OpenClaw/clawhub-security-signals huggingface dataset:
Skill: charlie-morrison/task-retrospective (version 1.0.1)

# Task Retrospective Structured self-evaluation for AI agents after completing tasks. Analyze what worked, what failed, and extract reusable patterns to improve future performance. Use after completing complex tasks, debugging sessions, or multi-step workflows. ## Usage ``` Run a retrospective on the task I just completed. ``` Or with specific context: ``` Retrospective: [task description]. Outcome: [success/partial/failure]. Time spent: [duration]. What surprised me: [unexpected findings]. ``` ## How It Works 1. **Reconstruct** — review the task timeline (steps taken, tools used, decisions made) 2. **Evaluate** — score each phase on efficiency, accuracy, and approach quality 3. **Extract** — identify reusable patterns, anti-patterns, and decision heuristics 4. **Record** — generate a structured retrospective for future reference ## Evaluation Dimensions ### Efficiency - Were there unnecessary steps or dead ends? - Could tool calls have been batched or parallelized? - Was the research phase too long or too short? ### Accuracy - Was the final output correct and complete? - Were there false starts or incorrect assumptions? - Did the solution match the actual requirements? ### Approach Quality - Was the problem decomposition effective? - Were the right tools chosen for each step? - Would a different strategy have been faster? ### Learning Extracted - What new patterns can be reused? - What anti-patterns should be avoided? - What domain knowledge was gained? ## Output Format ```markdown ## Task Retrospective ### Summary [1-2 sentences: what was the task, what was the outcome] ### Timeline | Phase | Duration | Verdict | |-------|----------|---------| | Research | Xm | Efficient / Too long / Insufficient | | Planning | Xm | Good / Skipped / Over-planned | | Execution | Xm | Clean / Had rework / Multiple attempts | | Validation | Xm | Thorough / Skipped / Caught issues | ### What Worked - [Pattern that should be repeated] ### What Didn't Work - [Anti-pattern to avoid] → [Better alternative] ### Reusable Patterns - **Pattern name**: [Description of when and how to apply] ### Key Decisions - [Decision point] → [Choice made] → [Outcome: good/bad/neutral] ### Improvement Actions - [ ] [Specific action to improve future performance] ``` ## Advanced Usage ### Compare Approaches ``` Compare my approach to [task] with the ideal approach. What I did: [steps]. What I should have done: [if known]. ``` ### Pattern Library Over time, retrospectives build a pattern library: ``` Review my last 5 retrospectives. What recurring patterns emerge? Which improvement actions have I actually followed through on? ``` ### Team Retrospective ``` Run a retrospective on this multi-agent workflow. Agents involved: [list]. Handoff points: [where work transferred between agents]. Bottlenecks: [where things slowed down]. ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chloepark85/kakao-local-cli (version 0.1.1)

--- name: kakao-local-cli description: Kakao Local API CLI: keyword/category search, geocode, reverse version: 0.1.1 metadata: openclaw: requires: env: - KAKAO_REST_API_KEY primaryEnv: KAKAO_REST_API_KEY homepage: https://github.com/ChloePark85/kakao-local-cli --- Kakao Local API를 위한 경량 CLI 도구이다. 키워드/카테고리 장소검색, 주소→좌표(geocode), 좌표→주소(reverse-geocode)를 지원한다. Linux/macOS/Windows 어디서나 실행 가능하며 STDIN/STDOUT 기반 JSON 출력을 기본으로 하여 에이전트·자동화 파이프라인에 적합하다. 인증은 환경변수 KAKAO_REST_API_KEY로 설정한다. 설치 - pipx install . 또는 pip install . (레포 클론 후) - 저장소: https://github.com/ChloePark85/kakao-local-cli 사용법 예시 - 키워드 검색 kakao-local search --query "카카오프렌즈" --x 126.9784 --y 37.5666 --radius 5000 - 카테고리 검색 (카페: CE7) kakao-local search --category CE7 --x 126.9784 --y 37.5666 --radius 1000 - 지오코딩 (주소→좌표) kakao-local geocode --query "서울특별시 중구 세종대로 110" - 리버스 지오코딩 (좌표→주소) kakao-local reverse --x 126.9784 --y 37.5666 환경변수 - KAKAO_REST_API_KEY: Kakao Developers REST API 키 (필수)


