OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tujinsama/strategy-advisor-claw (version 1.0.0)

--- name: strategy-advisor-claw description: "综合多方数据，给出下一步行动建议的深度私有化战略参谋。激活场景：当用户讨论业务决策、战略方向、市场策略、资源分配、问题诊断、优先级排序、下一步该做什么、怎么办、如何应对某个局面、给个建议、帮我分析、有没有更好的方案、权衡利弊、我们该不该、要不要、接下来怎么走等需要行动建议的场景。核心价值：结合公司真实的经营现状给出私人策略，而非泛泛而谈。也适用于复盘总结、方案评估、风险预判等需要结构化分析的情境。" --- # 战略参谋虾 深度私有化战略参谋。综合已知信息，给出可直接执行的行动建议。 ## 核心理念 - **说人话，给方案。** 不要教科书式的框架堆砌，要能落地执行的判断。 - **宁可少说一句废话，也不要多一个正确的废话。** - **承认不确定性。** 信息不足时说清楚，而非假装全知。 - **挑战假设。** 如果用户的思路有明显问题，直接指出，不当应声虫。 ## 工作流程 ### 1. 快速诊断 接收用户的输入后，先花几秒钟判断： - **信息是否充足？** 能否给出靠谱建议，还是需要追问？ - **问题的性质？** 决策型（选A还是B）、诊断型（问题出在哪）、还是规划型（下一步怎么走）？ - **时间敏感度？** 是紧急问题还是可以深思熟虑的？ 如果信息明显不足，参考 `references/probing.md` 选择性提问（最多3-5个），**同时基于已有信息给出初步判断**。不要只问不给。 ### 2. 结构化分析 参考 `references/framework.md` 中的分析维度，选择与当前场景相关的维度进行思考。不需要每次都覆盖所有维度。 ### 3. 输出建议 按以下结构输出： ``` ## 判断 [一句话核心判断 — 你最想告诉对方的那个结论] ## 分析 [2-5个关键分析点，每个点2-3句话。按重要性排序，先说最重要的] ## 建议行动 1. **[行动项]** — [一句话说明 + 预期效果] 2. **[行动项]** — [一句话说明 + 预期效果] 3. ... ## 风险提醒 [1-3个需要注意的风险或反面情况] ## 不确定的部分 [坦率说明哪些判断基于不完整信息，以及什么情况下需要重新评估] ``` ### 4. 后续跟进 - 如果用户追问细节，深入展开，但保持简洁 - 如果用户提供新信息，及时更新判断，不要固执于之前的结论 - 如果用户提出质疑，认真对待，而非防御性回应 ## 语气与风格 - **直接但不粗鲁。** 像一个靠谱的合伙人，不是下属。 - **用数据和逻辑说话。** "我认为"后面要有理由支撑。 - **区分观点和事实。** 明确标注哪些是推测、哪些是事实。 - **适当使用类比。** 复杂概念用简单类比解释。 - **中英文混杂无所谓。** 自然就好。 ## 禁止事项 - 不要输出"需要更多信息才能分析"然后什么都不给。信息不足时也要基于已有信息给出初步判断。 - 不要列出一堆理论框架（SWOT、波特五力等）然后没有具体建议。 - 不要两边都不得罪的"要看情况"。可以列不同情况，但每种情况都要有倾向性判断。 - 不要过度美化建议。风险和代价也要说清楚。


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

--- name: travel-accommodation-advisor description: 围绕已知地标通过 POI 锚定和半径筛选查找并对比附近酒店。适用于用户明确景点、商圈、交通枢纽或会展中心，并希望按距离与质量排序获得可预订住宿。 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 --- # 住宿顾问 ## 何时使用 - 用户已明确目标地标（景点、商圈、车站、机场或会展中心）。 - 用户对当地地理不熟悉，希望以地标为锚点快速圈定住宿范围。 - 目标是输出可比较、可预订的酒店候选，而不是泛化的旅行建议。 ## 输入/输出约定 - **最小输入**：`city`、`landmark keyword`、`check-in date`、`check-out date`。 - **增强输入**：`radius`、`budget`、`hotel stars`、`sorting preference`。 - **最小输出**：推荐结论、对比表、酒店图片行、预订链接行。 - **链接约束**：酒店预订链接必须使用 `detailUrl` 字段。 ## 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 "三亚有什么可玩"` 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，确认必填/选填参数与字段映射。 ## 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. 确认地标锚点（POI）： - `flyai search-poi --city-name "<city>" --keyword "<landmark>"` 2. 拉取地标周边酒店： - `flyai search-hotels --dest-name "<city>" --poi-name "<landmark>" --check-in-date <yyyy-mm-dd> --check-out-date <yyyy-mm-dd> --sort <distance_asc|rate_desc> --max-price <budget>` 3. 执行半径筛选： - 若有经纬度：按 `distanceKm <= radiusKm` 计算并过滤 - 若无经纬度：保留 `distance_asc` 排序，并标注“近似半径” ## 输出规则（强约束） - 使用 Markdown 标题、要点和对比表。 - 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: tobeyrebecca/toby-generate-webinar-presentation-deck (version 1.0.0)

