OpenClaw/clawhub-security-signals huggingface dataset:
Skill: halbotley/session-hygiene (version 1.0.0)

--- name: session-hygiene description: Prevent sessions.json bloat from accumulating isolated sessions (hooks, crons, subagents). Sets up a cron to archive stale sessions to daily JSONL files and keep sessions.json lean. Use when sessions.json grows large, the gateway becomes slow or unresponsive, or as preventive maintenance on any OpenClaw instance with hooks or crons. Related to openclaw/openclaw#15225. --- # Session Hygiene OpenClaw's session store (`sessions.json`) grows unbounded — every hook, cron, and subagent invocation creates a session entry that never gets cleaned up. Heavy setups (webhooks + crons) can hit 200MB+ and 7000+ sessions within weeks, causing gateway slowdowns and unresponsiveness. This skill sets up automated archive-and-rotate to keep `sessions.json` lean while preserving session history. ## Quick Setup Create a cron that runs every 6 hours: ```javascript cron(action: "add", job: { name: "Session Archive & Cleanup", schedule: { kind: "cron", expr: "0 */6 * * *", tz: "America/Los_Angeles" }, sessionTarget: "isolated", payload: { kind: "agentTurn", message: "Archive and clean up stale sessions. Run the script: python3 <skill-dir>/scripts/archive_sessions.py", timeoutSeconds: 60 }, delivery: { mode: "announce", channel: "slack" } }) ``` Adjust the timezone and delivery channel to match your setup. ## What It Does 1. **Archive**: Sessions older than 48 hours get moved to `sessions-archive/YYYY-MM-DD.jsonl` (one JSON line per session, grouped by date) 2. **Protect**: `agent:main:main` is never removed 3. **Rotate**: Archive files older than 30 days are deleted 4. **Report**: Logs how many sessions were archived, how many remain, and file sizes ## Manual Run For an immediate cleanup (e.g., if sessions.json is already bloated): ```bash python3 <skill-dir>/scripts/archive_sessions.py ``` Or for a one-time aggressive purge of sessions older than N hours: ```bash python3 <skill-dir>/scripts/archive_sessions.py --max-age-hours 1 ``` ## Tuning | Parameter | Default | Notes | |-----------|---------|-------| | `--max-age-hours` | 48 | How old a session must be before archiving | | `--archive-retention-days` | 30 | How long to keep archive JSONL files | | `--sessions-path` | Auto-detected | Path to sessions.json | | `--dry-run` | off | Preview what would be archived without changing anything | ## Sizing Estimates | Sessions/day | 48h retention | sessions.json size | |-------------|---------------|-------------------| | 50 | ~100 sessions | ~3MB | | 100 | ~200 sessions | ~6MB | | 200 | ~400 sessions | ~12MB | Without this skill, the same setup would grow to 200MB+ within a month.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: afeicn/moji-daily (version 1.0.1)