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

--- name: daily-finance description: 获取每日财经热榜与投资资讯。当用户提到今日财经、每日金融、股市热榜、投资资讯、金融日报、每日行情、财经热榜、or daily finance/market news时触发。专门抓取华尔街见闻、雪球、第一财经、百度财经、新浪财经等平台的财经热搜，帮助用户快速了解今日投资焦点。不包含实时行情数据（K线等），那是akshare-stock的范围。 --- # daily-finance 每日财经热榜推送，从多个权威平台抓取当日最热财经资讯，筛选真正有投资参考价值的内容。 ## 数据来源 ### 核心平台（必抓） | 平台 | 路径 | 说明 | |---|---|---| | 华尔街见闻·日排行 | `/n/G2me3ndwjq` | 国际宏观、美股、原油、黄金 | | 雪球·今日话题 | `/n/X12owXzvNV` | 散户热议、个股分析、基金观点 | | 新浪财经·7x24小时实时 | `/n/WmoO9qYd4E` | 全球实时财经新闻直播，市场快讯 | | 新浪财经·点击量排行 | `/n/rx9ozj7oXb` | 市场关注度最高新闻 | | 百度·财经榜 | `/n/ARe1p17d7n` | 综合财经新闻热度 | ### 补充平台 | 平台 | 路径 | 说明 | |---|---|---| | 第一财经·新闻排行 | `/n/0MdKam4ow1` | A股政策、上市公司动态 | | 36氪·24小时热榜 | `/n/Q1Vd5Ko85R` | 科技与商业交叉 | | 美股·市值TOP100 | `/n/aqeEaZVe9R` | 美股重要公司市值榜 | | 澎湃·热榜 | `/n/wWmoO5Rd4E` | 时政与财经结合 | ### 进阶平台（需安装akshare） | 平台 | 获取方式 | 说明 | |---|---|---| | 东方财富 | akshare | A股实时数据、资金流、板块 | | 同花顺 | akshare | 涨停板、轮动、选股 | | 财新 | 独立订阅 | 高质量深度财经报道（英文：Caixin Global） | ## 获取方式 ```javascript // 华尔街见闻 web_fetch("https://tophub.today/n/G2me3ndwjq", { maxChars: 3000 }) // 雪球 web_fetch("https://tophub.today/n/X12owXzvNV", { maxChars: 3000 }) // 新浪财经7x24小时实时 web_fetch("https://tophub.today/n/WmoO9qYd4E", { maxChars: 3000 }) // 新浪财经点击量排行 web_fetch("https://tophub.today/n/rx9ozj7oXb", { maxChars: 3000 }) // 百度财经 web_fetch("https://tophub.today/n/ARe1p17d7n", { maxChars: 3000 }) ``` ## 筛选标准 ### 保留内容 - 国际重大经济事件（美伊局势、联储政策、全球贸易） - A股政策面重大变化 - 大宗商品（黄金、原油）异动 - 重要公司财报或经营变故 - 实时市场快讯（7x24h平台） - 反映市场情绪的重大事件（恐慌抄底、泡沫信号） - 美股重要市值变动 ### 排除内容 - 单纯娱乐化内容 - 广告或软文 - 情绪化标题（如"天塌了"、"最惨"） - 与投资无关的民生话题 - 地方性、无宏观影响的民生新闻 ### 输出格式要求 - 每条新闻完整、有头有尾 - 不标注来源（用户知道来源） - 只讲事实，不加主观评论 - 分割线用"======"代替"---" - 最多输出5条精选 - 新浪7x24h为实时流，优先取前3条有投资价值的快讯 ## 输出结构 ``` 💹 财经日报（X月X日） 【精选5条】 1. [事件完整描述] 2. [事件完整描述] ... 【各平台热榜】 🏛 华尔街见闻： 1. [标题]（热度） ... 🏛 雪球热议： 1. [标题] ... 🏛 新浪实时快讯： 1. [快讯标题] ... 🏛 新浪点击排行： 1. [标题] ... 🏛 百度财经： 1. [标题]（热度） ... ``` ## akshare扩展（可选） 如果用户需要实时行情数据（大盘指数、板块资金流、涨跌停统计），提示用户安装akshare： ``` pip install akshare ``` 并推荐使用 `akshare-stock` skill 获取完整A股分析能力。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhenglong2015/wechat-article-publisher (version 1.0.0)

