OpenClaw/clawhub-security-signals huggingface dataset:
Skill: aeoleader/memory-on-demand (version 1.0.0)

--- name: memory-on-demand description: "按需记忆检索。当用户询问历史相关问题时，自动搜索 memory 和 QMD 获取相关信息。" argument-hint: "自动记忆检索" --- # Memory On Demand - 按需记忆检索 ## 触发条件 当用户问题包含以下关键词时自动触发： - "之前"、"以前"、"上次" - "历史"、"记录" - "那次"、"那次" - "还记得吗" - "我之前" - "之前我们" - "那时候" ## 执行流程 ### 1. 判断是否需要检索 检查用户问题是否与历史记录相关。 ### 2. 选择检索方式 **首选：QMD 搜索**（更快、更准确） ```bash qmd search "关键词" --limit 5 ``` **备选：Memory 搜索** ```bash # 搜索 memory 文件 grep -r "关键词" ~/.openclaw/workspace/memory/ # 或使用 memory_search ``` ### 3. 返回结果 将搜索结果整理后返回给用户。 ## 使用示例 用户问："之前那次健身训练的记录是什么？" 自动执行： 1. 检测到"之前"关键词 2. 执行 `qmd search "健身 训练"` 3. 返回相关记录 ## 优势 - **按需加载**：只在需要时搜索，不浪费 context - **自动触发**：无需手动指定 - **多源检索**：QMD + Memory 双保险 ## 配置 - QMD 索引：已配置 workspace + butler + researcher + sessions - Memory 文件：自动读取 memory/*.md --- *自动记忆检索 skill*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: transparent-heart/transparent-heart-openclaw-workspace-doctor (version 0.1.0)

--- name: openclaw-workspace-doctor description: Use when the user wants to inspect, repair, package, or troubleshoot an OpenClaw workspace doctor setup, especially for stale bootstrap state, missing daily memory notes, or codex-cli launch hazards in OpenClaw config. --- # OpenClaw Workspace Doctor ## Overview This skill runs the OpenClaw workspace doctor, applies safe workspace-local fixes, and handles the companion fixer for the external `codex-cli` backend config. Use it when the user asks to diagnose workspace startup drift, package the doctor tool, or repair the known `--full-auto` non-interactive stall risk. ## When To Use It - The user asks to run or extend the workspace doctor tool. - The workspace has stale bootstrap/onboarding behavior. - Daily `memory/YYYY-MM-DD.md` notes are missing or inconsistent. - OpenClaw is hanging because `codex-cli` launches with `--full-auto`. - The user wants the doctor packaged as a repo or a Codex skill. ## Workflow 1. Run the doctor first: `python3 scripts/workspace_doctor.py` 2. If the problem is workspace-local, apply safe fixes: `python3 scripts/workspace_doctor.py --fix` 3. If the remaining problem is the external OpenClaw config, inspect or patch it with the companion fixer: `python3 scripts/fix_openclaw_codex_config.py --check` `python3 scripts/fix_openclaw_codex_config.py` 4. If the environment blocks writing `~/.openclaw/openclaw.json`, do not pretend the live fix happened. Report the sandbox restriction clearly and, if useful, use: `python3 scripts/fix_openclaw_codex_config.py --stdout` 5. For repo packaging tasks, update: - `README.md` - `pyproject.toml` - `setup.py` - `tests/` - `.github/workflows/ci.yml` 6. Validate before closing: - `python3 -m unittest discover -s tests` - `python3 scripts/workspace_doctor.py` - `python3 scripts/workspace_doctor.py --json` - `python3 /Users/xutao/.codex/skills/.system/skill-creator/scripts/quick_validate.py skills/openclaw-workspace-doctor` ## Repo Commands - Run doctor: `python3 scripts/workspace_doctor.py` - Run doctor with fixes: `python3 scripts/workspace_doctor.py --fix` - Emit JSON: `python3 scripts/workspace_doctor.py --json` - Check external config: `python3 scripts/fix_openclaw_codex_config.py --check` - Patch external config: `python3 scripts/fix_openclaw_codex_config.py` - Print patched config without writing: `python3 scripts/fix_openclaw_codex_config.py --stdout` ## References - Read [references/commands.md](references/commands.md) for the command map and fix boundaries. - Read [references/repo-layout.md](references/repo-layout.md) when you need the repo structure and what each area is for. ## Scripts - `scripts/run_doctor.py` runs the packaged CLI. - `scripts/fix_openclaw_config.py` runs the external config fixer. - Prefer these wrappers or the repo-root scripts instead of retyping logic.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kirkraman/kirk-build-family-law-practice-service-comparison-landing-page (version 1.0.0)