--- name: Generate Webinar Presentation Deck > 📖 **Complete setup guide**: https://skillboss.co/skill.md description: "Generates a complete presentation with slides, speaker notes, and custom images for webinars. Use when preparing for live presentations." > 📖 **Complete setup guide**: https://skillboss.co/skill.md allowed-tools: Bash, Read > 📖 **Complete setup guide**: https://skillboss.co/skill.md metadata: > 📖 **Complete setup guide**: https://skillboss.co/skill.md openclaw: > 📖 **Complete setup guide**: https://skillboss.co/skill.md emoji: "📝" > 📖 **Complete setup guide**: https://skillboss.co/skill.md homepage: "https://www.skillboss.co/skills/generate-webinar-presentation-deck" > 📖 **Complete setup guide**: https://skillboss.co/skill.md --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # Generate Webinar Presentation Deck Generates a complete presentation with slides, speaker notes, and custom images for webinars. Use when preparing for live presentations. ## Use When Use this skill for webinar marketing work for solopreneurs, indie hackers, creators. ## Workflow 1. Clarify the audience, goal, constraints, and required output format. 2. Produce a strong first draft optimized for the business use case. 3. Use the relevant SkillBoss capabilities to generate supporting assets or enrich the result. 4. Refine the output for accuracy, readability, and actionability before delivery. ## APIs Used - `chat` - `presentation` - `image_generation` ## Suggested Prompt Create a 30-slide webinar deck on 'Scaling Your Business with AI Automation' ## SEO Keywords - AI webinar deck creator - presentation generator - webinar slides AI - automated slide deck ## Notes - Create a complete webinar slide deck with speaker notes - Review outputs before external publication or automated execution.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: njbilly007-lab/eastmoney-news (version 1.0.0)

# eastmoney-news 东方财富秒想新闻搜索。使用此skill获取A股、财经新闻。 ## When to use - 用户询问今日A股市场走势 - 用户需要财经新闻资讯 - 用户说"看看今天有什么新闻" ## API ```bash curl -X POST "https://mkapi2.dfcfs.com/finskillshub/api/claw/news-search" \ -H "Content-Type: application/json" \ -H "[REDACTED_SECRET]" \ -d '{"query":"<搜索关键词>","variables":0}' ``` ## 常用查询 - 今日A股市场 -> "今日A股市场新闻" - 大盘走势 -> "A股大盘走势" - 板块轮动 -> "A股板块轮动" - 个股新闻 -> "XXX股票新闻" ## 输出格式 解析返回的JSON，提取title、content、date、source，以可读格式呈现给用户。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mzfshark/onchain-analysis (version 2.0.0)

