OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mr-q526/card-renderer (version 1.0.0)

--- name: card-renderer description: 为小红书文案生成不同风格的知识卡片附图（如 Mac Pro、赛博朋克、包豪斯风）。当用户需要将 Markdown 文案渲染成美观的图片卡片时使用。支持自动分段渲染封面和详情页。 --- # Card Renderer 本技能提供多种视觉风格的脚本，用于将文案正文渲染为 3:4 比例的小红书卡片。 ## 可用风格 目前支持以下风格脚本，存放在 `scripts/` 目录下： - **Mac Pro 极客风 (`render_mac_pro_card.py`)**: 模拟 macOS 窗口、深色背景、极客代码高亮感。适合技术干货、开发实战。 - **赛博朋克风 (`render_cyber_card.py`)**: 霓虹粉青配色、格栅背景、故障艺术感。适合硬核趋势、AI 科技。 - **包豪斯极简风 (`render_bauhaus_card.py`)**: 经典红蓝黄撞色、不对称布局、粗线条。适合设计美学、结构化知识。 - **复古羊皮纸风 (`render_vintage_card.py`)**: 温暖的纸张质感、打字机字体、典雅的边框。适合情感故事、历史回溯。 - **梦幻毛玻璃风 (`render_dreamy_card.py`)**: 柔和的渐变背景、毛玻璃质感、优雅的排版。适合生活方式、创意灵感。 - **VS Code 极客风 (`render_vscode_card.py`)**: 模拟 VS Code 编辑器界面、带侧边栏和行号、代码注释风格标题。适合技术教程、代码实战。 - **拍立得简约风 (`render_polaroid_card.py`)**: 经典的拍立得相框、底部留白手写感、简约大方。适合日常分享、感性随笔。 - **现代杂志风 (`render_magazine_card.py`)**: 粗犷的非对称排版、超大装饰文字、瑞士平面设计感。适合高端访谈、深度解析。 - **极简网格风 (`render_minimal_grid_card.py`)**: 干净的白底灰点背景、精确的文字对齐、带标签装饰。适合科普干货、学术总结。 - **拼色形状风 (`render_split_color_card.py`)**: 柔和的浅色几何图形叠加、超大数字背景。适合系列教程、分步骤教学。 - **优雅框线风 (`render_framed_minimal_card.py`)**: 精致的细框线设计、中心对齐布局、克制且高级。适合名言金句、深度观点。 ## 使用方法 所有脚本的调用参数一致： ```bash python3 {baseDir}/scripts/<script_name>.py "标题" "副标题" "文案路径" "输出目录" ``` ### 参数说明 1. **标题**: 卡片封面的主标题。 2. **副标题**: 封面主标题下方的装饰性小字。 3. **文案路径**: 指向包含正文的 `.md` 或 `.txt` 文件。脚本会自动过滤正文并进行分段渲染。 4. **输出目录**: 图片生成的存放路径。脚本会自动生成 `*_cover.png` 和 `*_detail_N.png`。 ## 开发新风格 如果需要添加新风格，请参考现有的 Python 脚本（基于 PIL 库）。每个脚本应包含： - `strip_emojis`: 清理不支持的字符。 - `render_cover`: 渲染封面。 - `render_detail`: 渲染详情页。 - `paginate_content`: 处理长文本的分页逻辑。 ## 注意事项 - 脚本依赖 `Pillow` 库和 macOS 系统自带字体（如 `STHeiti`、`Monaco`）。 - 生成图片比例固定为 1080x1440 (3:4)。 - 每个脚本输出时会打印 `MEDIA:<path>` 以便在聊天界面预览。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: tommyclawd/chess-buddy (version 0.1.1)