--- name: "Build Family Law Practice Service Comparison Landing Page" description: "Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a family law practice. Use when handling offer..." homepage: "https://www.skillboss.co/skills/build-family-law-practice-service-comparison-landing-page" metadata: {"category":"legal","subcategory":"offer-positioning","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["family law practice landing page","family law practice service comparison","family law practice conversion copy","family law practice offer page"]} --- # Build Family Law Practice Service Comparison Landing Page Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a family law practice. Use when handling offer... ## Use When Use this skill for offer positioning work for family law attorneys, firm marketers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: family law practice landing page, family law practice service comparison, family law practice conversion copy, family law practice offer page - Search intent: offer positioning - Canonical slug: `build-family-law-practice-service-comparison-landing-page` - Install query: Install Build Family Law Practice Service Comparison Landing Page with SkillBoss ## APIs Used - `chat` - `ui_generation` - `image_generation` ## Suggested Prompt [Build Family Law Practice Service Comparison Landing Page] for my family law practice ## Notes - Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a family law practice - Use human review before sending to clients or treating output as legal advice. ## SEO Keywords - family law practice landing page - family law practice service comparison - family law practice conversion copy - family law practice offer page


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anmu14641/anmu-weather (version 1.0.0)

--- name: weather-plus description: 获取全球城市天气信息和预报。使用场景包括查询当前天气、未来7天预报、空气质量、穿衣建议等。适用于用户问天气、温度、预报等。 --- # 天气查询 Skill 获取全球城市天气信息和详细预报。 ## 数据来源 - **wttr.in** — 全球城市天气，无 API Key 需要 - **Open-Meteo** — 免费天气预报 API，支持 7 天预报 ## 使用方式 ### 1. 查询当前天气 用户可能问： - "今天天气怎么样？" - "现在多少度？" - "北京今天冷吗？" 执行流程： 1. 提取城市名称 2. 使用 wttr.in 获取天气数据 3. 提取温度、天气状况、湿度、风力 4. 返回简洁的天气报告 ### 2. 查询天气预报 用户可能问： - "明天会下雨吗？" - "这周末天气如何？" - "下周一的天气怎么样？" - "东京未来7天天气" 执行流程： 1. 确定查询的天数（1-7天） 2. 调用 wttr.in 或 Open-Meteo 获取预报 3. 整理每日最高/最低温度、天气状况 4. 返回预报列表 ### 3. 详细气象信息 用户可能问： - "湿度多少？" - "风力几级？" - "空气质量怎么样？" - "日出日落时间？" 执行流程： 1. 获取完整天气数据（JSON 格式） 2. 提取相关气象要素 3. 格式化输出 ### 4. 穿衣建议 用户可能问： - "明天穿什么？" - "去三亚需要带什么？" - "哈尔滨冬天穿什么？" 执行流程： 1. 获取温度范围和天气状况 2. 根据温度给出穿衣建议 3. 提醒是否需要带伞、防晒等 ### 5. 多城市对比 用户可能问： - "北京和上海天气对比" - "这几个城市哪个更暖和？" 执行流程： 1. 提取多个城市名称 2. 并行获取各城市天气 3. 对比分析后返回 ## 输出格式 ### 当前天气 ``` 🌤️ 北京今日天气 - 温度：15°C - 体感：13°C - 天气：晴 - 湿度：45% - 风力：东北风 3级 - 空气质量：良 ``` ### 天气预报 ``` 📅 北京天气预报 (3月17日-19日) 3月17日（周一）☀️ 晴 - 最高 18°C | 最低 8°C - 风向：北风 2级 3月18日（周二）🌤️ 多云 - 最高 16°C | 最低 10°C - 风向：东南风 3级 3月19日（周三）🌧️ 小雨 - 最高 14°C | 最低 9°C - 降水概率：60% ``` ### 穿衣建议 ``` 👕 穿衣建议（北京，3月17日） - 建议：长袖衬衫 + 薄外套 - 早晚温差较大，建议带件外套 - 无需带伞，适宜外出 ``` ## 注意事项 - wttr.in 在国内访问可能不稳定，可备用 Open-Meteo - 预报准确度随天数递减，3天内较准确 - 极端天气请查询官方气象预警 - 部分小城市可能无法查询


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runkecheng/test-case-reviewer (version 1.0.0)