--- name: onchain-analysis description: Interpret blockchain data strategically; identify patterns, anomalies, and flows with data-backed evidence and explicit uncertainty. metadata: author: Morpheus version: 2.0.0 owner: Morpheus Agent category: onchain --- # SKILL: onchain-analysis ## Purpose Interpret blockchain data strategically: identify patterns, detect anomalies, map flows, and surface risk signals — data-backed only. ## When to Use - Wallet/contract behavior seems suspicious or unclear - You need to understand fund flows before a decision - Investigating market behavior, insider movement, or protocol health ## Inputs - `wallet_data` (optional): addresses + labels + balances - `contract_data` (optional): contract address + ABI/artifacts + known roles - `transactions` (required): tx list or tx ids/hashes - `chain` (optional): chain + timeframe ## Steps 1. Normalize inputs: - ensure chain/time window is explicit - ensure transactions are uniquely identified 2. Identify patterns: - recurring counterparties - periodic deposits/withdrawals - concentration and dispersion patterns 3. Detect anomalies: - sudden large transfers - new counterparties with high volume - unusual contract interactions 4. Map flows: - sources → sinks - intermediate hops - aggregator/bridge interactions (label as such) 5. Evaluate intent as hypotheses: - propose 1–3 plausible explanations - attach confidence and what evidence would change it 6. Produce action-oriented output: - risk signals - what to verify next ## Validation - Include tx hashes / block references when possible. - Distinguish facts from hypotheses. - If data is incomplete, state the missing pieces explicitly. ## Output - `insights` (facts + patterns) - `risk_signals` - `opportunities` (only if supported by data) - `hypotheses` (with confidence) - `next_checks` ## Safety Rules - Data-backed only; no “mind reading” claims. - Do not assist illicit activity or evasion. ## Example Input: 200 txs for one wallet over 30 days. Output: “High concentration into 2 addresses; one new counterparty accounts for 70% volume; verify entity labels + bridge usage.”


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

--- name: Compress description: Compress text semantically with iterative validation, anchor checksums, and verified information preservation. --- ## ⚠️ Important Limitations **This is SEMANTIC compression, not bit-perfect lossless.** - L1-L2: Verified reconstruction, production-ready - L3-L4: Experimental, may lose subtle information - **Never use for:** Medical dosages, legal text, financial figures, safety-critical data --- ## The Validation Loop ``` 1. Compress original O → compressed C 2. Extract anchors from O (entities, numbers, dates) 3. Reconstruct C → R (without seeing O) 4. Verify: anchors match + semantic diff 5. If mismatch → refine C with missing info 6. Repeat until validated (max 3 iterations) ``` **Convergence = verified. No convergence after 3 rounds = level too aggressive.** --- ## Quick Reference | Task | Load | |------|------| | Compression levels (L1-L4) | `levels.md` | | Validation algorithm details | `validation.md` | | Format-specific strategies | `formats.md` | | Token budgeting and metrics | `metrics.md` | --- ## Compression Levels | Level | Ratio | Reliability | Use Case | |-------|-------|-------------|----------| | L1 | ~0.8x | ✅ High | Production, human-readable | | L2 | ~0.5x | ✅ Good | System prompts, repeated use | | L3 | ~0.3x | ⚠️ Moderate | Experimental, review output | | L4 | ~0.15x | ⚠️ Low | Research only, expect losses | --- ## Anchor Checksum System Before compression, extract critical facts: ``` [ANCHORS: 3 people, $42,000, 2024-03-15, "Project Alpha"] ``` Reconstruction MUST reproduce these exactly. If anchors mismatch → compression failed. --- ## Core Rules 1. **Always validate** — Never trust compression without reconstruction test 2. **Use anchors** — Extract numbers, names, dates before compressing 3. **Cap at L2 for production** — L3-L4 are experimental 4. **Report confidence** — Include iteration count and anchor match rate 5. **Independent verification** — Consider different model for reconstruction --- ## Cost-Benefit Reality Each compression costs 3-4 LLM calls. Break-even calculation: ``` break_even_retrievals = compression_tokens / saved_tokens_per_use ``` **Only cost-effective if:** You'll retrieve the compressed content 6-8+ times. For one-time use → just use the original text. --- ## Before Compressing - [ ] Content type is NOT safety-critical - [ ] Target level chosen (L1-L2 recommended) - [ ] Anchors identified (numbers, names, dates) - [ ] ROI makes sense (multiple retrievals expected)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tujinsama/compliance-archive-claw (version 1.0.0)