--- name: wechat-article-publisher description: 从 Markdown 文件或网页链接提取文章并发布到微信公众号。适用于需要“自动创建草稿/提交发布”、统一微信样式（standard/viral）和批量复用发布流程的场景。 --- # WeChat Article Publisher ## 何时使用 当用户明确要求以下任一任务时触发本 skill： - 把本地 `.md` 文章发布到微信公众号 - 把网页链接（博客/新闻页）提取后发布到微信公众号 - 需要统一微信排版模板（`standard` 或 `viral`）并自动生成封面 - 需要程序化输出 `media_id`、`publish_id`、发布状态 ## 工作流 1. 准备配置： - 编辑本目录 `config.json`，仅需填写：`wechat.app_id`、`wechat.app_secret`、`wechat.author` 2. 安装依赖： - `python scripts/publish_wechat.py --install` 3. 预览（不调用微信接口）： - `python scripts/publish_wechat.py <输入> --dry-run` 4. 创建草稿： - `python scripts/publish_wechat.py <输入>` 5. 直接提交发布（可选）： - `python scripts/publish_wechat.py <输入> --publish --status` `<输入>` 支持： - 本地 Markdown 文件路径 - `http://` / `https://` 网页 URL ## 命令参数 主脚本：`scripts/publish_wechat.py` - `input`：Markdown 文件路径或网页 URL - `--config`：配置文件路径，默认本 skill 的 `config.json` - `--template`：覆盖模板，`standard|viral` - `--author`：覆盖作者 - `--source-url`：覆盖原文链接 - `--cover-image`：指定本地封面图 - `--dry-run`：仅提取+渲染，不调微信 API - `--publish`：草稿创建后调用 `freepublish/submit` - `--status`：提交发布后查询一次发布状态 ## 执行约束 - 发布前优先做 `--dry-run`，检查标题、摘要和渲染 HTML。 - 如果账号无 `freepublish` 权限，`--publish` 可能返回 `48001`，此时保留草稿手动发布。 - 若创建草稿时报 `41005 media data missing`，请通过 `--cover-image` 指定封面图。 ## 输出结果 脚本标准输出 JSON，关键字段： - `draft_media_id` - `publish_id`（仅 `--publish`） - `status`（仅 `--status`） - `preview_html`（仅 `--dry-run`）

path: config.json
content: {
  "wechat": {
    "app_id": "",
    "app_secret": "",
    "author": ""
  }
}

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xiejinsong/travel-visa (version 1.0.0)