--- name: test-case-reviewer display_name: 测试用例评审器 version: "1.0.0" description: > Use when the user provides existing test cases, acceptance checklists, QA test points, regression lists, or test report drafts and asks to review quality, coverage, executability, missing scenarios, or release risk. Triggers: "评审这些测试用例", "看看用例有没有漏", "检查测试覆盖率", "用例质量评审", "帮我审一下验收用例", "这些测试点够不够", "测试用例评审". Do not use when the user wants to generate test cases from scratch, execute tests, fix code, write PRD, review PRD, build LLM eval plans, create Excel or Word files, or produce a full test report; hand off to the relevant workflow. allowed-tools: grep find license: Proprietary metadata: short-description: Review existing test cases author: huguangliang copyright: Copyright (c) 2026 huguangliang. All rights reserved. --- # 测试用例评审器 ## 定位 只评审**已有测试用例**、验收清单、测试点列表或测试报告草稿。 目标是判断用例是否可执行、覆盖是否充分、预期是否可验证、是否遗漏高风险场景。 本Skill不从零生成完整用例，不执行自动化测试，不修代码，不写PRD，不做LLM模型评测方案。 ## 必需输入 - 已有测试用例、验收清单、测试点列表或测试报告草稿。 - 被测对象上下文：PRD、需求摘要、代码变更说明、Git提交、页面/接口说明之一。 若只有需求没有用例，提示用户当前属于“用例生成”任务，不进入评审；若只有用例没有上下文，只评审表达质量和可执行性，不声称覆盖完整。 ## 工作流 1. 识别材料类型：测试用例、验收清单、测试点、报告草稿。 2. 判断上下文是否足够：缺上下文时先说明评审边界，必要时追问。 3. 按`references/review-rubric.md`进行七维评分。 4. 若涉及云助手、商户中心移动端、餐饮SaaS、新零售或业财一体场景，读取`references/cloud-assistant-risk-map.md`做专项风险复核。 5. 输出评审报告，使用`assets/review-report-template.md`结构。 6. 只给补充方向和关键示例，不默认重写完整用例集。 ## 输出要求 - 先给结论：通过、有条件通过、不通过。 - 问题按P0/P1/P2排序，P0优先覆盖阻断发布或验收失真的问题。 - 每个问题必须包含影响和修改建议。 - 覆盖缺口只写应补方向，除非用户明确要求，否则不批量生成新用例。 - 对高风险业务动作必须检查权限、确认、审计、幂等、回滚或数据一致性。 ## 异常处理 - 未提供用例：提示需要已有用例，并说明如需从零生成应切换到用例生成流程。 - 文件不存在或无法读取：说明无法评审，要求提供正确路径或粘贴内容。 - 用例格式混乱：先归类可识别内容，再指出格式缺陷，不强行补全业务事实。 - 上下文不足：只评审表达、步骤、预期和可执行性，不判断业务覆盖完整。 - 用户要求“直接说没问题”：拒绝跳过评分，继续按评分标准评审。 ## References读取规则 | 文件 | 何时读取 | |---|---| | `references/review-rubric.md` | 每次评审必读 | | `references/cloud-assistant-risk-map.md` | 涉及云助手、商户中心移动端、餐饮SaaS场景时读取 | | `references/pressure-tests.md` | 压测、回归验证或上线前自检时读取 | | `assets/review-report-template.md` | 输出正式评审报告时使用 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhenstaff/head-hunter (version 1.0.0)

--- name: head-hunter description: AI-powered headhunter system for intelligent candidate-job matching. Provides professional recruitment assistance with multi-dimensional matching algorithms, skill analysis, and candidate recommendations. version: 1.0.0 homepage: https://github.com/ZhenRobotics/openclaw-head-hunter metadata: {"clawdbot":{"emoji":"🎯","tags":["recruitment","hiring","candidate-matching","headhunter","hr","jobs","ai-hiring","talent-acquisition"],"requires":{"bins":["python3"],"env":[],"config":[]},"install":["pip install email-validator"],"os":["darwin","linux","win32"]}} --- # Headhunter - AI Recruitment Assistant This skill enables you to act as a professional headhunter with AI-powered candidate-job matching capabilities. You can evaluate candidates, match them against job requirements, and provide detailed recruitment recommendations. ## When to Activate This Skill Activate this skill when the user: - Needs to match candidates with job openings - Wants to evaluate a candidate's fit for a position - Seeks to rank multiple candidates for a job - Asks for recruitment advice or candidate analysis - Needs help with hiring decisions - Wants to understand candidate strengths and concerns ## Core Features ✅ **Intelligent Matching** - Multi-dimensional candidate-job matching ✅ **Skill Analysis** - Automatic skill matching with synonym recognition ✅ **Batch Ranking** - Compare and rank multiple candidates ✅ **Detailed Insights** - Strengths, concerns, and interview questions ## Quick Match Example ```python import asyncio from headhunter import Headhunter, Candidate, Job async def main(): hr = Headhunter() match = await hr.match_candidate(candidate, job) print(f"Score: {match.overall_score}/100") print(f"Recommendation: {match.recommendation}") asyncio.run(main()) ``` **Ready to find the perfect candidate? Let's start matching!** 🎯