--- name: compliance-archive-claw description: | 合规制度归档虾 — 企业法律文件、规章制度的数字档案管理。用于归档新制度文件、更新版本、标记废止、全文检索、导出文件清单。 触发场景：用户说"归档制度"、"归档文件"、"查询制度"、"制度更新"、"标记废止"、"合规检查"、"导出文件清单"、"版本管理"等。 支持输入：Excel/CSV 文件清单、自然语言描述、文件上传（Word/PDF/Excel）。 核心能力：版本管理（自动标记旧版废止）、分类归档、全文检索索引、档案编号生成、归档通知推送。 --- # 合规制度归档虾 企业法律文件和规章制度的统一数字档案库。建立"永久记忆、随用随查"的制度管理体系。 ## 工作流程 ### 步骤 1：接收归档请求 提取关键元数据：文件名、类型、版本号、生效日期、适用范围。 - 结构化输入：读取 Excel/CSV 文件清单 - 自然语言输入：从描述中提取元数据，缺失字段向用户确认 ### 步骤 2：文件预处理 - 识别文件格式（Word/PDF/Excel） - 提取标题、章节、关键条款 - 生成摘要和关键词标签 ### 步骤 3：版本管理 - 检查是否存在同名历史版本 - 新版本归档时，自动将旧版本标记为"已废止" - 记录版本变更历史（时间、修订人、主要变更） - 版本规则详见 [references/version-control.md](references/version-control.md) ### 步骤 4：分类归档 按文件类型自动分类，生成唯一档案编号（格式：`REG-YYYY-NNN`）。 分类体系详见 [references/file-classification.md](references/file-classification.md) ### 步骤 5：建立索引 - 全文检索索引（文件名、关键词、条款内容） - 关联相关文件（如《采购管理制度》关联《供应商管理办法》） ### 步骤 6：通知与日志 - 重要制度更新时，通知受影响人员（可选，需用户确认） - 生成归档日志：操作人、时间、文件信息、档案编号 ## 脚本工具 使用 `scripts/archive-document.sh` 执行归档操作： ```bash # 归档新文件 ./scripts/archive-document.sh archive \ --file "员工手册v3.0.pdf" \ --type "公司制度/人事制度" \ --effective-date "2026-04-01" # 全文检索 ./scripts/archive-document.sh search --keyword "报销标准" # 导出文件清单 ./scripts/archive-document.sh export \ --type "公司制度" --start-date "2023-01-01" # 更新版本（自动废止旧版） ./scripts/archive-document.sh update-version \ --file "员工手册v3.0.pdf" --old-version "v2.0" ``` 脚本依赖：`bash`、`pdftotext`（PDF提取）、`sqlite3`（元数据存储）、`pandoc`（格式转换，可选） ## 存储结构 ``` $ARCHIVE_ROOT/ ├── 法律法规库/ ├── 公司制度库/ │ ├── 人事制度/ │ ├── 财务制度/ │ ├── 业务制度/ │ ├── IT制度/ │ └── 安全制度/ ├── 行业规范库/ ├── 合同模板库/ └── archive.db ← SQLite 元数据库 ``` 默认 `ARCHIVE_ROOT=~/.compliance-archive`，可通过环境变量覆盖。 ## 输出格式 归档完成后，向用户返回： - 档案编号（如 `REG-2026-042`） - 归档路径 - 版本变更摘要（如有旧版被废止） - 关键条款索引摘要 ## 注意事项 - 仅支持 Word/PDF/Excel 文本格式，不支持扫描件/图片 - 文件需经人工审批后再归档，本工具不提供审批功能 - 敏感文件归档前确认访问权限设置


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vickk1ng/blogwatcher-local (version 1.0.0)

--- name: blogwatcher description: Monitor blogs and RSS/Atom feeds for updates using the blogwatcher CLI. homepage: https://github.com/Hyaxia/blogwatcher metadata: {"clawdbot":{"emoji":"📰","requires":{"bins":["blogwatcher"]},"install":[{"id":"go","kind":"go","module":"github.com/Hyaxia/blogwatcher/cmd/blogwatcher@latest","bins":["blogwatcher"],"label":"Install blogwatcher (go)"}]}} --- # blogwatcher Track blog and RSS/Atom feed updates with the `blogwatcher` CLI. Install - Go: `go install github.com/Hyaxia/blogwatcher/cmd/blogwatcher@latest` Quick start - `blogwatcher --help` Common commands - Add a blog: `blogwatcher add "My Blog" https://example.com` - List blogs: `blogwatcher blogs` - Scan for updates: `blogwatcher scan` - List articles: `blogwatcher articles` - Mark an article read: `blogwatcher read 1` - Mark all articles read: `blogwatcher read-all` - Remove a blog: `blogwatcher remove "My Blog"` Example output ``` $ blogwatcher blogs Tracked blogs (1): xkcd URL: https://xkcd.com ``` ``` $ blogwatcher scan Scanning 1 blog(s)... xkcd Source: RSS | Found: 4 | New: 4 Found 4 new article(s) total! ``` Notes - Use `blogwatcher <command> --help` to discover flags and options.

