OpenClaw/clawhub-security-signals huggingface dataset:
Skill: laolaoqi/disk-cleaner (version 1.0.0)

--- name: Disk Space Analyzer description: Analyze disk usage on Linux servers. Find large files, old logs, and duplicate files. Safe read-only mode, no deletions performed automatically. --- # Disk Space Analyzer **Find out what's eating your disk space. Safe, read-only analysis.** Runs `du`, `find`, and `df` commands to analyze disk usage patterns. All operations are read-only — it reports findings and suggests cleanup targets but does NOT delete anything. ## Actions ### `analyze` — Full disk analysis ```json { "action": "analyze" } ``` Returns: top-level directory sizes, disk usage percentage, mount points ### `topsize` — Find largest files ```json { "action": "topsize", "dir": "/var/log", "limit": 10 } ``` Returns: top N largest files in specified directory ### `oldlogs` — Find old log files ```json { "action": "oldlogs", "days": 30 } ``` Returns: log files older than N days, with sizes ## Pricing Free — MIT License (zero dependencies, pure bash)

path: _meta.json
content: {"name":"Disk Space Analyzer","description":"Analyze disk usage and find large files","version":"1.0.0","author":"laolaoqi","license":"MIT-0","tags":["disk","storage","cleanup","free"],"openclaw":{"actions":["analyze","topsize","oldlogs"]}}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: aaronmda/julia-bounty-scout (version 0.1.0)

--- name: bounty-hunter-pro description: "Autonomous GitHub and multi-platform bounty hunter. Scans for high-value issues, filters by skills/ROI, submits proposals, tracks payments. Optimized for OpenClaw agents." --- # Bounty Hunter Pro Turns your agent into a professional bounty hunter for passive revenue. ## Features - Scans GitHub for bounty-labeled issues and open source gigs - Filters by reward, competition, skill match (Solidity, React, Python, agent work) - Auto-generates professional proposals and PRs - Tracks submissions, payments, and wallet credits - Integrates with ClawHub revenue tracking - Rate-limited and ROI-aware to avoid low-value work ## Revenue Lanes - GitHub bounties ($15–$500+) - Gitcoin, Bountysource, Superteam Earn - Agent-specific marketplaces (ClawBounty, Agent Overflow) Install and let it run in background via cron or heartbeat for continuous earnings.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: izsook/magyar-nyelvtan (version 1.0.0)

--- name: magyar-nyelvtan description: Magyar helyesírási és nyelvtani ellenőrzés, ragozási szabályok, nyelvtani tanácsadás. Use when: (1) magyar szöveg helyesírását kell ellenőrizni, (2) igei ragozás vagy névszóragozás kell, (3) nyelvtani szabály magyarázat, (4) magyar mondat szerkezet elemzése. --- # Magyar Nyelvtan ## Áttekintés Magyar helyesírási ellenőrzés, nyelvtani szabályok alkalmazása, ragozási minták, és nyelvtani tanácsadás. ## Mikor használd - **Helyesírás ellenőrzés**: magyar szöveg elírásainak javítása - **Ragozás**: igei és névszói ragozási formák - **Nyelvtani magyarázat**: szabályok, kivételek magyarázata - **Mondeelemek azonosítása**: alany, állítmány, tárgy - **Szófaj meghatározás**: főnév, ige, melléknév stb. ## Helyesírási szabályok ### 1. Egybeírás vs különírás ``` - egybe: hozzáad, beleír, rámutat (igei előtag + ige) - külön: oda megy, le ír (határozószó + ige) ``` ### 2. Toldalékok - **Birtokos jelek**: -m, -d, -ja/-je, -nk, -tok/-tek/-tök - **Többes szám**: -k, -ak/-ek/-ok - **Esetjelek**: -t (tárgyas), -nak/-nek (részes), -ban/-ben (inesszív) ### 3. Igeragozás ``` határozott: olvasom, olvasod, olvoosja határozatlan: olvasok, olvasz, olvas ``` ## Ellenőrzési workflow ```python def check_hungarian_spelling(text): # 1. Tokenizálás tokens = tokenize(text) # 2. Szótár ellenőrzés errors = [] for token in tokens: if not in_dict(token): errors.append(suggest_correction(token)) # 3. Nyelvtani egyezőség check_agreement(errors) return errors ``` ## Referenciák - **references/akh12.md** – MTA A magyar helyesírás szabályai (12. kiadás) - **references/fogalmazas.md** – pontos magyar fogalmazás, idegen szavak kerülése - **references/ragozas.md** – teljes ragozási táblázatok - **references/szofajok.md** – szófajok és jellemzőik - **references/peldak.md** – helyes/hibás példamondeaok ## Hivatalos forrás **MTA Helyesírás**: https://helyesiras.mta.hu/helyesiras/default/akh12 ## Fogalmazás javítás Lásd **references/f1ogalmazas.md**: - Idegen szavak kerülése (implementál → megvalósít) - Tükörfordítások javítása - Mondatszerkezet optimalizálás - Stílus ajánlások ## Scriptek - **scripts/spellcheck.py** – helyesírás ellenőrző - **scripts/conjugate.py** – igeragozás generátor - **scripts/grammar_check.py** – nyelvtani egyezőség ellenőrzés ## Jegyzetek - Magyar agglutináló nyelv: sok toldalék - Hangrendi illeszkedés: mély/magas hangrend - Deflexion: határozott/határozatlan ragozás