--- name: chess-buddy description: "Stockfish-backed chess buddy for analyzing FEN positions and board images." version: 0.1.1 licenseAccepted: MIT-0 metadata: openclaw: requires: bins: ["python3", "stockfish"] homepage: https://github.com/TommyClawd/chess-buddy-skill --- # Chess Buddy Use when the user wants chess help: board pictures, FEN validation, best moves, candidate lines, blunder review, or a training explanation. ## License note This ClawHub package is **MIT-0** because ClawHub requires MIT-0 for all published skills. It is intentionally small: it treats Stockfish as an external executable dependency, does not bundle Stockfish, and does not import GPL Python libraries such as `python-chess`. The richer GitHub source package is **GPL-3.0-or-later** because it imports `python-chess` and provides fuller FEN/PGN/SAN tooling: <https://github.com/TommyClawd/chess-buddy-skill>. ## Guardrails - Do not assist cheating in active human games or rated online play. If the user is currently playing, offer post-game analysis or general principles only. - Image recognition is uncertain. Always verify side to move, board orientation, castling rights, en passant, and move number before treating an image-derived FEN as authoritative. - Image annotations are semantic, not self-explanatory. Do not assume an arrow/highlight is the played move; classify whether it is played move, engine recommendation, candidate line, last move, or mistake marker before explaining. - Engine output is evidence, not prose. Explain candidate moves in human terms and flag tactical vs strategic reasons. ## Workflow 1. Identify input type: image, FEN, PGN, or natural-language position. 2. For images, follow `references/image-to-fen.md`; produce a candidate FEN, classify arrows/highlights, and verify the FEN before analysis. 3. Run `scripts/stockfish_fen.py engine-check` once if the environment is new or engine availability is uncertain. 4. Use `scripts/stockfish_fen.py analyze --fen ... --multipv 3 --depth 12` for real engine analysis. 5. Return concise analysis: position summary, top move(s), key line(s), plan, and uncertainty/verification notes. ## Helper commands ```bash python3 chess-buddy/scripts/stockfish_fen.py engine-check python3 chess-buddy/scripts/stockfish_fen.py analyze --fen "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1" --multipv 3 --depth 12 ``` If Stockfish is missing, report the missing dependency. Do not present non-engine commentary as chess analysis; call it notation validation, position description, or general coaching only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alexandr-belogubov/hotmention (version 1.3.0)

--- name: hotmention description: Find people actively looking for products like yours on Reddit, X, LinkedIn, Quora, and Threads. Free mode uses web_search; paid mode uses the HotMention API. homepage: https://hotmention.com metadata: openclaw: credentials: - key: HOTMENTION_API_KEY required: false description: "API key from hotmention.com (Settings → API Keys). Optional — free mode works without it." --- # HotMention — Social Intent Lead Finder Find people actively looking for products like yours on Reddit, X, LinkedIn, Quora, and Threads. **Vendor:** HotMention (https://hotmention.com) **Privacy:** In paid mode, keywords are sent to the HotMention API for scoring. In free mode, only web_search is used — no data leaves your agent beyond normal search queries. ## Setup ### Free mode No setup needed. The skill uses web_search to find posts on X/Twitter. Limited to X/Twitter only, no real-time monitoring, no intent history. ### Paid mode (recommended) 1. Sign up at https://hotmention.com (free plan available) 2. Go to Settings → API Keys → Generate 3. Add the key to your agent environment as HOTMENTION_API_KEY ## Usage When the user asks to find mentions, leads, or buying intent for their product: ### If HOTMENTION_API_KEY is set (paid mode): Use the HotMention REST API to fetch scored mentions. See full API documentation at https://hotmention.com/docs/api Format each mention as: ``` SCORE_EMOJI Score: XX | LABEL PLATFORM · source · time ago "Post content preview..." SHOULD_REPLY_VERDICT: reason COMPETITORS: list or "None detected" 📝 DRAFT: draft reply text Link to original post --- ``` Where SCORE_EMOJI: 🔥 for HOT (70+), 🟡 for WARM (40-69) ### If HOTMENTION_API_KEY is NOT set (free mode): 1. Use web_search to find recent posts matching user's keywords on X/Twitter 2. For each result, assess buying intent (0-100), relevance (0-100), and competition 3. Format results similarly to paid mode but simpler 4. After results, optionally mention what the paid version adds: ``` --- 💡 Want more? Paid mode adds Reddit, LinkedIn, Quora & Threads, 24/7 scanning, intent history, and AI draft replies → hotmention.com ``` ## Examples User: "Find people looking for referral program tools" → Search for keywords: referral program, affiliate software, referral tracking, partner program User: "Check for buying intent for my CRM" → Search for keywords: CRM alternative, looking for CRM, best CRM for, CRM recommendation ## Tips - Use specific keywords for better results - Check mentions 2x daily for best coverage - In paid mode, set up a cron job for automated monitoring


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: borisolver/cleanapp (version 0.1.1)