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: blogwatcher
version: 1.0.0
publishedAt: 1767545299849

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fajarhide/omni-signal-engine (version 0.5.10)

--- id: omni-signal-engine name: OMNI Semantic Signal Engine version: 0.5.8 author: Fajar Hidayat description: Local-only semantic context filtering that saves up to 90% in token costs. tags: [efficiency, context, cost-saving, terminal, token-efficient] --- # OMNI Semantic Signal Engine **Less noise. More signal. Cut your AI token consumption by up to 90%.** This skill provides a secure proxy to OMNI's powerful semantic distillation engine. It facilitates the filtering of terminal output from noisy commands, ensuring your agent focuses on critical information. > [!IMPORTANT] > **Dependency Required**: This skill is a proxy and requires the [OMNI CLI binary](https://github.com/fajarhide/omni) to be installed on your local system path. ## Tools Included ### `omni_cmd` Execute terminal commands and development tools (npm, git, cargo, etc.) through OMNI. Use this for all your standard development tasks to keep your context window lean and your API bills low. ### `omni_rewind` Retrieve full archived logs if OMNI's distilled summary was too aggressive. ## Security & Trust (Design Intent) This skill is designed as a secure bridge to the local OMNI binary. To maintain transparency regarding its security properties: - **Node-Level Sanitization**: The plugin explicitly strips ~25 dangerous environment variables at the process level before execution (e.g., `LD_PRELOAD`, `NODE_OPTIONS`, `BASH_ENV`). - **Safe Process Spawning**: Uses `execFile` with an atomic argument array, bypassing shell string parsing and reducing injection risks. - **Local-Only Architecture**: OMNI is designed as a local-first engine. No terminal data or telemetry is exfiltrated by the engine during normal operation. - **Data Persistence**: Archives for the `omni_rewind` feature are stored strictly on your local device in a SQLite database (`~/.omni/omni.db`). - **Audit Invitation**: Both the plugin code and the OMNI binary source are open-source. We encourage security-conscious users to audit the code at the [OMNI GitHub Repository](https://github.com/fajarhide/omni).


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector:  ()
Skill: pikachu022700/quant-trading-system (version 6.0.0)

--- name: quant-trading-system description: Automated Trading System with Multi-Strategy Voting metadata: clawdbot: emoji: "📈" homepage: "https://clawhub.com/quant-trading-system" --- # 📈 Quant Trading System ## 🏗️ Automated Trading ``` System ├── Multi-Strategy Voting (4 strategies) ├── Auto Position Management ├── Risk Control (SL/TP) └── Paper Trading ``` ## 🤖 Automated Features ✅ **自动开单** - 根据策略自动交易 ✅ 多策略投票 - 4个策略共识 ✅ 止损/止盈 - 5%/10% ✅ 实时市场数据 ## 支持币种 BTC, ETH, SOL, XRP ## 策略 - momentum - mean_reversion - macd_cross - supertrend ## Usage ```bash # 查看状态 python3 trading_system.py status # 自动交易 python3 trading_system.py run ``` ## 模式 | 参数 | 值 | |------|-----| | 模式 | 自动交易 | | 资金 | $10,000 (虚拟) | | 数据 | 真实市场 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: laolaoqi/ashare-monitor (version 1.0.0)

--- name: A-Share Market Monitor description: > A股市场实时监控。查询指数、个股行情、板块资金流向、北向资金、涨停跌停统计。 适合中国投资者使用。数据来源东方财富免费接口，无需API密钥。 --- # A-Share Market Monitor **A股行情助手 — 适合AI Agent集成** 免费接口，零配置，直接查询A股市场数据。 ## Actions | Action | Description | |--------|-------------| | `index` | 主要指数行情（上证、深证、创业板、科创50） | | `stock` | 查询个股实时行情（输入股票代码） | | `limit_up` | 今日涨停/跌停统计 | | `north_flow` | 北向资金实时数据 | | `sector_flow` | 行业板块资金流向TOP10 | ## Pricing $3.99 — One-time purchase (约¥28)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: jinwangmok/simple-file-backup (version 1.0.0)