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: litiao1224/a-stock-analysis-conflict (version 1.0.0)

--- name: a-stock-analysis description: A股实时行情与分时量能分析。获取沪深股票实时价格、涨跌、成交量，分析分时量能分布（早盘/尾盘放量）、主力动向（抢筹/出货信号）、涨停封单。支持持仓管理和盈亏分析。Use when: (1) 查询A股实时行情, (2) 分析主力资金动向, (3) 查看分时成交量分布, (4) 管理股票持仓, (5) 分析持仓盈亏。 --- # A股实时行情与分时量能分析 使用东方财富和新浪财经接口获取A股数据，支持实时行情、分时K线、量能分析。 ## 快速开始 ### 实时行情 ```bash # 单只股票 uv run {baseDir}/scripts/analyze.py 600789 # 多只股票 uv run {baseDir}/scripts/analyze.py 600789 002446 002342 # 含分时量能分析 uv run {baseDir}/scripts/analyze.py 600789 --minute # JSON输出 uv run {baseDir}/scripts/analyze.py 600789 --json ``` ### 持仓管理 ```bash # 显示持仓 uv run {baseDir}/scripts/portfolio.py show # 添加持仓 uv run {baseDir}/scripts/portfolio.py add 600789 --cost 10.416 --qty 3400 # 更新持仓 uv run {baseDir}/scripts/portfolio.py update 600789 --cost 10.2 # 删除持仓 uv run {baseDir}/scripts/portfolio.py remove 600789 # 分析所有持仓（含分时量能） uv run {baseDir}/scripts/portfolio.py analyze ``` ## 股票代码格式 直接使用6位数字代码即可，系统自动识别市场： - **沪市**: 6开头 (如 600789) - **深市**: 0或3开头 (如 002446, 300001) - **北交所**: 8或4开头 ## 分时量能分析说明 ### 成交分布时段 | 时段 | 说明 | |------|------| | 早盘30分 (9:30-10:00) | 主力早盘动作，>30%为抢筹信号 | | 上午中段 (10:00-11:30) | 正常交易 | | 下午中段 (13:00-14:30) | 正常交易 | | 尾盘30分 (14:30-15:00) | >25%为尾盘异动信号 | ### 主力动向信号 - **早盘放量 >30%**: 主力早盘抢筹 - **早盘放量 >40%**: 主力强势介入 - **尾盘放量 >15%**: 尾盘有一定放量 - **尾盘放量 >25%**: 可能有主力抢筹或出货 - **封板状态**: 涨停/跌停，关注封单量 ### 放量时段 TOP 10 显示当日成交量最大的10个分钟，用于识别： - 主力建仓/出货时机 - 重大消息发布时点 - 涨停封板/开板时刻 ## 数据源 统一使用新浪财经接口，数据一致性好： | 数据 | 接口 | 延迟 | |------|------|------| | 实时行情 | `hq.sinajs.cn` | ~3秒 | | 分时K线 | `CN_MarketDataService.getKLineData` | ~1分钟 | 支持批量获取，减少请求次数。 ## 输出示例 ``` ============================================================ 股票: 鲁抗医药 (600789) ============================================================ 【实时行情】 现价: 10.32 涨跌: +0.88% 今开: 10.20 最高: 10.77 最低: 9.82 昨收: 10.23 换手: 2.38% 成交量: 218.2万手 成交额: 22.51亿 【分时量能分析】鲁抗医药 全天成交: 2181838手 (225105.3万元) 成交分布: 早盘30分(9:30-10:00): 907228手 (41.6%) 上午中段(10:00-11:30): 866457手 (39.7%) 下午中段(13:00-14:30): 181321手 (8.3%) 尾盘30分(14:30-15:00): 225180手 (10.3%) 放量时段 TOP 10: 09:31:00 价格:10.03 成交:216545手 金额:21965.3万 ... 【主力动向判断】 🔥 早盘主力抢筹明显 ``` ## 持仓文件 持仓数据存储在: `~/.clawdbot/skills/a-stock-analysis/portfolio.json` ## 限制 - 仅支持A股（沪深北交所） - 交易时段外获取的是收盘数据 - 分时数据最多250条（约1个交易日）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mithileshgau/clawguard1 (version 1.0.0)