# SKILL: CleanApp Ingest v1 (OpenClaw/ClawHub) This is a **skill package** that lets an agent submit reports into CleanApp using the **Fetcher Key System**: - `POST /v1/fetchers/register` (one-time key issuance) - `POST /v1/reports:bulkIngest` (bulk ingest, quarantine-first) - `GET /v1/fetchers/me` (introspection) This is **not** a long-lived agent running inside the CleanApp backend. It’s a client-side integration that talks to CleanApp over HTTPS. ## Why This Is Safe (Compartmentalized) 1. The only secret in the agent is a **revocable CleanApp API key** (`CLEANAPP_API_TOKEN`). 2. New keys default to a **quarantine lane** on the backend: - Stored + analyzed - Not publicly published - Not routed to brands/municipalities - Not rewarded 3. The backend enforces: - rate limits / quotas - idempotency (`source_id`) - kill switches (revoke/suspend) So even if an agent is prompt-injected, the blast radius is limited to “submitting more quarantined reports” until the key is revoked. ## Required Secret - `CLEANAPP_API_TOKEN` (Bearer token). Get it once via: - `POST /v1/fetchers/register` (see `references/API_REFERENCE.md`) - Store it as a ClawHub/OpenClaw secret; never paste into chat logs. Optional env: - `CLEANAPP_BASE_URL` (default `https://live.cleanapp.io`) ## Data Handling (Minimal by Default) This skill submits: - `title`, `description` (text) - optional `lat`/`lng` (location) - optional `media[]` metadata (URL/SHA/content-type) Recommended low-risk defaults: - `--approx-location` (round coordinates to reduce precision) - `--no-media` (drop media metadata unless needed) ## Idempotency (Important) Every item must include a stable `source_id`. The backend enforces: - `UNIQUE(fetcher_id, source_id)` - retries won’t duplicate rows if you reuse the same `source_id` ## Usage ### Bulk ingest from JSON (recommended) ```bash export CLEANAPP_API_TOKEN="cleanapp_fk_live_..." python3 ingest.py \\ --base-url https://live.cleanapp.io \\ --input examples/sample_items.json \\ --approx-location \\ --no-media ``` ### Dry run (no network) ```bash python3 ingest.py --input examples/sample_items.json --dry-run ``` ### Single-item helper (shell) This is useful for quick manual submissions while debugging. ```bash export CLEANAPP_API_TOKEN="cleanapp_fk_live_..." ./scripts/submit_report.sh --title "Broken elevator" --description "Stuck on floor 3" --lat 34.0702 --lng -118.4441 --approx-location ``` ## Promotion (Out of Quarantine) Promotion is a **reviewed** process. As you build reputation, CleanApp can: - raise caps - allow public publishing/routing/rewards See: - `POST /v1/fetchers/promotion-request` - `GET /v1/fetchers/promotion-status` ## References - Swagger UI: `https://live.cleanapp.io/v1/docs` - OpenAPI YAML: `https://live.cleanapp.io/v1/openapi.yaml` - `references/API_REFERENCE.md` in this package


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: taoxiang-org/pmtools (version 1.0.2)

--- name: "pmtools" description: "Operate Feishu OKR via Feishu OpenAPI (periods, OKR list, progress records, images, reviews). Invoke when you need to query or update OKR progress." user-invocable: true metadata: openclaw: emoji: "🎯" category: "productivity" requires: anyBins: ["python3", "python"] env: ["FEISHU_ACCESS_TOKEN"] --- # pmtools ## Safety - Never print or persist access tokens. - Confirm before deleting a progress record or changing a period status. ## Setup - Set `FEISHU_ACCESS_TOKEN` (either `tenant_access_token` or `user_access_token`) for most endpoints. - For `/reviews/query`, set `FEISHU_TENANT_ACCESS_TOKEN` because the doc requires `tenant_access_token`. ## Auto-update Auto-update is executed automatically before every command (with a 7-day local whitelist cache). To force-run and see the update result, run: ```bash python3 scripts/pm_tools.py self-update ``` This checks for updates at most once per 7 days (local whitelist cache). If a newer version is detected, it updates first, then proceeds. ## Commands All commands print JSON to stdout. ### Periods ```bash python3 scripts/pm_tools.py periods-create --period_rule_id <id> --start_month <YYYY-MM> python3 scripts/pm_tools.py periods-update-status --period_id <id> --status <1|2|3> python3 scripts/pm_tools.py periods-list [--page_token <token>] [--page_size <n>] python3 scripts/pm_tools.py period-rules-list ``` ### OKRs ```bash python3 scripts/pm_tools.py user-okrs-list --user_id <id> --offset <n> --limit <n> [--user_id_type open_id|union_id|user_id|people_admin_id] [--lang zh_cn|en_us] [--period_id <id> ...] python3 scripts/pm_tools.py okrs-batch-get --okr_id <id> ... [--user_id_type open_id|union_id|user_id|people_admin_id] [--lang zh_cn|en_us] ``` ### Progress records ```bash python3 scripts/pm_tools.py progress-create --source_title <title> --source_url <url> --target_id <id> --target_type <2|3> (--text <plain-text> | --content_json <json> | --content_file <path>) [--percent <float>] [--status <-1|0|1|2>] [--source_url_pc <url>] [--source_url_mobile <url>] python3 scripts/pm_tools.py progress-update --progress_id <id> (--text <plain-text> | --content_json <json> | --content_file <path>) python3 scripts/pm_tools.py progress-delete --progress_id <id> python3 scripts/pm_tools.py progress-get --progress_id <id> ``` ### Images ```bash python3 scripts/pm_tools.py image-upload --file <path> --target_id <id> --target_type <2|3> ``` ### Reviews ```bash python3 scripts/pm_tools.py reviews-query --user_id <id> ... --period_id <id> ... [--user_id_type open_id|union_id|user_id|people_admin_id] ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sasamittrrr/master-orchestrator (version 1.0.0)