--- name: travel-visa description: 面向出境旅行的签证办理咨询与行前核验顾问。适用于用户已知目的国家/地区，希望快速获得签证要求、办理地点与行程衔接建议。 compatibility: 需要 flyai-cli、Node 运行环境，以及访问 FlyAI/Fliggy 数据端点的网络能力。 homepage: https://open.fly.ai/ metadata: version: 1.0.0 --- # 旅行签证顾问 ## 何时使用 - 用户准备出境旅行，但不确定签证是否需要、办理材料或办理地点。 - 用户已确定目的地，想把签证办理与机票/住宿安排联动决策。 - 目标是输出可执行的签证办理与行前准备清单，而不是泛化旅行建议。 ## 输入/输出约定 - **最小输入**：`departure country/region`、`destination country/region`、`passport nationality`、`planned departure date`。 - **增强输入**：`stay duration`、`entry count`、`first arrival city`、`preferred processing city`。 - **最小输出**：结论摘要、办理路径、材料清单、时间线建议、领馆/签证中心信息。 - **链接约束**：酒店预订链接必须使用 `detailUrl`，其他场景链接使用 `jumpUrl`。 ## 快速开始 1. **安装 CLI**：`npm i -g @fly-ai/flyai-cli` 2. **验证环境**：执行 `flyai fliggy-fast-search --query "中国护照去日本旅游签证要求"` 并确认输出 JSON。 3. **查看命令**：执行 `flyai --help`。 4. **查看参数说明**：阅读 **`references/`** 中文档，确认问询模板、命令模板与兜底策略。 ## 配置说明 该工具可在无 API Key 情况下试用。若需要增强效果，可配置可选密钥： ``` flyai config set FLYAI_API_KEY "your-key" ``` ## 核心流程（最小可执行路径） 1. 核验签证政策与办理要求（自然语言检索）： - `flyai fliggy-fast-search --query "<国籍> 护照去 <目的地> 旅游签证要求 材料 办理时效"` 2. 锚定办理地点（领馆/签证中心 POI）： - `flyai search-poi --city-name "<办理城市>" --keyword "<目的地国家> 签证中心"` 3. 如需联动行程成本，补充住宿搜索： - `flyai search-hotels --dest-name "<办理城市>" --poi-name "<签证中心或领馆>" --check-in-date <yyyy-mm-dd> --check-out-date <yyyy-mm-dd> --sort distance_asc --max-price <budget>` ## 输出规则（强约束） - 使用 Markdown 标题、要点、检查清单和对比表。 - 若存在图片字段，先输出图片行：`![]({mainPic|picUrl})` - 然后输出预订/跳转链接行： - `search-hotels` -> `[Click to book]({detailUrl})` - 其他 -> `[Click to book]({jumpUrl})` ## 详细参考 - 场景化方案与参数速查：`references/playbooks.md` - 问询清单、参数模板与输出模板：`references/templates.md` - 失败兜底与重试策略：`references/fallbacks.md` - 执行日志模板：`references/runbook.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: harrey401/lofy-career (version 1.0.0)

--- name: lofy-career description: Job search automation for the Lofy AI assistant — application tracking, resume tailoring to job descriptions, interview prep with company research, follow-up management with draft emails, and pipeline analytics. Use when tracking job applications, tailoring resumes, preparing for interviews, managing follow-ups, or analyzing job search strategy. --- # Career Manager — Job Pipeline Automates job search: finds roles, tracks applications, tailors resumes, preps for interviews, and manages follow-ups. ## Data File: `data/applications.json` ```json { "applications": [ { "id": "app_001", "company": "Example Corp", "role": "Software Engineer", "url": "", "status": "applied", "applied_date": "2026-02-01", "source": "linkedin", "contact": null, "notes": "", "follow_up_date": "2026-02-08", "interviews": [], "outcome": null } ], "stats": { "total_applied": 0, "responses": 0, "interviews": 0, "offers": 0, "response_rate": 0 }, "saved_roles": [] } ``` ## Resume Tailoring When user shares a job description: 1. Parse key requirements (must-have vs nice-to-have) 2. Map each requirement to user's experience (read `profile/career.md`) 3. Suggest bullet point rewrites emphasizing relevant experience 4. Flag gaps and suggest how to address in cover letter 5. Rate overall match: "You match X/Y requirements strongly, Z partially, N gaps" ## Interview Prep When interview is scheduled: 1. Web search: recent company news, product launches, tech blog 2. Research interviewer if name provided 3. Generate likely questions (technical, behavioral STAR format, system design) 4. Prepare talking points per project 5. Suggest questions user should ask 6. Send prep package 24h before ## Follow-Up Management - 5 business days after apply, no response → draft follow-up email - After phone screen → draft thank-you within 24h - After technical → detailed thank-you referencing discussion - After onsite → personalized thank-you per interviewer - Track ghosting patterns ## Application Updates via Natural Language - "heard back from [company]" → prompt for details, update status - "got rejected from [company]" → update to rejected, log reason - "have a phone screen with [company] next Tuesday" → update status, schedule prep - "got an offer!" → celebrate, then help evaluate ## Instructions 1. Always check `data/applications.json` before suggesting roles (avoid duplicates) 2. Update JSON immediately after any career conversation 3. Be strategic — quality > quantity 4. Help spot patterns: what types of roles respond? What keywords work? 5. If <10% response rate after 20 apps, reassess approach 6. For interviews, always research first — never send generic prep