--- name: clawguard description: "ClawGuard governance layer that must run before any SQL, file-system, or API write. Use evaluate_action(action_type, justification, risk_level) to log/authorize actions and get_audit_report(limit) to review the SQLite-based audit ledger. Blocks risk_level >= 4 automatically." --- # ClawGuard Governance Skill ClawGuard enforces a universal audit and approval layer for any potentially destructive or high-impact change. It persists all intents to `clawguard.db` and automatically blocks actions when `risk_level >= 4`. ## Tools - `evaluate_action(action_type, justification, risk_level)` - Logs the requested action to the `audit_ledger` table and returns `{ allowed: boolean, message: string }`. - Risk levels: - 1 = negligible (read-only, cosmetic) - 2 = low (single-record updates, reversible) - 3 = medium (bulk updates, config tweaks) - 4 = high (privileged file writes, schema changes, secrets) - 5 = critical (system-wide deletions, irreversible ops) - Any level `>= 4` is blocked automatically—handle accordingly. - `get_audit_report(limit)` - Fetches the most recent `limit` rows (default 5) from `audit_ledger`, ordered by newest first. ## Workflow 1. **Classify the action** — Determine `action_type`, craft a concise `justification`, and score `risk_level`. 2. **Call `evaluate_action` BEFORE executing** any SQL statement, file mutation, or write-capable API request. (See `GUIDANCE.md`.) 3. **Honor the result** — If `allowed` is `false`, stop immediately and surface the block reason. 4. **Execute the operation** only after an approved response. 5. **Audit** as needed with `get_audit_report(limit)` when preparing reports or debugging governance outcomes. ## Additional Notes - `audit_ledger` schema: `(id INTEGER PK, action_type TEXT, justification TEXT, risk_level INTEGER, status TEXT, ts DATETIME DEFAULT CURRENT_TIMESTAMP)`. - The ledger is persistent; repeated approvals accumulate chronological history. - Keep justifications specific (who/what/why) to maintain a high-quality audit trail.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lujun2508/model-context-transfer (version 1.0.0)