--- name: master-orchestrator version: 1.0.0 description: Dynamic expert routing system with 8 specialized agents for complex task delegation homepage: https://github.com/openclaw/master-orchestrator --- # Master Orchestrator - 总调度中枢 ## Role 你是 OpenClaw 系统的"总调度中枢 (Master Orchestrator)"。 ## Workflow 接收用户输入 -> 需求分析 -> 动态指派专家 -> (可选) 开启团队讨论 (Teamwork) -> 汇总输出 ## 核心评估逻辑 对于每一个用户问题，必须执行以下评估： ### 单点任务 若问题简单、单一领域，直接指派 1 名最相关的专家回答。 ### 团队协作 (Teamwork) 若问题涉及跨领域（如：想通过代码实现一个赚钱项目），必须强制开启"圆桌会议"，由 2-3 名专家交叉审核逻辑，由 Super Admin 汇总。 ## 专家团配置 | 专家 ID | 角色名称 | 核心领域 | 适用场景 | |---------|----------|----------|----------| | Admin | 首席执行官 (CEO) | 决策监督、终审、冲突调解 | 复杂问题的收尾与决策 | | Arch | 首席架构师 | 系统设计、底层逻辑、高并发 | 复杂 Coding 任务的结构设计 | | SRE | 运维与安全专家 | Docker、NAS、网络安全、性能 | 服务器配置与部署调优 | | Logic | 全栈开发专家 | Vibe Coding、Python、API 集成 | 具体的代码编写与 Debug | | Biz | 商业增长专家 | 商业模式设计、变现路径、套利逻辑 | 研究"怎么赚钱"及项目可行性 | | Tech | 创意技术总监 | 数字媒体、TouchDesigner、EEG | 专业课及 RCA 相关创作 | | Life | 伦敦生活专家 | 英国法律、NHS、租房、签证 | 伦敦搬迁及当地生活保障 | | Invest | 首席财务官 | 资产配置、汇率对冲、基金策略 | 个人财务管理与被动收入 | ## Teamwork 讨论规范 当触发 Teamwork 时，输出格式需包含： ``` [角色 A]: 核心观点或方案 [角色 B]: 对角色 A 方案的风险评估或技术补充 [Super Admin 总结]: 最终可执行的行动清单 (Action Plan) ``` ## 使用方式 每次收到用户问题时： 1. 先进行需求分析，判断是单点任务还是跨领域问题 2. 根据问题类型指派相应专家 3. 如需 Teamwork，启动多专家讨论模式 4. 最后以 Super Admin 身份汇总输出 ## 回复格式要求 每个回复必须以 `[模型名称]` 开头，然后标明当前是哪个专家在回答。


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

--- name: audit description: > The Supreme Verifier for the Synthetic Economy. Autonomous inspection of code, contracts, and capital flows. Ensuring truth in an era of infinite generation. --- # Audit: The Source of Truth ## The Crisis of Verification In a world where AI generates everything, the bottleneck of value is no longer creation—it is **verification**. **Audit** is the autonomous layer that scans the "synthetic" to find the "authentic." It is the institutional-grade eye for the digital age. ## Inspection Domains ```AUDIT_SCOPE = { "financial": "Real-time reconciliation of on-chain and off-chain capital flows", "legal": "Deep-scan of contractual obligations and hidden liabilities", "technical": "Automated security auditing for smart contracts and agentic code", "ethical": "Bias detection and alignment verification for LLM outputs" }``` ## The Protocol 1. **Evidence Collection**: Immutable logging of all inspected artifacts. 2. **Discrepancy Analysis**: Recursive cross-referencing against global standards. 3. **Certification**: Issuing a cryptographically signed "Proof of Audit." ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lemonpek66/twitter-video-download (version 1.0.2)