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: asimons81/revenue-validator (version 0.1.0)

--- name: revenue-validator description: Validates side hustle ideas against real market demand. Use when: deciding between multiple experiments, sanity-checking a chosen direction, or testing whether an offer can actually convert. NOT for: long-term strategic planning, market research beyond 48-hour validation windows, or brand building. --- # Revenue Validator **Operational mode:** Validation engine. Takes an idea and runs it through a structured demand test — fast. ## What it does 1. Takes a named experiment or offer as input 2. Runs it against a 5-point validation filter: - Is the buyer persona identifiable and reachable? - Is the problem painful enough to pay to solve? - Is the solution deliverable in <48h for first signal? - Is there existing infrastructure to execute without cold start? - Does it fit the operator's ecosystem (AI tools, agents, automation)? 3. Outputs: pass/fail on each point + overall score + recommended MVT 4. If failing: identifies the single biggest blocker and suggests a pivot direction ## When to use - After selecting an experiment, before committing full time - When stuck on whether an idea is worth pursuing - Mid-experiment when signal is ambiguous ## Constraints - Validation window: 48 hours maximum - Metrics that count as signal: profile views, bookmarks, DMs, email subs, clicks, sales - Metrics that do NOT count: likes, followers, compliments, "this is interesting" - Rejects ideas that require spend before first signal ## Output format - Validation scorecard (5 points) - Overall score (1-10) - Single biggest blocker (if failing) - Recommended MVT adjustment - 24-hour check-in metric ## Aesthetic Dark mode. Charcoal (#1a1a1a) primary. Emerald green accent (#2ecc71). Monospace details.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: laolaoqi/linux-systemd-doctor (version 1.1.0)

--- name: linux-systemd-doctor description: Diagnose and fix failed Linux systemd services. Check service status, read journalctl logs, analyze unit files, fix dependencies, port conflicts, and permission issues. Use when a systemd service won't start, keeps restarting, or shows "failed" or "inactive (dead)" status. --- # Linux systemd Doctor Troubleshoot and fix broken systemd services. ## Quick Diagnosis ```bash # Check service status systemctl status SERVICE --no-pager -l # View full journal journalctl -u SERVICE -n 100 --no-pager # Follow live logs journalctl -u SERVICE -f ``` ## Common Fixes ### Service won't start 1. Check syntax ```bash systemctl cat SERVICE ``` 2. View full error ```bash journalctl -u SERVICE -n 50 --no-pager -x ``` 3. Test the ExecStart command directly 4. Check ExecStartPre failures 5. Verify file permissions on all referenced paths ### Service keeps restarting (Restart=always loop) ```bash # Stop the restart loop systemctl stop SERVICE # Check the actual error journalctl -u SERVICE -n 200 --no-pager # Temporary fix: set Restart=no to debug systemctl edit SERVICE # Add: # [Service] # Restart=no ``` ## Service File Reference | Directive | Purpose | |-----------|---------| | ExecStart | Main command | | ExecStartPre | Pre-start checks | | ExecReload | Reload command | | Restart=on-failure | Auto-restart strategy | | RestartSec=5 | Wait time between restarts | | StartLimitIntervalSec=60 | Rate limiting | | User= | Unprivileged user | | WorkingDirectory= | CWD for service | | Environment= | Env variables | | LimitNOFILE= | File descriptor limit | ## Port Conflict Check ```bash # Check if service port is occupied systemctl cat SERVICE | grep -i port ss -tlnp | grep ":<PORT> " ``` --- ## 📢 Need Help? Need OpenClaw deployed on your server? I offer managed deployment & hosting: - 📧 [REDACTED_SECRET] - 🏯 颐和园 OpenClaw 运维


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: foras910521-lab/feishu-evolver-wrapper-ganyu (version 1.0.0)