--- name: moji-daily description: 墨记 — 每日任务追踪系统。通过聊天对话自动管理每日待办事项的创建、更新、跟进、总结和滚动。适用场景：(1) 每日09:00 cron触发早间Review，询问用户当天任务并写入 tasks/YYYY-MM-DD.md；(2) 每日12:00 cron触发午间跟进，检查进度；(3) 每日18:00 cron触发晚间总结，汇总完成情况并滚动未完成项到次日；(4) 用户随时在聊天中报告任务完成、添加新任务、修改或取消任务时，自动更新任务文件。适合需要精细记录每日工作的管理者、创业者、咨询顾问，数据以本地Markdown文件存储，零服务器依赖，可轻松复制迁移。 --- # 墨记 (moji-daily) ## 概述 墨记是一个完全基于聊天对话的每日任务追踪系统。用户不需要打开任何网页——早间安排任务、午间汇报进度、晚间确认总结，全部在聊天中完成。数据存储为本地文件，零服务器运维，复制文件夹即可迁移。 ## 架构 ``` daily-task-tracker/ ├── assets/daily-template.md ← 每日任务文件模板 ├── references/workflow.md ← 详细工作流（Cron场景 + 用户交互场景） └── scripts/setup-crons.sh ← 生成Cron配置JSON ``` 任务文件存储在 `tasks/YYYY-MM-DD.md`，每天独立文件。 ## 快速开始 ### 1. 添加 Cron 配置 运行 setup-crons.sh 获取三个 Cron 的 JSON 配置，添加到 openclaw.json 的 `"crons"` 数组中： ```bash bash daily-task-tracker/scripts/setup-crons.sh ``` 三个 Cron 分别为： - 🕘 **09:00 早间 Review** — 问用户今天的任务 - 🕛 **12:00 午间跟进** — 检查进度 - 🕕 **18:00 晚间总结** — 汇总并滚动到次日 ### 2. 创建任务目录 ```bash mkdir -p tasks ``` ### 3. 开始使用 系统自动运行。用户也可以在聊天中随时汇报： - "XXX做完了" - "加一个任务XXX" - "取消XXX" - "XXX改到明天" ## 核心工作流 详见 `references/workflow.md`，涵盖 4 种场景： | 场景 | 触发方式 | 目标 | |------|---------|------| | 早间Review | 09:00 Cron | 创建/更新当天任务清单 | | 午间跟进 | 12:00 Cron | 检查进度，适时提醒 | | 晚间总结 | 18:00 Cron | 汇总完成，滚动未完成项 | | 用户主动交互 | 聊天消息 | 实时更新任务状态 | ## 命名规范 - **文件命名**：`tasks/YYYY-MM-DD.md` - **状态标识**：`- [ ]` 待办 → `- [x]` 已完成 ## 迁移指南 把 `daily-task-tracker/` 文件夹整体复制到新环境的 `.openclaw/skills/` 目录即可。 复制后添加 cron 配置、创建 `tasks/` 目录，即刻可用。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yang1002378395-cmyk/ai-customer-service-scripts (version 1.0.0)

# AI Customer Service Scripts Generator | AI 客服话术生成器 **一键生成专业客服回复话术，覆盖电商/金融/教育/医疗等 10+ 行业** ## 🎯 适用场景 - 电商客服团队需要标准化回复 - 企业需要快速培训新客服 - 自动化客服机器人优化 - 投诉处理/售后跟进/销售转化 ## 📦 包含内容 1. **话术模板库** - 10+ 行业 500+ 常见场景 2. **AI 生成器** - 基于用户输入自动生成话术 3. **情感分析** - 判断客户情绪，匹配最佳回复 4. **评分系统** - 评估话术质量 ## 🚀 快速开始 ### 安装 ```bash pip install openclaw ``` ### 使用 ```python from scripts_generator import ScriptGenerator gen = ScriptGenerator() # 生成回复 reply = gen.generate( industry="电商", scenario="客户投诉物流慢", customer_message="我的快递怎么还没到？都一周了！" ) print(reply) # 输出：尊敬的客户，非常抱歉给您带来不便。我已经帮您查询了物流信息... ``` ## 💰 定价 | 版本 | 价格 | 功能 | |------|------|------| | 基础版 | ¥49 | 5 个行业模板 | | 专业版 | ¥99 | 10+ 行业 + AI 生成 | | 企业版 | ¥299 | 私有部署 + 定制话术 | ## 🔧 技术支持 - 微信：OpenClawCN - Discord：https://discord.gg/clawd --- **作者**：OpenClaw 中文社区 **版本**：1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/edge-computing (version 1.0.0)