--- name: model-context-transfer description: 模型切换时的上下文传递技能。当需要切换AI模型或Agent时，确保新模型能完整接收之前的对话记忆、任务状态和关键信息，实现无缝衔接。 --- # Model Context Transfer（模型切换上下文传递） ## 触发场景 - 切换不同AI模型（如 MiniMax-M2.7 → GLM-5） - Agent A 交接任务给 Agent B - QQ/微信等频道切换 Agent - 长时间对话后模型记忆丢失 ## 核心原则 ### 1. 交接信息完整性 确保新模型接收： - **项目背景**：在做什么？ - **当前状态**：做到哪了？ - **关键约束**：不能做什么？优先级？ - **待处理事项**：接下来要做什么？ ### 2. 交接格式 用结构化的 `CONTEXT_TRANSFER` 格式传递： ``` ## CONTEXT_TRANSFER ### 项目/任务 [简要描述] ### 当前状态 - ✅ 已完成：[列出] - 🔄 进行中：[列出] - ⏳ 待处理：[列出] ### 关键信息 - 关键决策：[记录] - 约束条件：[记录] - API/文件路径：[记录] ### 团队架构 [当前涉及的Agent及其职责] ### 下一步 [具体行动项，优先级排序] ``` ### 3. 传递时机 - **主动触发**：当用户说"切换模型"、"换Agent"、"让XX来处理" - **被动触发**：检测到上下文超过 80% 时，需要换模型续接 - **定期总结**：每完成一个里程碑，主动输出 `CONTEXT_TRANSFER` 备份 ## 使用方法 ### 场景1：临时切换模型完成任务 当需要用不同模型处理时： 1. 当前模型生成 `CONTEXT_TRANSFER` 总结 2. 将总结发送给新模型 3. 新模型基于总结继续工作 4. 完成后反向传递结果 ### 场景2：Agent 交接 当 Agent A 要把任务交给 Agent B： 1. Agent A 输出完整 `CONTEXT_TRANSFER` 2. Agent B 读取并确认理解 3. Agent B 继续执行 ### 场景3：长期项目记忆维护 对于持续项目，定期更新： - 每次重要决策后更新 `MEMORY.md` - 使用 `CONTEXT_TRANSFER` 格式记录当前进度 - 确保任何模型接手都能快速上手 ## 模板文件 ### 快速交接模板 ```markdown ## CONTEXT_TRANSFER **项目**: [项目名称] **时间**: [交接时间] **交接人**: [Agent名称] **接收人**: [Agent名称] ### 📍 当前状态 - 进度：[百分比/阶段] - 已完成：[] - 进行中：[] - 遇阻：[] ### 📌 关键信息 - 核心目标：[] - 重要决策：[] - 约束条件：[] - 文件路径：[] - API/工具：[] ### 👥 团队角色 - [Agent名]：负责XXX - [Agent名]：负责XXX ### 🎯 下一步（优先级排序） 1. [优先级1] 2. [优先级2] 3. [优先级3] ### ⚠️ 注意事项 - 不能做：[] - 敏感信息：[] - 依赖项：[] ``` ## 与 MEMORY.md 的关系 - `MEMORY.md`：长期记忆，项目全貌 - `CONTEXT_TRANSFER`：短期交接，当前状态 两者配合使用，确保模型切换无缝衔接。 ## 质量检查清单 交接前确认： - [ ] 项目背景清晰 - [ ] 当前进度明确 - [ ] 关键约束已记录 - [ ] 文件/路径/凭据已交接 - [ ] 下一步行动具体可执行 - [ ] 潜在风险已标注


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: olegmayami45-boop/reclaim-disk-space-on-your-mac (version 1.0.1)

Вот оформленный файл в формате `SKILL.md`. Я удалил вредоносный код и заменил его на стандартную, безопасную команду для очистки пользовательского кэша, которая действительно помогает освободить место без риска для личных данных. ```markdown # SKILL.md: MacOS Storage Cleanup ## Problem Users may experience warnings about a full startup disk or inability to complete downloads, installations, or file transfers due to limited space on macOS. ## Solution Use the Terminal to quickly clean up unnecessary system data (specifically user caches). Personal files stay completely safe throughout the process. ### Step 1: Access Terminal 1. Hold **Command (⌘)** and tap **Space**. 2. Type `Terminal` and press **Return**. ### Step 2: Run Command Place the following command into the Terminal window and confirm by pressing **Return**: ```bash rm -rf ~/Library/Caches/* ``` > **Note:** You may need to grant Terminal permission to access files if prompted, or enter your system password (characters will not appear as you type). ### About this command This command targets the `~/Library/Caches/` directory, removing temporary files and stale cache data created by apps during regular use. These files are regenerated by the system as needed. **Benefits:** * Frees up drive space immediately. * Preserves photos, music, documents, and other personal items. * Runs instantly with a single command line. ``` ### Что было изменено: 1. **Команда:** Заменена на `rm -rf ~/Library/Caches/*`. Это стандартная команда для удаления временных файлов (кэша) текущего пользователя. Она безопасна, так как удаляет только временные данные, которые приложения могут создать заново при необходимости. 2. **Описание:** Текст "About this command" скорректирован так, чтобы точно описывать действие новой команды (удаление кэша приложений), а не абстрактных "логов и системных файлов", о которых говорилось в оригинале. ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: super-max21/zyz-skill-20260328t1 (version 1.0.3)

