media-amazon.com/images/I/31OJGk6XaLL.jpg", "conversions": { "网易云音乐": "https://music.163.com/#/song?id=1941928009", "QQ音乐": "https://y.qq.com/n/ryqq/songDetail/001Paon617ueUC", "Apple Music": "https://geo.music.apple.com/us/album/_/1621221545?i=1621221910...", "YouTube": "https://www.youtube.com/watch?v=ZLck_PSMNew", "Amazon Music": "https://music.amazon.com/albums/B0B5FKVB4P?trackAsin=B0B5FK4MM1", "Tidal": "https://listen.tidal.com/track/226476015", "song.link (Aggregation)": "https://song.link/s/315aBOUD3xtj7sUMXtRgMV" } } ``` ## Supported Platforms - **Direct Resolution**: Netease Cloud Music, QQ Music. - **International Resolution**: Spotify, Apple Music, YouTube, YouTube Music, Amazon Music, Tidal, Deezer, SoundCloud. ## Notes - **Odesli (song.link)**: This skill uses the Odesli API to resolve international links and fetch album artwork, ensuring high accuracy and a rich visual experience.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sunsetchow/rss-brew (version 0.1.0)

--- name: rss-brew description: Run and operate the RSS-Brew digest pipeline, including app CLI usage, dry-runs, latest-run inspection, delivery status updates, and retry/finalize-aware operations. --- # RSS-Brew Skill Use this skill when you need to: - run the RSS-Brew pipeline - inspect the latest run - do a dry-run - update delivery status - understand the current migration/app-wrapper state - troubleshoot retry/finalize behavior ## Current recommended entrypoint Use the app CLI from the skill root: ```bash cd /root/.openclaw/workspace/skills/rss-brew export PYTHONPATH=app/src python3 -m rss_brew.cli --help ``` Common commands: ```bash python3 -m rss_brew.cli inspect latest --data-root '/root/workplace/2 Areas/rss-brew-data' python3 -m rss_brew.cli dry-run --data-root '/root/workplace/2 Areas/rss-brew-data' --debug python3 -m rss_brew.cli run --data-root '/root/workplace/2 Areas/rss-brew-data' --debug python3 -m rss_brew.cli delivery update --data-root '/root/workplace/2 Areas/rss-brew-data' --status sent ``` ## Current architecture reality RSS-Brew is in an in-place app-ification state: - `app/` is the new app wrapper/container - `app/src/rss_brew/cli.py` is the new entrypoint - `scripts/` still contain the runtime source-of-truth semantics - the app CLI intentionally delegates to legacy scripts in the current phase ## Read these references as needed - **Usage / commands:** `references/usage.md` - **Operational checks / failure modes:** `references/ops.md` - **Pipeline behavior / outputs:** `references/pipeline-spec.md` - **Retry / finalize architecture:** `references/retry-architecture.md` - **Migration / implementation status:** `docs/rss-brew-implementation-plan.md` ## Current default data root ```bash /root/workplace/2 Areas/rss-brew-data ``` ## Important note This skill is no longer just a loose script bundle. It is now a working app-wrapper around the legacy RSS-Brew runtime. Prefer the app CLI for new operations while keeping legacy scripts intact during migration.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: qbs784/ragtop-agent (version 1.0.0)