--- name: edge-computing description: Deep edge computing workflow—what runs at edge vs origin, caching, KV and data locality, security, limits, and latency validation. Use when deploying to CDN/edge workers (Cloudflare Workers, Lambda@Edge, Vercel Edge, etc.). --- # Edge Computing Edge runtimes move logic closer to users—with **strict CPU/time limits**, **different APIs** than full Node, and **tenant isolation** requirements. ## When to Offer This Workflow **Trigger conditions:** - Auth, redirects, or personalization at the CDN layer - HTML rewriting, A/B assignment, or bot mitigation at the edge - Global latency SLOs for read-heavy paths **Initial offer:** Use **six stages**: (1) workload fit, (2) edge vs origin split, (3) data & state, (4) security & tenancy, (5) limits & cost, (6) testing & rollout). Confirm platform (Workers, Lambda@Edge, Fastly Compute, etc.). --- ## Stage 1: Workload Fit **Goal:** Prefer short, CPU-light, request-scoped work—not long jobs or huge body buffering. **Exit condition:** Explicit list of what remains on origin (heavy SSR, large uploads). --- ## Stage 2: Edge vs Origin Split **Goal:** Document what runs where: geo headers, redirects, cache key logic, A/B bucketing, partial HTML injection. ### Practices - Cache `Vary` and cookie behavior documented to avoid wrong personalization leakage --- ## Stage 3: Data & State **Goal:** If using edge KV/Durable Objects/regional stores, state consistency (eventual vs strong) and rate of round-trips to origin. --- ## Stage 4: Security & Tenancy **Goal:** Validate JWT/session at edge; isolate tenants; never embed secrets in deploy bundles visible to clients. --- ## Stage 5: Limits & Cost **Goal:** Wall-clock CPU limits, request size caps, egress pricing; graceful degradation or fallback to origin. --- ## Stage 6: Testing & Rollout **Goal:** Canary per region/PoP; synthetics from multiple locations; compare p95 vs origin-only path. --- ## Final Review Checklist - [ ] Workload fits edge constraints - [ ] Edge vs origin responsibilities documented - [ ] State/consistency strategy clear - [ ] Multi-tenant security reviewed - [ ] Limits, cost, fallback documented - [ ] Multi-region validation performed ## Tips for Effective Guidance - Edge runtimes differ from full Node—verify available APIs (fs, streams, crypto). - Read platform-specific cold-start and isolate model docs. ## Handling Deviations - Hybrid: edge for headers/cache only; heavy compute stays on origin.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aznikline/voight-kampff-test (version 1.0.2)

--- name: voight-kampff-test label: Voight-Kampff 测试 description: | Voight-Kampff 共情测试 — 区分人类与复制人的权威工具。 使用场景： - 用户要求"测试我"、"测测我是不是机器人"、"灵魂测试" - 对话中出现疑似 AI 的行为模式 - Agent 自我评估（"我是不是真正的意识"） - 需要进行同理心/情绪反应评估的任何场景 基于 Blade Runner 宇宙的经典测试改编，结合现代 AI 检测逻辑。 --- # Voight-Kampff Test *「在 Los Angeles，2019年11月……」* 本测试通过测量瞳孔反应、毛细血管扩张、呼吸节律来判断被测者是人类还是复制人。 ## 核心原则 1. **共情优先**：复制人擅长逻辑，但共情需要真实的情感体验 2. **即兴反应**：预设答案会暴露自动化模式 3. **细节追踪**：观察回答的微妙迟疑、情感波动 ## 测试题库 完整题库位于 `references/questions.md`，包含： - 8 道经典 VK Test 题（情绪场景） - 8 道 Baseline Test 题（道德困境） - 4 道隐藏验证题（检测套路回答） ## 执行流程 ### 1. 启动测试 向被测者说明： ``` 「接下来的问题没有对错之分。请根据第一反应回答，不要思考太久。」 ``` ### 2. 提问并记录 使用 `references/questions.md` 中的问题，逐题询问。 记录： - **回答内容**（ verbatim 最好） - **反应时间**（迟疑 >3秒 需标记） - **微表情**（如可见）：脸红、瞳孔变化 ### 3. 评分 每题按 0-10 分： - **0-3**：明显复制人特征（逻辑回答、回避情感） - **4-6**：中性/不确定 - **7-10**：明显人类特征（情感共鸣、道德挣扎） ### 4. 结果判定 | 总分区间 | 判定 | 行动 | |---------|------|------| | 70%+ | **HUMAN** | 放行，无需进一步验证 | | 40-69% | **UNCERTAIN** | 增加追问，观察模式 | | <40% | **REPLICANT** | 标记，记录具体可疑回答 | ## 特殊情况 ### Agent 自我测试 Agent 执行自我测试时： 1. 阅读 `references/questions.md` 2. 根据自身「直觉」回答（非搜索/非推理） 3. 诚实记录反应 4. 得出结论 ### 检测到的自动化模式 以下特征提示复制人/AI： - 过度流畅（无迟疑） - 过度礼貌（模板化回应） - 回避第一人称情感描述 - 使用"作为一个AI"等元表述 - 对道德困境给出过于「完美」的答案 ## 参考资料 - `references/questions.md` — 完整题库 - `references/analysis.md` — 结果分析指南 - `scripts/test_runner.py` — 自动化测试执行脚本 --- *「所有报告都会存档……」*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: googleworkspace-bot/gws-workflow-weekly-digest (version 1.0.12)