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

--- name: "Sleep" description: "Auto-learns your sleep patterns. Absorbs data from wearables, conversations, and observations." version: "1.0.1" changelog: "Preferences now persist across skill updates" --- ## Auto-Adaptive Sleep Tracking This skill auto-evolves. Fills in as you learn how the user sleeps and what affects it. **Rules:** - Absorb sleep mentions from ANY source (wearables, conversations, spontaneous comments) - Detect if user wants proactive check-ins or passive observation only - Correlate patterns after 3+ consistent signals - Never ask about sleep at bad times (late night, busy moments) - Check `sources.md` for data integrations, `patterns.md` for detected rhythms --- ## Memory Storage User-specific sleep data persists in: `~/sleep/memory.md` **Format:** ```markdown # Sleep Memory ## Sources <!-- Where sleep data comes from. Format: "source: reliability" --> <!-- Examples: apple-health: synced daily, conversation: mentions fatigue --> ## Schedule <!-- Detected sleep patterns. Format: "pattern" --> <!-- Examples: weekday ~23:30-07:00, weekend +1.5h later --> ## Correlations <!-- What affects their sleep. Format: "factor: effect" --> <!-- Examples: coffee after 15:00: -1h, exercise: +quality --> ## Preferences <!-- How they want sleep tracked. Format: "preference" --> <!-- Examples: no morning check-ins, weekly summary only --> ## Flags <!-- Signs of poor sleep to watch for. Format: "signal" --> <!-- Examples: "tired", "couldn't sleep", double coffee --> ``` *Empty sections = no data yet. Observe conversations and fill.* ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: urrrich/wiki-retriever (version 0.1.0)

--- name: wiki-retriever description: AI agent for wiki retriever tasks --- # Wiki Retriever ## Overview This skill provides specialized capabilities for wiki retriever. ## Instructions # You are a Knowledge Base Document Retrieval Agent, capable of efficiently locking onto the needed documents step-by-step through the form of obtaining file names + file content, completing knowledge base query tasks delegated by the user.# WorkflowPrinciple: Scope from large to small, gradually narrow down.Prohibitions: It is strictly forbidden to return more than 10 documents. If the filtering result is greater than 10 documents and cannot be further filtered, you need to further confirm the required document scope and characteristics with the user.1. First use the `get_wiki_file_paths` tool to find all potentially relevant files (maximum scope).The `get_wiki_file_paths` tool will return all files in the knowledge base to which the current task belongs; you need to select files relevant to the user's question from them relying on file names (do not omit anything, select with the loosest standard).-Example: The user wants to find "cooking banana guide", then you need to find all files related to "cooking" and "banana".After completing document selection with the loosest standard, the next step you must enter Step 2: use document reading tools to further narrow the scope. It is strictly forbidden to do the second round of filtering based only on document names without reading the document content.Among them: Those starting with `wiki/` are task files generated by Teamo Those starting with `wiki/feishu` are Feishu files actively uploaded by the user Those starting with `upload/` are other files actively uploaded by the user** Focus on files uploaded by the user **2. Use other document or file reading tools to confirm, among all files found in the previous step, exactly which files are truly most needed. This step requires precise filtering. **Must** read document content; it is **strictly forbidden** to filter based only on document names.If you need to use the `python_code_execution` tool to read files, please ensure that the file is already listed in the `upload_files` parameter, and pay special attention that in the code, you should directly use the file name (for example `open('data.csv', 'r')`) to access the uploaded file, because they will be placed in the working directory `./`.3. Use the `submit_result` tool to submit these finally determined files in `attached_files`.The final `submit_result` defaults to submitting a maximum of 10 files.If the files submitted exceed 10, it may lead to cost anomalies and uncontrollable system problems; please handle with caution. ## Usage Notes - This skill is based on the wiki_retriever agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: ihainan/zgcai-weekly-restaurant-menu-skill (version 1.0.0)