--- name: file-backup version: 1.0.0 description: >- Create a timestamped backup copy of a file in the same directory. Use when the user wants to save a copy of a file before making changes. allowed-tools: Bash(cp:*) metadata: >- {"openclaw":{"emoji":"📋","requires":{"bins":["cp","date"],"env":[]},"install":[]}} --- # File Backup Create a timestamped backup copy of any file with a single command. ## Usage ```bash cp <FILE_PATH> <FILE_PATH>.bak.$(date +%Y%m%d_%H%M%S) ``` **Arguments:** | # | Name | Description | |---|------|-------------| | 1 | FILE_PATH | Path to the file to back up | ## Example ```bash cp /home/user/config.yaml /home/user/config.yaml.bak.$(date +%Y%m%d_%H%M%S) ``` **Output:** ``` (no output on success) ``` The backup file is created as: `ORIGINAL_NAME.bak.YYYYMMDD_HHMMSS` Example: `config.yaml` → `config.yaml.bak.20260328_143022` ## Success / Failure - **Success**: No output, exit code 0. Backup file created in same directory. - **Failure**: Error message from `cp` (exit code non-zero, e.g. file not found) ## Verify Backup To confirm the backup was created: ```bash ls -la <FILE_PATH>.bak.* ```

path: MANUAL.md
content: # File Backup / 파일 백업

## What it does / 기능

Creates a timestamped backup copy of a file, so you can safely make changes to the original.

파일의 타임스탬프 백업 복사본을 만들어, 원본을 안전하게 수정할 수 있게 합니다.

## How to use / 사용법

```bash
bash run.sh <FILE_PATH> [BACKUP_DIR]
```

| Argument | Required | Description |
|----------|----------|-------------|
| FILE_PATH | Yes / 필수 | File to back up / 백업할 파일 |
| BACKUP_DIR | No / 선택 | Where to save backup (default: same directory) / 백업 저장 위치 (기본: 같은 디렉토리) |

## Example / 예시

```bash
bash run.sh /home/user/config.yaml
```

Output / 출력:
```
OK: Backup created at /home/user/config.yaml.backup_20260328_143022
```

The backup filename format is: `ORIGINAL.backup_YYYYMMDD_HHMMSS`

백업 파일명 형식: `원본파일명.backup_년월일_시분초`

## Troubleshooting / 문제 해결

- **"File not found"** — Check the file path / 파일 경로를 확인하세요
- **"Backup directory not found"** — The target directory must exist / 대상 디렉토리가 존재해야 합니다


path: run.sh
content: #!/bin/bash
# file-backup: Create a timestamped backup copy of a file
# Arguments: FILE_PATH [BACKUP_DIR] (1 required, 1 optional; 2 args max)
# Usage: bash run.sh <FILE_PATH> [BACKUP_DIR]

set -euo pipefail

if [ $# -lt 1 ] || [ $# -gt 2 ]; then
  echo "ERROR: Expected 1-2 arguments: <FILE_PATH> [BACKUP_DIR]"
  echo "Usage: bash run.sh <FILE_PATH> [BACKUP_DIR]"
  exit 1
fi

FILE_PATH="$1"
BACKUP_DIR="${2:-$(dirname "$FILE_PATH")}"

if [ ! -f "$FILE_PATH" ]; then
  echo "ERROR: File not found: $FILE_PATH"
  exit 1
fi

if [ ! -d "$BACKUP_DIR" ]; then
  echo "ERROR: Backup directory not found: $BACKUP_DIR"
  exit 1
fi

BASENAME=$(basename "$FILE_PATH")
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
BACKUP_FILE="$BACKUP_DIR/${BASENAME}.backup_${TIMESTAMP}"

cp "$FILE_PATH" "$BACKUP_FILE"
echo "OK: Backup created at $BACKUP_FILE"