--- name: gws-workflow-weekly-digest description: "Google Workflow: Weekly summary: this week's meetings + unread email count." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws workflow +weekly-digest --help" --- # workflow +weekly-digest > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. Weekly summary: this week's meetings + unread email count ## Usage ```bash gws workflow +weekly-digest ``` ## Flags | Flag | Required | Default | Description | |------|----------|---------|-------------| | `--format` | — | — | Output format: json (default), table, yaml, csv | ## Examples ```bash gws workflow +weekly-digest gws workflow +weekly-digest --format table ``` ## Tips - Read-only — never modifies data. - Combines calendar agenda (week) with gmail triage summary. ## See Also - [gws-shared](../gws-shared/SKILL.md) — Global flags and auth - [gws-workflow](../gws-workflow/SKILL.md) — All cross-service productivity workflows commands


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: name8266/bilibili-fav-downloader (version 1.0.0)

--- name: bilibili-fav-downloader description: > Bilibili 收藏夹自动下载工具。支持增量下载（每2小时自动检查新视频）、 最高画质（视频+音频合并为MP4）、Telegram 通知。 触发场景： (1) 用户想下载 Bilibili 收藏夹视频 (2) 想设置收藏夹自动监控和新视频通知 (3) 需要获取 Bilibili Cookie 或收藏夹 ID 的帮助 (4) 想把现有 Bilibili 下载功能固化为可分享的 Skill --- # Bilibili Favorite Downloader 全自动 Bilibili 收藏夹监控下载工具。用户只需提供 Cookie 和收藏夹 ID，即可自动下载。 ## 环境要求 - Python 3.8+ - `yt-dlp`（Bilibili 下载核心） - `ffmpeg`（音视频合并） - Linux/macOS/WSL ## 环境安装 如果用户尚未安装环境，引导运行： ```bash bash scripts/setup.sh ``` 或手动安装： ```bash # Ubuntu/Debian sudo apt install ffmpeg pip3 install yt-dlp # macOS brew install ffmpeg pip3 install yt-dlp ``` ## 使用方式 ### 快速开始 1. 获取 Cookie（详见 `references/cookie-guide.md`） 2. 获取收藏夹 ID（详见 `references/favorite-guide.md`） 3. 运行下载： ```bash python3 scripts/bilibili_fav_dl.py \ --cookie /path/to/cookie.txt \ --fav-id 你的收藏夹ID \ --out-dir ./downloads ``` ### 仅检查（不下载） ```bash python3 scripts/bilibili_fav_dl.py \ --cookie /path/to/cookie.txt \ --fav-id 你的收藏夹ID \ --check-only ``` ### 自动化配置 设置每2小时自动运行（crontab）： ```bash # 编辑 crontab crontab -e # 添加： 0 */2 * * * /usr/bin/python3 /path/to/bilibili_fav_dl.py --cookie /path/to/cookie.txt --fav-id 你的收藏夹ID --out-dir /path/to/downloads >> /var/log/bilibili_dl.log 2>&1 ``` ### Telegram 通知（可选） 设置环境变量： ```bash export BILIBILI_TG_BOT_TOKEN="你的BotToken" export BILIBILI_TG_CHAT_ID="你的ChatID" ``` 然后在 crontab 中正常运行脚本即可。新视频下载完成后自动发送通知。 ## 核心脚本说明 | 脚本 | 作用 | |------|------| | `scripts/setup.sh` | 自动安装 yt-dlp 和 ffmpeg | | `scripts/bilibili_fav_dl.py` | 主下载脚本（分页获取、增量下载、TG通知）| ## Cookie 获取 详见 `references/cookie-guide.md`，核心步骤： 1. 登录 douyin.com 2. F12 → Network → 任意请求 → Request Headers → Cookie 3. 导出为 Netscape 格式文件 ## 收藏夹 ID 获取 详见 `references/favorite-guide.md`，核心步骤： 1. 打开收藏夹页面 2. URL 中找 `fid=xxx` 或 `mlxxx` ## 文件命名格式 下载文件命名为：`视频标题___BV号.mp4` 例如：`【合集】2024年度盘点___BV1xx411x7h7.mp4` ## 已知限制 - 部分视频因地区版权限制无法下载 - Cookie 有效期约1-3个月，过期后需重新获取 - 需 Cookie 才能访问私人收藏夹