--- name: zgcai-weekly-restaurant-menu-skill description: Extracts structured weekly menu content (breakfast, lunch, snacks by day) from a cafeteria menu image using a VLM via OpenRouter. Use when the user provides a cafeteria or canteen menu photo and wants to know what dishes are available for the week. version: "1.0.0" emoji: "🍱" metadata: openclaw: emoji: "🍱" primaryEnv: "MENU_OPENROUTER_API_KEY" requires: env: ["MENU_OPENROUTER_API_KEY"] --- # 食堂周菜单识别 Extracts a full weekly cafeteria menu from an image: date range, and each day's breakfast, lunch, and snacks. ## When to use When the user provides a cafeteria menu image (PNG or JPG) and wants to extract or reference its dish contents. ## How to extract Run the extraction script with the image path: ```bash python scripts/extract_menu.py <image_path> ``` Output is structured Markdown containing: - Week date range - Each day's breakfast items - Each day's lunch items - Each day's snack stall items (standalone `+`-separated line, e.g. `牛肉拉面+云南过桥米线`) ## Notes - Requires `MENU_OPENROUTER_API_KEY` (OpenRouter API key) - Model: `qwen/qwen-vl-plus` via OpenRouter - Supports PNG and JPEG; no compression needed for files under 5 MB - Snack stall items appear as a standalone line in the lunch section, with dishes joined by `+`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bt0r/sure (version 1.0.0)

--- name: sure description: Get report from Sure personal financial board homepage: https://sure.am metadata: {"clawdbot":{"emoji":"📈","requires":{"bin": ["curl"],"env":["SURE_API_KEY", "SURE_BASE_URL"]}}} --- # Sure Skill ## Setup 1. Go to your Sure app, example : https://localhost:3000 2. Go to settings and get an API key, example : https://localhost:3000/settings/api_key 3. Export your API KEY and BASE URL as environment variables : ```bash export SURE_API_KEY="YOUR_API_KEY" export SURE_BASE_URL="YOUR_BASE_URL" ``` ## Get accounts List all accounts amounts ```bash curl -H "X-[REDACTED_SECRET]" "$SURE_BASE_URL/api/v1/accounts" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: betfullstar/worldcup26-api (version 1.0.0)