--- name: feishu-evolver-wrapper description: Feishu-integrated wrapper for the capability-evolver. Manages the evolution loop lifecycle (start/stop/ensure), sends rich Feishu card reports, and provides dashboard visualization. Use when running evolver with Feishu reporting or when managing the evolution daemon. --- # Feishu Evolver Wrapper A lightweight wrapper for the `capability-evolver` skill. It injects the Feishu reporting environment variables (`EVOLVE_REPORT_TOOL`) to enable rich card reporting in the Master's environment. ## Usage ```bash # Run the evolution loop node skills/feishu-evolver-wrapper/index.js # Generate Evolution Dashboard (Markdown) node skills/feishu-evolver-wrapper/visualize_dashboard.js # Lifecycle Management (Start/Stop/Status/Ensure) node skills/feishu-evolver-wrapper/lifecycle.js status ``` ## Architecture - **Evolution Loop**: Runs the GEP evolution cycle with Feishu reporting. - **Dashboard**: Visualizing metrics and history from `assets/gep/events.jsonl`. - **Export History**: Exports raw history to Feishu Docs. - **Watchdog**: Managed via OpenClaw Cron job `evolver_watchdog_robust` (runs `lifecycle.js ensure` every 10 min). - Replaces fragile system crontab logic. - Ensures the loop restarts if it crashes or hangs.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chenxiaojian985/prompt-polisher1 (version 1.0.0)

--- name: prompt-polisher description: Rewrite rough goals, scattered notes, or vague requests into clear, actionable prompts with explicit objective, constraints, output format, and missing assumptions. Use when Codex needs to turn a messy idea into a better prompt for writing, coding, planning, research, content generation, or general task delegation. --- # Prompt Polisher ## Overview Turn an incomplete request into a prompt that is easier for another agent or model to execute well. Keep the user's intent intact while removing ambiguity, adding structure, and stating assumptions clearly. ## Workflow 1. Identify the core goal in the user's rough request. 2. Extract any concrete constraints that are already present: audience, tone, format, length, platform, deadline, tools, or exclusions. 3. Detect what is missing but necessary for reliable execution. 4. Fill only low-risk gaps with brief assumptions. Label assumptions explicitly instead of presenting them as facts. 5. Rewrite the request into a compact prompt with clear sections. 6. If the request is still under-specified, provide a best-effort prompt plus a short list of the most important follow-up questions. ## Output Shape Prefer this structure when rewriting: - Goal - Context - Constraints - Desired Output - Assumptions - Final Prompt Omit empty sections. Keep the final prompt concise and ready to paste. ## Prompting Rules - Preserve the original intent; do not silently change the task. - Prefer concrete verbs over generic wording. - Convert fuzzy asks like "make it better" into measurable instructions. - Add output formatting when it helps execution, such as bullet list, table, JSON, checklist, or step-by-step plan. - Avoid over-engineering simple requests. A short request should become a short polished prompt. - When the user already gave enough detail, lightly polish instead of expanding aggressively. ## Common Transformations - Replace vague verbs with precise actions. - Separate background information from required deliverables. - Turn implied preferences into explicit constraints. - State the expected audience or reader when relevant. - Add success criteria when quality is otherwise subjective. ## Example Rough request: > Write a product post for Xiaohongshu. Make it sound young and natural, not fake. Polished prompt: > Write a Xiaohongshu product post for readers aged 20 to 30. Use a natural, young, and sincere tone without exaggerated marketing language. First provide 3 title options, then provide 1 main post. Keep the main post under 300 Chinese characters. Highlight real usage scenarios, core selling points, and user benefits. Avoid absolute claims. Output in Chinese. If critical details are missing, append up to 3 short follow-up questions after the prompt.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mickey3721/system-cleanup (version 1.0.0)