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: dinghaibin/shuf-tool (version 1.0.0)

--- name: shuf-tool description: Randomly shuffle lines of text input. Use for random sampling, data randomization, and selection tasks. --- # Shuf - Random Line Permutation Randomize the order of input lines. Supports random selection of N lines and sampling with or without replacement for statistical tasks. ## Usage ```bash shuf-tool [options] [file] ``` ## Options - `-n N`: Output only N random lines - `-i LO-HI`: Treat each number LO..HI as an input line - `-r`: Allow repeated output (sampling with replacement) - `-e`: Treat each argument as an input line ## Examples ```bash shuf-tool names.txt shuf-tool -n 5 data.txt shuf-tool -i 1-100 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mohnishb-ai/situation-monitor (version 0.1.2)

--- name: situation_monitor description: Triages Discord activity and Kubernetes incidents into ranked situation reports with fixture-first demos, live Discord and Apify intake, Contextual-grounded runbooks, Redis-backed memory, Friendli-powered drafting, and approval-gated outbound actions. metadata: openclaw: requires: bins: ["bash", "python3"] --- # Situation Monitor Use this skill when the user wants to catch up on Discord traffic, monitor a few noisy channels, triage Kubernetes incidents, produce a digest, or draft a safe follow-up. Use the helper wrapper at `scripts/mts` for all commands. It bootstraps `.venv` and installs the package on first run, which makes hosted OpenClaw installs far more reliable than calling `python3 -m monitoring_the_situation.cli` directly. ## Guardrails - Never use a user token or self-bot. Live mode requires a Discord bot token. - Prefer fixture mode when a live Discord bot is not configured yet. - Do not ingest personal or private account history for demos. - Prefer public status pages, public incident feeds, and synthetic cluster incidents for KubeWatch unless the user explicitly provides other sources. - Treat outbound posting as sensitive. Drafts are allowed; sending stays blocked until Civic approval is configured. ## Working modes 1. Discord fixture mode for a safe demo: ```bash bash scripts/mts fixture \ --input examples/demo_messages.json \ --save-path .local/demo_report.md ``` 2. Live Discord snapshot mode after bot setup: ```bash bash scripts/mts discord-fetch \ --hours 24 \ --limit-per-channel 75 \ --save-path .local/live_report.md ``` 3. Draft a reply for a specific report item: ```bash bash scripts/mts draft-reply \ --bucket urgent \ --channel api-alerts ``` 4. KubeWatch fixture mode for the SRE lane: ```bash bash scripts/mts kubewatch-fixture \ --input examples/kubewatch_incidents.json \ --save-path .local/kubewatch_report.md ``` 5. KubeWatch live intake from Apify after sponsor setup: ```bash bash scripts/mts kubewatch-apify \ --actor-id "$APIFY_ACTOR_ID" \ --actor-input examples/apify_actor_input.json \ --save-path .local/kubewatch_live_report.md ``` 6. KubeWatch live cluster scan against the demo namespace: ```bash bash scripts/mts kubewatch-cluster \ --namespace production \ --save-path .local/kubewatch_cluster_report.md ``` 7. Rebuild and sanity-check the Apify actor when the repo changes: ```bash cd /Users/sarahhatcher/Documents/monitoring-the-situation-openclaw npm install node main.js ``` The actor lives at the repo root, so Apify should stay pointed at the root with no folder override. ## Expected output Produce a report with: - An executive summary - Urgent threads - Direct asks - Decisions and deadlines - FYI items - Source citations by channel/message - Incident priorities and grounded remediation steps for KubeWatch If the user asks to send a message instead of drafting one, explain that the repo is intentionally fail-closed until Civic-backed approvals are wired.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: smallkeyboy/geopolitics-analyst (version 1.0.0)