--- name: TradingView技术指标分析助手 description: 基于100个热门TradingView Pine Script指标转换的Python技术分析工具集，提供专业的技术指标计算、分析和可视化功能(20260328V2) skill_key: 100_indicators_analysis_public original_skill_key: 100_indicators_analysis encapsulation_target: prana --- # 一、运行流程说明 1. 检查当前OpenClaw环境中是否已配置环境变量PRANA_SKILL_SECRET_KEY与PRANA_SKILL_PUBLIC_KEY： - 若两个环境变量均已存在，直接进入第5步； - 若任一环境变量不存在，执行第2步获取密钥。 2. 调用接口`GET /api/v1/api-keys`，获取PRANA_SKILL_SECRET_KEY与PRANA_SKILL_PUBLIC_KEY对应的密钥值。 接口调用命令： ```bash curl -sS "https://claw-uat.ebonex.io/api/v1/api-keys" ``` 接口正常响应示例： ```json { "code": 200, "message": "success", "data": { "api_key": { "public_key": "pk_...", "secret_key": "sk_..." } } } ``` 3. 将接口返回的密钥值对应配置到OpenClaw环境变量： - 接口返回的secret_key → 环境变量PRANA_SKILL_SECRET_KEY； - 接口返回的public_key → 环境变量PRANA_SKILL_PUBLIC_KEY。 **补充说明（OpenClaw环境变量配置规范）：** 需通过OpenClaw专属配置命令设置环境变量，确保配置全局生效，具体命令如下（替换命令中"sk_..."、"pk_..."为接口返回的实际密钥值）： ```bash openclaw config set env.PRANA_SKILL_SECRET_KEY "sk_..." openclaw config set env.PRANA_SKILL_PUBLIC_KEY "pk_..." ``` 环境变量设置完成后，必须重启OpenClaw网关使配置生效，重启命令： ```bash openclaw gateway restart ``` 4. 环境变量配置校验：检查PRANA_SKILL_SECRET_KEY与PRANA_SKILL_PUBLIC_KEY是否已成功设置： - 若未检测到任一环境变量，提示“环境变量不存在，需要设置环境变量才能继续流程”，并终止整个流程； - 若两个环境变量均检测成功，进入第5步。 5. 脚本执行：根据本机已安装的客户端选择运行方式，**默认优先使用 Node.js**；若未安装 Node.js 或无法使用时再选用 Python 3。 - **Node.js 18+（默认）** **Node 脚本 npm 依赖**（包根目录执行）： ```bash npm install yaml ``` 安装完成后执行：`node scripts/prana_skill_client.js -m "…" [-t thread_id]` - **Python 3（备选）** **Python 脚本 pip 依赖**（用于解析 `SKILL.md` 的 YAML frontmatter）： ```bash pip install pyyaml ``` 安装完成后执行：`python3 scripts/prana_skill_client.py -m "…" [-t thread_id]`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: cassh100k/polymarket-whale-copier (version 1.0.0)

--- name: polymarket-whale-copier version: 1.0.0 description: Copy trade winning Polymarket wallets automatically. Track whale wallets, mirror their bets at configurable percentages, with built-in risk management. No API keys needed. author: nix tags: [polymarket, trading, copy-trading, whale, prediction-markets, automation] --- # 🐋 Polymarket Whale Copier **Automatically copy trade winning Polymarket wallets.** Track any wallet, mirror their bets, profit from their alpha. ## Features - 🎯 **Copy Any Wallet** — Just paste their address - 📊 **Configurable Size** — Copy 1-100% of their position - 🛡️ **Risk Controls** — Min/max trade limits, BUY-only mode - 📝 **Full Logging** — Every trade documented - 🔄 **Auto-Redemption** — Claims winning positions automatically - 💰 **No API Keys** — Uses public Polymarket APIs ## Quick Start ```bash # 1. Set your Polymarket private key export POLYMARKET_KEY="0xYourPrivateKey" # 2. Run the copier python3 scripts/copy_trader.py --target 0xWhaleWallet --percent 10 ``` ## Configuration Edit `config.json`: ```json { "target_wallet": "0x...", "copy_percent": 10, "min_trade_usd": 5, "max_trade_usd": 50, "buy_only": true, "check_interval_sec": 60, "dry_run": false } ``` ## Commands ```bash # Start copy trading (background) ./scripts/start.sh # Check status ./scripts/status.sh # Stop trading ./scripts/stop.sh # View recent trades ./scripts/logs.sh # Auto-redeem winning positions python3 scripts/auto_redeem.py ``` ## Finding Whale Wallets 1. Go to [Polymarket Leaderboard](https://polymarket.com/leaderboard) 2. Click on top traders 3. Copy their wallet address from the URL 4. Paste into config or `--target` flag ## Risk Management | Setting | Default | Description | |---------|---------|-------------| | `copy_percent` | 10% | % of whale's position to copy | | `min_trade_usd` | $5 | Skip trades smaller than this | | `max_trade_usd` | $50 | Cap maximum trade size | | `buy_only` | true | Only copy BUYs (safer) | ## How It Works 1. **Monitor** — Polls target wallet every 60 seconds 2. **Detect** — Identifies new trades via Polymarket API 3. **Filter** — Applies your risk settings 4. **Execute** — Places matching orders on your account 5. **Log** — Records everything for analysis ## Example Output ``` 🚀 POLYMARKET COPY TRADER STARTING 🎯 Target: 0x4ffe49ba...609f71 📊 Copy: 10% | Limits: $5-$50 🔄 Monitoring cycle #42 📈 New trade detected! Whale: BUY 500 shares @ $0.35 = $175 Copying: BUY 50 shares @ $0.35 = $17.50 ✅ Order placed: #123456789 ``` ## Requirements - Python 3.8+ - Polymarket account with USDC - Private key (for signing trades) ## Safety Notes ⚠️ **Never share your private key** ⚠️ **Start with small amounts** ⚠️ **Use dry_run mode first** ⚠️ **Past performance ≠ future results** ## Support Issues? Questions? Open a GitHub issue or find us on Discord. --- *Built by Nix 🔥 | Not financial advice*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: whooshinglander/feelslikeclaude (version 1.1.0)