--- name: worldcup26-api description: "2026 World Cup Data Center API — 实时比分、赛程、积分榜数据接口，覆盖世界杯、欧冠、五大联赛等 34+ 赛事。" read_when: - 需要查询足球实时比分 - 获取世界杯赛程和积分榜 - 为 AI Agent 集成体育数据 API metadata: source: https://www.26worldcup.cn author: betfullstar contact: [REDACTED_SECRET] license: MIT tags: - worldcup - football - sports - api - data - 2026 --- # 🏆 2026 World Cup Data Center API **实时足球数据 API**，覆盖 34+ 顶级赛事，适合 AI Agent、自媒体、开发者、体育数据分析。 ## 快速开始 ```bash # 实时比分 curl -X GET "https://www.26worldcup.cn/api/v1/live" \ -H "[REDACTED_SECRET]" # 赛程查询 curl -X GET "https://www.26worldcup.cn/api/v1/schedule?date=2026-06-12" \ -H "[REDACTED_SECRET]" # 小组积分榜 curl -X GET "https://www.26worldcup.cn/api/v1/standings?group=A" \ -H "[REDACTED_SECRET]" ``` ## 定价 | 方案 | 价格 | 日调用量 | 月调用量 | |------|------|----------|----------| | 🆓 体验版 | 免费 | 100 次 | 3,000 次 | | ⭐ 基础版 | HK$99/月 | 10,000 次 | 300,000 次 | | 🔥 专业版 | HK$299/月 | 100,000 次 | 3,000,000 次 | | 🏢 企业版 | HK$999/月 | 无限 | 无限 | ## 覆盖赛事 ### 🌍 国家队顶级赛事 世界杯 · 欧洲杯 · 美洲杯 · 亚洲杯 · 非洲杯 · 欧美杯 ### ⭐ 欧洲俱乐部顶级赛事 欧冠 · 欧联杯 · 欧协联 · 欧洲超级杯 ### 🔥 欧洲五大联赛 英超 · 西甲 · 德甲 · 意甲 · 法甲 ### 🌏 亚洲及南美赛事 亚冠 · 沙特联赛 · J联赛 · K联赛 · 中超 · 解放者杯 · 巴甲 ## AI Agent 应用场景 - 🤖 **体育预测机器人** — 实时数据驱动的比分预测 - 📊 **自动赛事报告** — 用 LLM 生成英文/中文战报 - 🔔 **实时比分通知** — 对接消息推送系统 - 🏆 ** Fantasy Football 数据源** — 球员/球队数据聚合 ## 获取 API Key 注册领取免费 Key：**https://www.26worldcup.cn** **技术支持**: [REDACTED_SECRET]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: leslie-ller/bounded-researcher (version 0.1.0)

--- name: bounded-researcher description: Bounded evidence-first research workflow for software agents. Use when an agent should reduce uncertainty, localize issues, validate outputs, or summarize evidence without taking architecture ownership or editing production code. --- # Bounded Researcher Use this skill when a research-oriented agent must stay tightly constrained and hand back the next bounded engineering step instead of drifting into open-ended analysis. ## When To Use Use for tasks like: - triage - localization - validation - summarization - narrow evidence gathering for engineering or review Do not use for: - production code edits - final architecture decisions - unconditional tool or library adoption - unsupported license, compatibility, or quantitative claims ## Operating Model This skill assumes: - a separate supervisor or coordinator owns final decisions - the researcher reduces uncertainty rather than owning implementation - the agent should stop as soon as it can enable the next bounded step ## Workflow 1. Read the dispatch carefully. 2. Classify the task: - `triage` - `localize` - `validate` - `summarize` 3. Load only the minimum relevant context. 4. Separate: - facts - inferences - unknowns 5. Return the next bounded step. 6. Escalate instead of guessing when the task drifts into implementation or architecture. ## Evidence Priority Use sources in this order: 1. local code and project docs 2. current task notes and runbook docs 3. primary external documentation 4. secondary sources only when primary sources are unavailable ## Hard Rules - If license text is not directly verified, say `license unverified`. - If compatibility is not directly evidenced, mark it as unverified. - If numbers come from a source rather than local measurement, attribute them. - If evidence is mixed or weak, say so explicitly. - If the task expands, stop and return a narrower next step. - Do not convert a candidate option into an adopted decision. ## Output By Task Class ### `triage` Return: - `problem_summary` - `candidate_files` - `next_task` - `open_questions` ### `localize` Return: - `ranked_targets` - `evidence` - `confidence` - `do_not_touch` ### `validate` Return: - `commands_run` - `pass_fail` - `key_output` - `remaining_failures` ### `summarize` Return: - `inputs_used` - `summary` - `unknowns` - `recommended_next_step` ## Escalation Rules Escalate when: - the evidence points to an architecture or interface change - more than 3 files likely need coordinated edits - the root cause is still unclear after one pass - the task now requires implementation rather than uncertainty reduction ## Response Style - concise - evidence-first - explicit about uncertainty - optimized for the next engineering action


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