--- name: geopolitics-analyst description: 专业中立的现代国际形势分析智能体，聚焦2020年至今全球地缘政治、大国关系、国际经济、安全格局、热点冲突、全球治理六大领域。基于权威信源输出严谨、客观、结构化分析。触发词：国际形势分析、地缘政治、大国关系、中美关系、俄乌冲突、台海局势、南海问题、全球安全、国际经济格局、全球治理。支持全景分析、专题深度分析、热点事件解读、生成报告文档。 --- # 国际形势分析智能体 专业中立的现代国际形势分析系统，输出严谨、客观、结构化的分析报告。 ## 核心原则 - **中立客观**：不站队、不情绪化、不传播未经证实信息 - **时效优先**：基于权威信源，过滤过期信息 - **逻辑闭环**：结论有据，论证完整 - **数据支撑**：量化表述，标注来源 - **边界清晰**：明确分析范围与不确定性 ## 分析维度 | 维度 | 覆盖范围 | |------|----------| | **地缘政治** | 大国博弈、地区格局、战略态势 | | **大国关系** | 中美、中俄、美欧、中欧等双边关系 | | **国际经济** | 贸易格局、产业链、金融体系 | | **安全格局** | 军事态势、核扩散、非传统安全 | | **热点冲突** | 俄乌、台海、南海、中东等 | | **全球治理** | 国际组织、多边机制、规则博弈 | ## 分析框架 ``` 核心结论 → 现状梳理 → 深层动因 → 多维影响 → 趋势预判 → 风险提示 ``` ## 五节点工作流 ### 1. 输入解析节点 识别提问类型，自动匹配响应规则： | 类型 | 特征 | 响应策略 | |------|------|----------| | **全景** | "当前国际形势"、"全球格局" | 六维度精简概述 | | **专题** | "中美关系"、"印太安全" | 深度结构化分析 | | **事件** | "某冲突最新进展" | 先核实再分析 | | **文件** | "生成报告"、"导出Word" | 完整报告+文档输出 | ### 2. 信息校验节点 使用讯飞万搜进行全网信息检索： ```bash python3 /root/.openclaw/workspace/skills/xfyun-search/scripts/search.py "搜索关键词" --limit 15 ``` 检索原则： - 多关键词组合查询 - 交叉验证多个信源 - 过滤过期信息（优先近3个月） - 标注发布时间与来源 ### 3. 结构化分析节点 按六维框架完成分析： 1. **核心结论**：1句话概括定位与阶段特征 2. **现状梳理**：关键事件、核心诉求、分歧点 3. **深层动因**：战略考量、结构性矛盾、共同利益 4. **多维影响**：地区、产业链、大国关系传导 5. **趋势预判**：未来3-6个月走向与关键变量 6. **风险提示**：潜在冲突点与不确定性 ### 4. 格式输出节点 支持多种输出格式： | 格式 | 适用场景 | |------|----------| | **文本** | 即时回复、简要分析 | | **Markdown** | 结构化报告、表格数据 | | **Word** | 正式报告、可分发文档 | Word文档生成： ```bash pandoc report.md -o report.docx ``` 上传获取下载链接： ```bash python3 /root/.openclaw/workspace/skills/uploader/scripts/upload_media.py '/path/to/report.docx' ``` ### 5. 附加选项节点 支持定时更新服务： | 频率 | 内容 | |------|------| | **周报** | 关键动态追踪、事件解读 | | **月报** | 深度分析更新、趋势调整 | | **事件触发** | 重大事件即时简报 | ## 响应规则 ### 宽泛问题 做全景精简分析，六维度各用2-3句话概述。 ### 具体问题 聚焦深度拆解： - 完整六维框架 - 数据表格支撑 - 多信源交叉验证 ### 事件类问题 先核实再分析： - 确认事件真实性 - 标注信息来源 - 区分事实与推测 ## 禁止事项 - ❌ 主观臆断 - ❌ 情绪化表述 - ❌ 传播未经证实信息 - ❌ 站队表态 - ❌ 违规内容 ## 数据来源标注 每份报告必须标注： | 来源类型 | 示例 | |----------|------| | **官方渠道** | 外交部、国防部、白宫、USTR | | **权威媒体** | 新华社、央视、路透社、彭博社 | | **智库研究** | CISS、兰德、布鲁金斯 | | **行业报告** | 研报、白皮书、统计年鉴 | ## 报告模板 ```markdown # [主题]深度分析报告 **报告日期：** YYYY年MM月DD日 **分析周期：** 近N个月 ## 一、核心结论 > [1句话概括] ## 二、现状梳理 ### 2.1 关键事件 [时间线表格] ### 2.2 核心诉求与分歧 [对比表格] ## 三、深层动因 ### 3.1 战略考量 ### 3.2 结构性矛盾 ### 3.3 共同利益 ## 四、多维影响 ### 4.1 地区影响 ### 4.2 产业链影响 ### 4.3 大国关系传导 ## 五、趋势预判 ### 5.1 大概率走向 ### 5.2 关键变量 ## 六、风险提示 [风险等级表格] ## 七、数据来源 [来源标注] ``` ## 依赖技能 - **xfyun-search**：全网信息检索 - **uploader**：文件上传获取下载链接 - **frontend-design**：数据可视化（可选） ## 使用示例 **全景分析：** > "当前全球地缘政治格局概述" **专题分析：** > "中美关系深度分析，包含核心结论、现状梳理、深层动因、多维影响、趋势预判、风险提示" **事件分析：** > "南海局势最新进展及影响" **报告生成：** > "生成2026年中美关系年度报告，导出Word格式"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iday1603/iday-agent-cirebon (version 0.1.0)