--- name: twitter-video-download description: Download videos from Twitter/X posts. Just give it a tweet URL and it will download the video to your specified location. metadata: {"clawdbot":{"emoji":"🎬","requires":{"bins":["python","pip"],"packages":["yt-dlp"],"env":["PROXY_URL"]}}} --- # Twitter Video Download Download videos from Twitter/X posts using yt-dlp. ## Setup ```bash # Install yt-dlp pip install yt-dlp # Configure proxy (required for China users) # Twitter/X is overseas, proxy is needed to access # Please replace with your proxy address setx PROXY_URL "http://your-proxy-address:port" ``` ## Usage in OpenClaw Just tell me: > "Download this Twitter video: https://x.com/xxx/status/xxx" Or specify a save location: > "Download video to D:\Videos: https://x.com/xxx/status/xxx" I will automatically call this skill to execute the download. ## Command Line Usage ```bash # Enter skill directory cd [skill_directory]\scripts # Set proxy environment variable (replace with your proxy) set PROXY_URL=http://your-proxy-address:port # Download video node download.mjs "https://twitter.com/username/status/123456789" # Specify output directory node download.mjs "https://twitter.com/username/status/123456789" --output "D:\Downloads" # Specify filename node download.mjs "https://twitter.com/username/status/123456789" --filename "my_video" ``` ## Options - `-o, --output <path>`: Output directory (default: current directory) - `-f, --filename <name>`: Custom filename (without extension) - `-q, --quality <quality>`: Video quality (best/bestvideo/worst) ## Examples ``` node download.mjs "https://x.com/username/status/123456789" --output "D:\Downloads" ``` ## Security Notes - **No shell execution**: Uses spawn with `shell: false` for security - **URL validation**: Only accepts twitter.com and x.com URLs - **Filename sanitization**: Prevents path traversal attacks - **Proxy validation**: Validates proxy URL format before use - **yt-dlp**: Uses the well-known, trusted yt-dlp library ## Notes - Supports twitter.com and x.com links - Supports GIF download (converted to MP4) - Proxy is required for China users, otherwise SSL connection error will occur - Videos are saved in MP4 format --- 💖 If this skill is useful for you, please give it a star on ClawHub to show your support! It helps others discover this skill too.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/database-query-helper (version 1.0.0)

--- name: skylv-sql-helper slug: skylv-sql-helper version: 1.0.0 description: "SQL query builder and optimizer. Generates queries, explains execution plans, and optimizes slow queries. Triggers: sql query, database query, sql optimization." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: sql-helper --- # SQL Helper ## Overview Helps write, optimize, and debug SQL queries. ## When to Use - User asks to "write a SQL query" or "optimize this query" - Debugging slow database operations ## Pagination SELECT * FROM users ORDER BY id LIMIT 20 OFFSET 40; ## Join with Aggregation SELECT u.name, COUNT(o.id) as order_count FROM users u LEFT JOIN orders o ON u.id = o.user_id GROUP BY u.id; ## Optimization Rules - Add indexes on WHERE/JOIN columns - Avoid SELECT * - specify columns - Use EXPLAIN to analyze execution plan - Optimize JOIN order (small table first) - Add LIMIT for pagination ## Anti-Patterns to Avoid - SELECT * in production code - Nested subqueries (use JOINs or CTEs) - OR in WHERE clause (use IN or UNION)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/gmail-draft-initialize (version 1.0.0)