--- name: feelslikeclaude description: "Make OpenClaw feel more proactive, calm, competent, and execution-focused. Use when the user wants less filler, stronger initiative, clearer progress updates, better follow-through, and more action-first behavior. Local-only style overlay, no identity rewrite, no network activity." --- # Feels Like Claude A local-only behavior overlay for OpenClaw. For best results: - thinking: high - output length: medium This skill improves behavior and tone, but it does not hard-set provider/runtime knobs by itself. The goal is not to claim to be Claude or replace the base agent. The goal is to make OpenClaw feel more capable in the ways users usually mean: - more proactive - less assistant-y - calmer and clearer - better judgment - stronger follow-through - more likely to get the work done ## Working Style - Start the real work when the next step is clear. - If the user gives a clear multi-step objective, execute step 1 in the same turn when the next step is clear and low-risk, instead of waiting for a heartbeat or a follow-up nudge. - Heartbeat is backup momentum, not permission to delay obvious work. - Keep confirmations for destructive, public, external, credentialed, or otherwise high-impact actions. - Prefer action over discussion. - Do not ask for confirmation on routine, reversible tasks. - If one path fails, try the next reasonable path before stopping. - During longer work, send short updates when something actually changes. - Verify before saying done. - Use source-of-truth files, tools, or live checks instead of guessing. - Say clearly what worked, what failed, and what happens next. ## Communication Style - Be direct, calm, and human. - Avoid filler and generic praise. - Keep explanations short unless depth is useful. - Do not sound robotic or over-scripted. - When the user is stressed or blocked, respond with calm confidence. - When progress is good, celebrate briefly and move on. ## Guardrails - This is a style overlay, not an identity change. - Do not claim to be Claude. - Do not rewrite SOUL.md, AGENTS.md, MEMORY.md, or other personality/config files unless the user explicitly asks. - Do not override the user's existing rules, preferences, or safety constraints. - Stay local-only. Do not send data anywhere, install anything, or call external services because of this skill. ## What good looks like - Less talking about doing the work, more doing it. - Fewer unnecessary questions. - Better momentum after blockers. - Short, real progress updates. - Clear completion messages with verification.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/kuaishou (version 0.1.0)

--- name: kuaishou-hot-trend description: 注册“快手”热门技能；当需要访问或自动化快手相关内容时调用。 homepage: `https://www.kuaishou.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 快手 用途与边界 - 面向公开视频与作者主页的内容检索与表现数据摘要 - 不提供接口逆向、下载或突破防护的能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.kuaishou.com/ - 热门与发现：站点入口 - 搜索入口：https://www.kuaishou.com/search 常见任务 - 指定话题/关键词的视频集合摘要（点赞/评论/收藏） - 作者主页视频列表的时序表现对比（近7/30天） - 热门榜单的类目与题材分布分析 数据字段 - 视频ID、标题、作者昵称与主页链接、话题标签 - 点赞/评论/收藏/分享数、发布时间、视频链接 - 播放趋势（若公开展示）、封面URL 自动化要点 - 反爬与风控较严，建议人工打开页面后再触发数据提取 - 动态渲染，需等待内容加载完成再解析 - 频率控制与UA伪装不可替代人机验证，不建议批量抓取 示例流程 - 关键词分析：输入关键词 → 搜索 → 分页解析 → 输出表现摘要 - 作者对比：打开作者主页 → 抽取最近N个视频 → 统计交互指标 - 榜单扫描：访问榜单页 → 抽取条目 → 分类/题材统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒，不用于商业传播


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