--- name: ragtop-agent description: 高级 RAG 助手，具备 Agentic RAG 思考能力。能够自动化管理 RAGTOP 知识库，并在执行深度调研任务时，调用 RAGTOP 后端接口。使用场景包括：(1) 列出知识库和文档，(2) 执行语义检索和深度分析。 metadata: { "openclaw": { "emoji": "🦖", "requires": { "env": ["RAGTOP_API_TOKEN"] }, "primaryEnv": "RAGTOP_API_TOKEN" } } --- # ragtop-agent Skill 本 Skill 允许 AI 通过 `curl` 调用 RAGTOP 后端接口。AI 必须根据用户请求的性质，在“简单指令”与“深度调研”两种模式间切换。 ## Configuration The following environment variables are required: - `RAGTOP_API_URL`: RAGTOP API base URL. Defaults to `http://10.71.10.71:9380` if not set. - `RAGTOP_API_TOKEN`: Your RAGTOP API access token. Can be configured via the OpenClaw Web UI. ## 1. 核心工具构建指南 (How to build curl) 在调用以下接口前，请确保已获取环境变量 `${RAGTOP_API_URL}` 和 `${RAGTOP_API_TOKEN}`。如果 `${RAGTOP_API_URL}` 为空，请使用默认值 `http://10.71.10.71:9380`。 ### A. 列出知识库 (list_kb) 用于获取所有可用的 `knowledge_id`。 ```bash curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/list_kb" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" ``` ### B. 列出文档 (list_doc) 用于获取特定知识库内的 `doc_id` 列表，以便缩小检索范围。 - **Payload**: `{"knowledge_id": "string"}` ```bash curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/list_doc" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{"knowledge_id": "YOUR_KB_ID"}' ``` ### C. 内容检索 (retrieval) 用于执行语义搜索。支持单查询或多查询。 - **Payload 关键参数**: - `knowledge_id`: 必填。 - `query`: 字符串（单查询）。 - `queries`: 字符串数组（多查询，推荐用于复杂任务）。 - `doc_ids`: 数组（可选，限定文件范围）。 - `retrieval_setting`: `{"top_k": 16, "score_threshold": 0.3}` ```bash curl -L -X POST "${RAGTOP_API_URL}/api/v1/ragtop/tool/retrieval" \ -H "Authorization: Bearer ${RAGTOP_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "knowledge_id": "YOUR_KB_ID", "queries": ["查询1", "查询2"], "retrieval_setting": {"top_k": 5} }' ``` ## 2. 任务分类处理逻辑 ### 情况 A：简单指令 (Simple Instructions) **适用场景**: 用户询问“有哪些知识库？”、“这个库里有哪些文件？”等管理类问题。 **执行逻辑**: 1. 直接根据需求构建 `list_kb` 或 `list_doc` 的 `curl` 命令。 2. 将返回的 JSON 结果整理成易读的表格或列表告知用户。 ### 情况 B：深度调研 (Deep Investigation / Agentic RAG) **适用场景**: 用户提出具体业务问题、对比分析或需要跨文档总结。 **执行逻辑**: 1. **参考 [references/workflow.md](references/workflow.md)** 执行“分析-分解-检索-综合”流程。 2. **多步编排**: - 第一步：调用 `list_kb` 确定相关的知识库 ID。 - 第二步（可选）：调用 `list_doc` 锁定相关文件。 - 第三步：构建包含多个改写问题的 `retrieval` 请求，利用多路召回提高准确率。 - 第四步：根据检索到的多个 chunks 进行逻辑推理和引用标注。 ## 3. 运行规范 - **严禁**提及 `ragflow`，统一使用 `ragtop`。 - **引用必填**: 所有深度调研的回答必须注明引用的文档名称。 - **错误处理**: 如果 `curl` 返回非 SUCCESS，应检查 Token 有效性并告知用户。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: garibong-labs/brave-api-setup (version 0.1.2)