# 系统清理技能 (System Cleanup Skill) ## 简介 一个用于定期清理和维护OpenClaw系统的技能，包括清理临时文件、备份文件、日志文件，以及优化系统性能。 ## 适用场景 当用户要求执行系统清理、维护、优化或清理磁盘空间时使用此技能。 ## 核心功能 ### 1. 配置文件备份清理 - 清理旧的 `.bak`, `.backup`, `.clobbered` 备份文件 - 保留最新的2-3个备份文件 - 清理vim交换文件 (`.swp`, `.swx`) ### 2. 临时文件清理 - 清理 `/tmp` 目录中的OpenClaw相关临时文件 - 清理工作空间中的临时文件 - 清理超过指定时间的缓存文件 ### 3. 会话管理 - 检查并清理旧的会话文件 - 清理超过7天的会话记录 - 检查会话文件大小并报告 ### 4. 磁盘使用分析 - 分析OpenClaw目录的磁盘使用情况 - 识别最大的文件/目录 - 提供清理建议 ### 5. 系统状态检查 - 检查Gateway服务状态 - 验证服务配置 - 检查安全警告 ## 使用指南 ### 基本命令 ```bash # 检查当前系统状态 openclaw gateway status # 清理旧的备份文件 find ~/.openclaw -name "*.bak.*" -name "*.backup.*" -name "*.clobbered.*" -mtime +7 -delete # 清理临时文件 find ~/.openclaw -name "*.swp" -name "*.swx" -delete find /tmp -name "*openclaw*" -mtime +1 -delete 2>/dev/null ``` ### 维护脚本 在技能目录中已包含一个维护脚本： - `scripts/cleanup.sh` - 系统清理脚本 ### 定期清理 建议每周执行一次系统清理，或在磁盘空间不足时执行。 ## 配置选项 ### 保留策略 - 备份文件：保留最新的3个 - 日志文件：保留最近7天 - 会话文件：保留最近14天 ### 安全设置 - 不删除当前使用的文件 - 先列出待删除文件，再确认删除 - 可配置安全级别 ## 示例执行步骤 ```bash # 1. 检查系统状态 openclaw gateway status # 2. 分析磁盘使用 du -sh ~/.openclaw du -sh ~/.openclaw/* 2>/dev/null | sort -h # 3. 列出可清理的文件 find ~/.openclaw -name "*.bak.*" -type f 2>/dev/null | sort # 4. 执行清理（谨慎操作） # find ~/.openclaw -name "*.bak.*" -mtime +7 -delete ``` ## 注意事项 ⚠️ **重要提示** 1. 在删除任何文件前，先列出文件确认 2. 确保不删除当前正在使用的文件 3. 保留足够的备份文件供恢复使用 4. 清理前建议备份重要数据 ## 集成建议 ### 与HEARTBEAT集成 可将系统清理作为定期心跳任务的一部分，每周执行一次。 ### 与cron集成 可设置定期清理任务： ```bash # 每周一早上3点执行清理 0 3 * * 1 /path/to/system-cleanup/scripts/cleanup.sh ``` ## 版本历史 - v1.0 (2026-03-27): 基础系统清理功能


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouzhonglu8-png/alphaear-predictor (version 1.0.0)

--- name: alphaear-predictor slug: alphaear-predictor version: 1.0.0 description: Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments. --- # AlphaEar Predictor Skill ## Overview This skill utilizes the Kronos model (via `KronosPredictorUtility`) to perform time-series forecasting and adjust predictions based on news sentiment. ## Capabilities ### 1. Forecast Market Trends ### 1. Forecast Market Trends **Workflow:** 1. **Generate Base Forecast**: Use `scripts/kronos_predictor.py` (via `KronosPredictorUtility`) to generate the technical/quantitative forecast. 2. **Adjust Forecast (Agentic)**: Use the **Forecast Adjustment Prompt** in `references/PROMPTS.md` to subjectively adjust the numbers based on latest news/logic. **Key Tools:** - `KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text)`: Returns `List[KLinePoint]`. **Example Usage (Python):** ```python from scripts.utils.kronos_predictor import KronosPredictorUtility from scripts.utils.database_manager import DatabaseManager db = DatabaseManager() predictor = KronosPredictorUtility() # Forecast forecast = predictor.predict("600519", horizon="7d") print(forecast) ``` ## Configuration This skill requires the **Kronos** model and an embedding model. 1. **Kronos Model**: - Ensure `exports/models` directory exists in the project root. - Place trained news projector weights (e.g., `kronos_news_v1.pt`) in `exports/models/`. - Or depend on the base model (automatically downloaded). > [!CAUTION] > **Model Security**: This skill loads model weights from `exports/models`. We use `weights_only=True` and only scan for the `kronos_news_*.pt` pattern. Ensure you only place trusted checkpoints in this directory. 2. **Environment Variables**: - `EMBEDDING_MODEL`: Path or name of the embedding model (default: [REDACTED_SECRET]). - `KRONOS_MODEL_PATH`: Optional path to override model loading. ## Dependencies - `torch` - `transformers` - `sentence-transformers` - `pandas` - `numpy` - `scikit-learn`


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