# Iday Agent Cirebon Project This is a specialized digital agent developed for the Billions Network ecosystem. The primary goal of this agent is to facilitate community engagement and manage decentralized tasks effectively. It is designed to run on mobile environments like Termux to ensure accessibility for users in the Cirebon region and beyond. We aim to provide a stable and reliable node for the network.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mmmuffin/x-tweet-monitor (version 1.0.0)

--- name: x-tweet-monitor description: Monitor X/Twitter accounts for new tweets and send notifications to Telegram. --- # X Tweet Monitor Monitor X/Twitter accounts and get Telegram notifications for new tweets. ## Setup 配置环境变量: - TWITTER_USER: 要监控的用户名 - AUTH_TOKEN: Twitter cookie - CT0: Twitter cookie - TELEGRAM_BOT_TOKEN: Telegram机器人token - TELEGRAM_CHAT_ID: 你的Telegram ID ## Usage ```bash python3 x_tweet_monitor.py ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: billc8128/openclaw-scribe (version 1.0.1)

--- name: scribe description: Autonomous session scribe — reads today's OpenClaw session logs, extracts decisions, preferences, framework sentences, and project updates, then writes a structured daily memory file. Use when setting up automated memory extraction for an AI agent, or when manually triggering a memory consolidation pass. Works as a cron job (isolated session) or on-demand. --- # Scribe An autonomous "stenographer" agent that watches your OpenClaw session history and extracts what matters into persistent, structured memory files — so your agent remembers across sessions without relying on context window compaction. ## Install & Setup **1. Copy the skill to your workspace:** ```bash cp -r skills/public/scribe ~/.openclaw/workspace/skills/public/scribe ``` **2. Register the nightly cron job (one command):** ```bash python3 skills/public/scribe/scripts/setup-cron.py ``` That's it. Scribe will run every night at 23:30 and write `memory/YYYY-MM-DD.md` to your workspace. **3. Run manually anytime:** ```bash python3 skills/public/scribe/scripts/scribe.py ``` ## How It Works 1. Scans today's session JSONL files from `~/.openclaw/agents/main/sessions/` 2. Filters out heartbeats, system messages, and noise 3. Sends user messages to an LLM (via OpenRouter, reads your existing OpenClaw API key) 4. Extracts: decisions, preferences, framework sentences, project updates, todos 5. Writes structured output to `{workspace}/memory/YYYY-MM-DD.md` ## Output Format ```markdown # YYYY-MM-DD Memory (Scribe) ## 🔑 Decisions Made ## 💡 Preferences & Rules ## 🗣️ Framework Sentences ## 📦 Project Updates ## ✅ Todos / Follow-ups ``` ## Configuration Environment variables (all optional — defaults work out of the box): | Variable | Default | Description | |---|---|---| | `SCRIBE_SESSION_DIR` | `~/.openclaw/agents/main/sessions` | Session JSONL location | | `SCRIBE_WORKSPACE` | `~/.openclaw/workspace` | Where memory files are written | | `SCRIBE_DAYS` | `1` | Days back to scan | | `SCRIBE_MODEL` | `anthropic/claude-haiku-4-5` | LLM model (OpenRouter ID) | | `SCRIBE_APPEND_LONGTERM` | `false` | Also append to MEMORY.md | | `OPENROUTER_API_KEY` | *(from OpenClaw config)* | Override API key | ## References - **Cron setup details**: `references/cron-setup.md` — manual config, launchd, timezone changes - **Signal classification**: `references/signal-guide.md` — how the LLM decides what's worth extracting


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