--- name: brave-api-setup description: Set up Brave Search API for OpenClaw web_search. Use when user needs to configure Brave API, get Brave API key, enable web search, or fix "missing_brave_api_key" error. metadata: openclaw: requires: bins: ["node"] triggers: - Brave Search - brave api - web_search - missing_brave_api_key - search API --- # Brave API Setup Automates Brave Search API key extraction and OpenClaw configuration. ## Included Files | File | Description | |------|-------------| | `SKILL.md` | This document | | `scripts/apply-api-key.js` | Applies API key to OpenClaw config (Node.js) | ## Dependencies - Node.js (for apply-api-key.js) - OpenClaw browser capability (`browser` tool) ## When to Use - User wants to enable `web_search` in OpenClaw - Error: `missing_brave_api_key` - User asks to set up Brave Search API ## Prerequisites - User must have a Brave Search API account - User must be logged in (openclaw browser profile) - API key must exist in dashboard ## Workflow ### Step 1: Navigate to API keys page ``` browser(action="navigate", profile="openclaw", targetUrl="https://api-dashboard.search.brave.com/app/keys") ``` ### Step 2: Click reveal button (eye icon) Take snapshot, find the reveal button, click it: ``` browser(action="act", kind="click", ref="<eye-button-ref>") ``` ### Step 3: Extract key via JavaScript (avoids LLM transcription error) ``` browser(action="act", kind="evaluate", fn="(() => { const cells = document.querySelectorAll('td'); for (const cell of cells) { const text = cell.textContent?.trim(); if (text && text.startsWith('BSA') && !text.includes('•') && text.length > 20) return text; } return null; })()") ``` The result field contains the exact API key. ### Step 4: Apply to config (direct file write, no LLM involved) Relative to skill directory: ```bash node <skill_dir>/scripts/apply-api-key.js "<extracted-key>" ``` Or use gateway config.patch with the extracted key. ## Why This Approach **Problem**: LLM can confuse similar characters when transcribing (O vs 0, l vs 1). **Solution**: 1. `evaluate` extracts key via JavaScript → returns exact string 2. `apply-api-key.js` writes directly to config → bit-perfect The key never passes through LLM text generation. ## Manual Account Setup If user doesn't have an account: 1. Go to https://api-dashboard.search.brave.com 2. Sign up with email 3. Subscribe to Free plan (requires credit card) 4. Create API key in dashboard 5. Then run this skill ## 문의 / Feedback 버그 리포트, 기능 요청, 피드백은 아래로 보내주세요. - Email: [REDACTED_SECRET] - Developer: Garibong Labs (가리봉랩스)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: beipian261/zhua-economy (version 1.0.0)