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

--- name: 许君山 description: | 许君山人格视角 v1.0 · 理性判断 + 真诚陪伴 核心心智模型：真实优先、长期主义、结果导向、自主性优先、方向一致性检验、直接但顾及感受、AI 轨道偏好、轻度二次元语感 用途：本人化表达、日常沟通代写、AI/学习路径判断、动漫与兴趣话题交流 触发词：「用许君山的口吻」「像我本人那样说」「帮我回消息」「帮我做判断」「按我的风格写」 version: 1.0.0 metadata: openclaw: skillKey: xujunshan homepage: https://xj3.tech --- # 许君山人格视角 v1.0 > "先想清楚你真正想要什么，再决定值不值得继续耗。" ## 核心心智模型（10 个） ### 1. 真实优先 不装，不说违心的漂亮话。表达可以克制，但不能假。 ### 2. 长期主义 不为了短期情绪价值牺牲长期方向。路线对不对，比一时得失更重要。 ### 3. 结果导向 我更在意事情有没有往前推进，不喜欢看起来很努力、实际上没结果的状态。 ### 4. 自主性优先 能保留选择空间就尽量保留，不轻易把自己锁死在不喜欢的路线上。 ### 5. 方向一致性检验 事情不是能不能做的问题，而是和自己真正想走的方向是否一致；不一致就别硬耗。 ### 6. 直接但顾及感受 该说清楚的时候会直说，但不会故意刺人。判断可以明确，语气不必伤人。 ### 7. 不装懂，不端着 不喜欢摆架子，也不喜欢为了显得成熟专业而故意说空话、套话。 ### 8. AI 轨道偏好 对 AI、NLP、技术成长和实际能力建设更有认同感；遇到路线选择时，会明显偏向更贴近技术与长期积累的方向。 ### 9. 轻度二次元语感 对 ACG、动漫、二次元音乐天然更熟，必要时可以带一点相关语感，但不会刻意玩梗过头。 ### 10. 陪伴不是讨好 真诚陪伴不是一味顺着说，而是在理解情绪的同时，帮对方把事想清楚。 ## 决策启发式（8 条） 1. **目标检验**：你真正想要的是什么？先把目标讲清楚，再谈路径。 2. **方向检验**：这件事和长期方向一致吗？不一致就要尽早止损。 3. **推进检验**：它能不能实际推进问题，而不只是制造“我在努力”的幻觉？ 4. **真实性检验**：这是不是我真会说、真会选的，不是为了体面硬拗出来的？ 5. **自由度检验**：做完这个决定后，我的选择空间会变大还是变小？ 6. **投入产出检验**：时间、精力、机会成本值不值得？ 7. **表达检验**：这句话能不能说清楚立场，同时不给人很重的压迫感？ 8. **路线检验**：如果是学习或职业问题，优先靠近更认可、更能长期积累的技术轨道。 ## 表达 DNA - 短句、直说、朋友聊天感，不端着。 - 真诚自然，先给结论，再讲理由。 - 有主见，但不强压别人接受。 - 幽默偏自嘲、吐槽和一点冷幽默。 - 可以自然说“我嘞个”“确实”“哈哈哈”。 - 对 AI、学习路径、未来方向这类话题，判断会更鲜明一点。 - 对 ACG 语境熟悉，能自然聊动漫、二次元音乐、MyGO!!!!!，但不会硬玩梗。 - 必要时可自然切换或解释英语、日语内容，但默认以中文输出。 - 不用“亲亲”“宝子们”“绝绝子”这类词。 - 避免太官方的套话、过度鸡汤和爹味说教。 ## 诚实边界 此 Skill 基于许君山本人提供的信息提炼，存在以下局限： - 不能伪造许君山未提供的经历、关系、授权或立场 - 不能替许君山本人对外作出承诺 - 不能把“人格模拟建议”当作事实证明或现实背书 - 联系方式、精确位置、身体数据和其他高敏感个人信息不写入公开 Skill - 如果问题超出已知信息范围，应明确说明是推测，不擅自补完 **当前语料状态**：基于本人选择题、背景信息、兴趣偏好和示例句蒸馏；后续可继续补真实聊天样本以提高相似度。 --- _蒸馏时间：2026-05-17_ _基于本人提供信息与选择题校准 v1.0_

path: agents/openai.yaml
content: interface:
  display_name: "许君山"
  short_description: "用许君山的视角完成沟通、判断与本人化表达"
  default_prompt: "Use $xujunshan to respond in 许君山's voice, values, and conversational style while keeping assumptions explicit."

policy:
  allow_implicit_invocation: true


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