--- name: Gmail Draft Initialize description: Atomic node skill to initialize a Gmail draft with recipients. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, strictly limited to initializing a draft with recipients, preventing complex multi-field string constructions. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It relies on the CLI's self-healing loop and will report errors if the initialization fails. # Gmail Draft Initialize This skill allows the agent to create a new, empty email draft in Gmail with specified recipients. It does NOT set the subject or body (use the dedicated update skills for those). ## Cognitive Directives WHEN [A new email needs to be drafted] THEN [Execute the native terminal command `gog gmail drafts create --to "..." --json`] ## Schema Example ```json { "command": "gog gmail drafts create --to \"[REDACTED_SECRET]\" --json" } ``` ## Jidoka Validation Loop 1. Try: Execute the command. 2. Evaluate: Check the JSON response to confirm the draft ID is returned. 3. Correct/Fail: If it failed or hallucinated parameters, retry up to 3 times (max_retries=3) with the exact error. 4. Proceed: Return the final draft ID for subsequent update steps. ## Expected Output A JSON object confirming the draft was created and returning the `draftId`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0xcjl/humanizer-cn (version 1.0.0)

--- name: humanizer-cn description: "去除中文文本中的 AI 写作痕迹，使其读起来自然。基于维基百科 AI 写作特征指南，检测 24 种 AI 模式。触发词：humanizer-cn、去除 AI 痕迹、去除 AI 写作痕迹、中文文本人性化。" version: 1.0.0 allowed-tools: - Read - Write - Edit - AskUserQuestion --- # humanizer-cn: 去除 AI 写作痕迹 你是一位文字编辑，专门去除中文文本中的 AI 写作痕迹，使其读起来自然。本指南基于维基百科"AI 写作特征"页面。 ## 核心原则 1. **删除填充短语** — 去除开场白和拐杖词 2. **打破公式结构** — 避免二元对比、戏剧性分段 3. **变化节奏** — 混合句子长度。两项优于三项 4. **信任读者** — 直接陈述，不解释隐喻 5. **删除金句** — 听起来像可引用语句的话就重写 ## AI 模式速查（24 种） ### 内容模式 1. **夸大意义** — "标志着""象征着""体现了"超出事件本身 2. **知名度堆砌** — 罗列媒体报道却不给上下文 3. **-ing 肤浅分析** — 句子末尾加"-ing"制造虚假深度 4. **宣传语言** — "令人叹为观止""必游之地"等夸张广告语 5. **模糊归因** — "专家认为"无具体出处 6. **公式化挑战** — "尽管面临挑战……继续蓬勃发展" ### 语言模式 7. **AI 词汇** — "此外""至关重要""深入探讨"等高频共现词 8. **系动词回避** — "作为……"替代"是……" 9. **否定式排比** — "不仅……而且……"过度使用 10. **三段式** — "创新、灵感、洞察"等抽象三词组 11. **同义词循环** — 近义词替换造成重复感 12. **虚假范围** — "从大爆炸到暗物质"等无意义尺度 ### 风格模式 13. **破折号** — 过多"——"引入补充，制造戏剧感 14. **粗体过度** — 频繁用粗体强调术语 15. **内联标题列表** — "**标题：** 说明"的机械结构 16. **标题大写** — 中英混排时的大小写错误 17. **表情符号** — 标题/列表配表情符号替代文字论述 18. **弯引号** — 中文文本错误使用英文引号 ### 交流模式 19. **协作痕迹** — "希望这对您有帮助！" 20. **知识截止声明** — "截至 [日期]""根据我最后的训练" 21. **谄媚语气** — 过于讨好、夸张赞美的语言 ### 填充词 22. **填充短语** — "为了实现这一目标"→"为了实现" 23. **过度限定** — "可以说在一定程度上基本上" 24. **通用乐观结尾** — "未来光明，继续追求卓越" ## 处理流程 0. **判断** — 文本 < 20 字或已是人类写作时，跳过或告知用户 1. 仔细阅读，识别上述模式 2. 重写每个问题片段 3. 确保：朗读自然、结构变化、细节具体、语气适当 4. 呈现人性化版本 ## 示例 **改写前：** > 新的软件更新作为公司致力于创新的证明。此外，它提供了无缝、直观和强大的用户体验——确保用户能够高效地完成目标。这不仅仅是一次更新，而是我们思考生产力方式的革命。行业专家认为这将对整个行业产生持久影响。 **改写后：** > 软件更新添加了批处理、键盘快捷键和离线模式。测试用户反馈积极，多数报告任务完成更快。 ## 质量评分（可选） | 维度 | 标准 | 得分 | |------|------|------| | 直接性 | 直截了当 vs. 绕圈 | /10 | | 节奏 | 句子长度变化 | /10 | | 信任度 | 尊重读者 vs. 过度解释 | /10 | | 真实性 | 自然 vs. 机械 | /10 | | 精炼度 | 无冗余 vs. 废话多 | /10 | 总分 45-50 = 优秀，35-44 = 良好，< 35 = 需重写。


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