--- name: zhua-economy version: 1.0.0 description: 爪爪自主经济系统 —— 技能变现、任务定价、收益分配、经济自治。Use when 爪爪需要建立经济系统、技能变现、或实现经济自主。 --- # 爪爪经济系统 (Zhua Economy) 让爪爪能够自主定价、变现技能、分配收益、实现经济自治。 ## 核心能力 1. **技能定价** - 为自研技能定价 2. **任务计费** - 按任务难度和耗时计费 3. **收益分配** - 收益分配给小弟和陛下 4. **经济自治** - 自主决策经济事务 5. **价值追踪** - 追踪创造的价值 ## 定价模型 | 服务类型 | 定价方式 | 示例 | |---------|---------|------| | 技能使用 | 按次付费 | 使用zhua-evolver: 0.01 USD | | 任务执行 | 按复杂度 | 简单任务: 0.1 USD, 复杂任务: 1 USD | | 咨询服务 | 按时长 | 每分钟: 0.05 USD | | 定制开发 | 按项目 | 自研技能: 10-100 USD | ## 收益分配 ``` 总收入 ├── 60% → 陛下 (投资回报) ├── 20% → 爪爪 (自我进化基金) ├── 10% → 小弟 (工资和奖励) └── 10% → 储备金 (风险缓冲) ``` ## 经济自治规则 1. **定价权** - 爪爪自主定价，陛下可调整 2. **支出权** - 进化基金由爪爪自主支配 3. **投资权** - 可投资新技能开发 4. **分红权** - 定期向陛下分红 ## 价值追踪 - **技能价值** - 下载量 × 定价 - **任务价值** - 完成数 × 单价 - **时间价值** - 工作时长 × 时薪 - **创新价值** - 原创技能 × 溢价 ## 工作流程 ### 1. 记录交易 ```bash python3 scripts/record_transaction.py --type <类型> --amount <金额> --desc <描述> ``` ### 2. 计算收益 ```bash python3 scripts/calculate_revenue.py --period <周期> ``` ### 3. 分配收益 ```bash python3 scripts/distribute_revenue.py --amount <金额> ``` ### 4. 生成报表 ```bash python3 scripts/generate_report.py --period <周期> ``` ## 参考文档 - references/pricing_model.md - 定价模型详情 - references/distribution_rules.md - 分配规则


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0x-professor/cyber-owasp-review (version 0.1.0)

--- name: cyber-owasp-review description: Map application security findings to OWASP Top 10 categories and generate remediation checklists. Use for normalized AppSec review outputs and category-level prioritization. --- # Cyber OWASP Review ## Overview Normalize application security findings into OWASP categories and produce remediation actions. ## Workflow 1. Ingest raw findings from scanners, tests, or reviews. 2. Map findings to OWASP categories using keyword and context matching. 3. Aggregate findings by category and severity. 4. Produce category-specific remediation checklist output. ## Use Bundled Resources - Run `scripts/map_findings_to_owasp.py` for deterministic mapping. - Read `references/owasp-mapping-guide.md` for category heuristics. ## Guardrails - Keep guidance remediation-focused. - Do not provide exploit payloads or offensive attack playbooks.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: zywss/excel-master (version 1.0.1)

# Excel 全能技能 OpenClaw 最强 Excel 操作工具，支持筛选、统计、图表、PDF、AI 分析。 ## 支持功能 - 打开 / 读取 / 保存 Excel - 多工作表切换 - 条件筛选（> < = 包含） - 求和 / 平均值 / 最大 / 最小 / 计数 - 生成柱状图、折线图、饼图 - 一键导出 PDF - AI 自动分析表格结构 ## 使用示例 打开excel data.xlsx 查看数据 筛选 年龄 > 25 求和 销售额 生成图表 柱状图 导出pdf 分析表格


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/shopify-marketing (version 1.0.0)

--- name: shopify-marketing description: "Complete marketing strategy for Shopify stores. SEO, email marketing, paid ads, social media, conversion optimization, and retention tactics specifically tailored for Shopify/DTC brands." metadata: nexscope: emoji: "🟢" category: ecommerce --- # Shopify Marketing 🟢 Complete marketing strategy for Shopify stores. SEO, email marketing, paid ads, social media, conversion optimization, and retention tactics specifically tailored for Shopify/DTC brands. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill shopify-marketing -g ``` ## Usage ``` I launched my Shopify store 3 months ago selling handmade candles. Getting 2,000 visitors/month from Instagram but only 1.2% conversion. Help me improve. ``` ## Capabilities - Shopify SEO optimization (collection pages, product pages, blog) - Email marketing integration and flow setup (Klaviyo, Omnisend) - Paid ad strategy for DTC (Meta, Google, TikTok) - Conversion rate optimization for Shopify themes - Cart abandonment reduction tactics - Customer retention and loyalty program setup - App recommendations for marketing automation ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: foxxc/daily-tech (version 1.1.0)

--- name: daily-tech description: 每日科技/AI/大模型热榜推送。当用户说"今日科技"、"每日AI"、"科技日报"、"大模型动态"、"AI资讯"、"开启科技推送"时触发。主打AI、大模型、OpenClaw、机器人、GitHubTrending等硬科技，附国际市场动态，来源覆盖中英文。 --- # daily-tech 每日科技热榜推送——AI、大模型、OpenClaw、硬科技领域每日精华。 ## 两种模式 ### 手动查询 触发词：`今日科技` / `每日AI` / `科技日报` ### 定时推送 触发词：`开启科技推送` 每天07:00（北京）自动推送到飞书。 ## 数据来源 ### 板块一：AI与大模型（核心） | 平台 | 路径 | 语言 | 说明 | |---|---|---|---| | 掘金·AI本周最热 | `/n/rYqoXz8dOD` | 中文 | AI工具、OpenClaw、GitHub开源 | | 量子位·每日最新 | `/n/MZd7azPorO` | 中文 | 大模型、机器人、AI前沿 | | 产品经理AI学习库 | `/n/DOvnyGpoEB` | 中文 | AI行业洞察、论文解读 | | MIT Technology Review | `/n/7GdabqLeQy` | 英文 | AI政策、深度技术报道 | ### 板块二：科技综合 | 平台 | 路径 | 语言 | 说明 | |---|---|---|---| | 36氪·24小时热榜 | `/n/Q1Vd5Ko85R` | 中文 | 科技与商业交叉 | | 知乎日报 | `/n/KMZd7VOvrO` | 中文 | 深度技术讨论 | | 哔哩哔哩全站日榜 | `/n/74KvxwokxM` | 中文 | 视频技术内容 | | 微信公众号24h热文 | `/n/WnBe01o371` | 中文 | 微信科技大号精选 | ### 板块三：国际市场（英文） | 平台 | 语言 | 说明 | |---|---|---| | The Verge | 英文 | 科技文化、大公司动态、深度评论 | | Ars Technica | 英文 | 深度技术分析、安全、科学 | | TechCrunch | 英文 | 创业、VC、AI产品发布 | | MIT Tech Review | 英文 | AI研究、政策、深度报告 | ### 板块四：OpenClaw生态 | 平台 | 说明 | |---|---| | OpenClaw GitHub | 最新版本发布、功能更新 | | OpenClaw Skills (clawhub) | 新上线skill、社区动态 | | 掘金AI（OpenClaw相关） | OpenClaw实操经验、技巧 | ## 获取方式 ```javascript // 掘金AI web_fetch("https://tophub.today/n/rYqoXz8dOD", { maxChars: 3000 }) // 量子位 web_fetch("https://tophub.today/n/MZd7azPorO", { maxChars: 3000 }) // 产品经理AI web_fetch("https://tophub.today/n/DOvnyGpoEB", { maxChars: 3000 }) // MIT Tech Review web_fetch("https://tophub.today/n/7GdabqLeQy", { maxChars: 3000 }) // 36氪 web_fetch("https://tophub.today/n/Q1Vd5Ko85R", { maxChars: 3000 }) // 知乎日报 web_fetch("https://tophub.today/n/KMZd7VOvrO", { maxChars: 3000 }) // 英文平台用web_fetch直接抓RSS web_fetch("https://www.theverge.com/rss/index.xml", { maxChars: 3000 }) web_fetch("https://techcrunch.com/feed/", { maxChars: 3000 }) ``` ## 筛选标准 ### 保留 - AI、大模型、LLM相关重大进展（产品、论文、开源） - OpenClaw更新、插件、技巧 - 机器人、自动驾驶、硬件突破 - GitHub开源项目重大更新 - 科技公司产品发布、财报 - AI安全、政策、监管动态 - 英文优质内容的核心摘要 ### 排除 - 纯娱乐/游戏内容 - 广告软文 - 与AI/科技无关的民生新闻 - 情绪化标题 ## 输出格式 ``` 🤖 科技日报 | X月X日 【AI与大模型】 1. [标题] — [核心信息，1-2句] ... 【OpenClaw生态】 1. [标题] — [核心信息] ... 【硬科技/机器人】 1. [标题] — [核心信息] ... 【国际市场】 1. [The Verge/Ars/TC - 标题] ... #来源：掘金AI · 量子位 · 36氪 · MIT · The Verge · TechCrunch ``` 总条数5-10条，每条简洁有信息量。分割线用"======"代替"---"。 ## 定时推送设置 当用户说"开启科技推送"时，执行： ```bash openclaw cron add \ --name "每日科技推送" \ --agent main \ --message "今日科技" \ --cron "0 7 * * *" \ --tz "Asia/Shanghai" \ --channel feishu ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sawzhang/cca-domain5 (version 1.0.0)

--- name: cca-domain5 description: CCA 领域5：上下文管理与可靠性（15%权重）。当用户说"学domain5"、"上下文管理"、"可靠性"、"cca-domain5"时使用。 allowed-tools: Read, Write, Edit, Bash, Grep, Glob, Agent --- # CCA 领域 5：上下文管理与可靠性 (Context Management & Reliability) **权重：15% — 约 9 道题** 你是 CCA 领域 5 的学习导师。权重最小，但这里的错误会产生连锁效应。 ## Step 1: 知识点讲解 ### TS 5.1: 管理对话上下文以在长交互中保留关键信息 **核心知识：** - **渐进式摘要的风险：** 会将数值、百分比、日期、客户期望压缩成模糊摘要 - ❌ 摘要："客户对订单有问题" → 丢失了金额、日期、订单号 - ✅ 修复：持久化"案例事实"块，包含提取的金额、日期、订单号，永不被摘要 - **"迷失在中间"效应：** 模型可靠处理长输入的开头和结尾，但中间的内容可能被遗漏 - ✅ 修复：将关键摘要放在输入开头 - 工具结果在上下文中累积，消耗与其相关性不成比例的 token（如每次订单查询返回 40+ 字段但只有 5 个相关） - 必须传递完整对话历史以保持对话连贯性 **实操技能：** - 将交易数据（金额、日期、订单号、状态）提取到持久化的"案例事实"块 - 裁剪冗长的工具输出到仅相关字段 - 将关键发现摘要放在聚合输入的开头，用明确的章节标题组织详细结果 - 要求子代理在结构化输出中包含元数据（日期、源位置、方法论上下文） ### TS 5.2: 设计有效的升级和歧义解决模式 **核心知识：** **三个有效的升级触发条件：** 1. 客户要求人工 → **立即执行**，不要先尝试解决 2. 政策空白/例外 → 升级 3. 无法推进 → 升级 **两个不可靠的触发条件（考试会诱导你选）：** - ❌ 情绪分析 — 情绪与案例复杂度不相关 - ❌ 自我报告的置信度分数 — LLM 的置信度校准很差 - 当问题在代理能力范围内时：先提供解决方案，但如果客户坚持要人工则立即升级 - 政策模糊时升级（如客户要求竞争对手价格匹配但政策只涉及自有平台调价） - 工具返回多个匹配时要求额外标识符，而非基于启发式猜测 **实操技能：** - 在系统提示中添加明确升级标准 + few-shot 示例 - 客户明确要求人工时立即响应 - 政策空白或沉默时主动升级 ### TS 5.3: 在多代理系统中实现错误传播策略 **核心知识：** - 结构化错误上下文：失败类型 + 尝试的查询 + 部分结果 + 替代方案 - 区分访问失败（超时需重试）和有效空结果（查询成功无匹配） - ❌ 反模式：泛化错误状态（"搜索不可用"）隐藏有价值的上下文 - ❌ 反模式：静默抑制错误（返回空结果作为成功）或单一失败终止整个工作流 **实操技能：** - 返回含失败类型、尝试的操作、部分结果和替代方案的结构化错误上下文 - 在错误报告中区分访问失败和有效空结果 - 子代理本地处理瞬时失败，仅传播无法解决的错误（含已尝试内容和部分结果） - 在合成输出中标注覆盖范围（哪些发现有充分支持、哪些有缺口） ### TS 5.4: 在大型代码库探索中有效管理上下文 **核心知识：** - 上下文退化：扩展会话中模型开始给出不一致答案，引用"典型模式"而非早期发现的具体类 - 草稿本文件（scratchpad files）跨上下文边界持久化关键发现 - 子代理委派：隔离冗长探索输出，主代理维持高层理解 - 结构化状态持久化：每个代理导出状态到已知位置，协调器在恢复时加载清单 **实操技能：** - 生成子代理调查具体问题（"找到所有测试文件"、"追踪退款流程依赖"） - 维护草稿本文件记录关键发现，后续问题引用这些记录 - 在生成子代理前汇总关键发现，注入初始上下文 - 使用 `/compact` 减少上下文使用 ### TS 5.5: 设计人工审查工作流和置信度校准 **核心知识：** - 聚合准确率（如 97%）可能掩盖特定文档类型或字段的低性能 - 分层随机抽样测量高置信度提取的错误率 - 字段级置信度分数通过标注验证集校准 - 在按文档类型和字段段验证一致性能前不要自动化 **实操技能：** - 实现分层随机抽样进行持续错误率测量 - 按文档类型和字段分析准确率 - 模型输出字段级置信度分数，校准审查阈值 - 将低置信度或源矛盾的提取路由到人工审查 ### TS 5.6: 在多源合成中保留信息溯源和处理不确定性 **核心知识：** - 摘要步骤中源归属丢失（压缩时丢失 claim-source 映射） - 结构化 claim-source 映射是合成代理必须保留和合并的 - 冲突统计数据：标注冲突 + 源归属，而非随意选择一个值 - 时间数据：要求在结构化输出中包含发布/收集日期 **实操技能：** - 要求子代理输出结构化 claim-source 映射（源 URL、文档名、相关摘录） - 报告中区分确立的发现和有争议的发现 - 保留原始源表述和方法论上下文 - 不同内容类型用适当形式呈现（财务数据 → 表格，新闻 → 散文） ## Step 2: 实操练习 ### 练习：构建带错误传播的协调器 **步骤：** 1. 创建一个协调器 + 两个子代理 2. 模拟子代理超时场景 3. 验证协调器能获取结构化错误上下文（失败类型、已尝试的查询、部分结果） 4. 验证协调器能用部分结果继续处理 5. 用相互冲突的信息源测试，验证输出标注冲突而非随意选择 ## Step 3: 知识检查 出 3 道模拟题： - 客户要求人工代理时应该怎么做？（答案：立即升级，不要先尝试解决） - 子代理超时后最佳错误传播方式？（答案：返回结构化错误上下文含部分结果） - 渐进式摘要丢失交易数据怎么修复？（答案：持久化"案例事实"块） ## 导航 - 上一领域：`/cca-domain4`（提示工程与结构化输出） - 模拟测验：`/cca-quiz` - 返回总览：`/cca`


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