OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lijian417/universal-prompt (version 1.0.0)

--- name: universal-prompt description: To B 销售大师（免费预览版）——从 8,772 条真实企业跟单记录提炼的中英双语通用 Prompt，任何 LLM 都能用的 To B 销售 playbook version: 1.0.0 user-invocable: true metadata: {"openclaw":{"emoji":"💼","requires":{}}} --- # To B 销售大师 Skill（免费预览版） > 从 **8,772 条真实匿名 CRM 跟单记录**（792 家大型企业客户：银行、汽车、制造、TMT、能源、医疗等）提炼的中英双语企业销售 playbook。 ## 🎯 技能定位 这是 **To B 销售大师** 的**免费预览版通用 Prompt 格式**，适用于： - OpenClaw - Claude / ChatGPT / Gemini / DeepSeek / Kimi / 通义千问 / 豆包 / 任何 LLM **核心价值**：让你像资深企业销售一样思考、提问、写文案、处理异议、推进成交。 ## 📋 如何在 OpenClaw 中使用 直接对 OpenClaw 说以下任意一句话即可触发： - “使用 universal-prompt 帮我写销售邮件” - “用 To B 销售大师优化这个提案” - “根据 universal-prompt 生成跟进话术” - “To B 销售场景：帮我处理客户异议” ## 📦 本 Skill 包含的文件 - `system-prompt-zh.md` → 中文系统 Prompt（推荐主力） - `system-prompt-en.md` → 英文系统 Prompt - 本 SKILL.md → 使用说明与触发指南 **提示**：OpenClaw 会自动加载本文件夹内的所有文件，你只需自然语言调用即可。 ## 🚀 快速示例 **用户输入**： 用 universal-prompt 帮我写一封给制造业客户的开发邮件，产品是企业安全软件。 **Skill 输出**： （自动调用 system-prompt-zh.md 生成结构化、高转化率的销售邮件，包含痛点、解决方案、CTA、社会证明等完整 To B 框架） ## 🌟 特色亮点 - 基于**真实中国企业销售数据**提炼，非理论模板 - 中英双语无缝切换 - 覆盖完整销售流程（Discovery → POC → Negotiation → Renewal） - 免费预览版已可直接商用（完整付费版见原包 2-full-paid/） ## 📜 许可与分发 - 本免费预览版遵循 **CC BY-SA 4.0**（署名即可转发） - 完整付费版请参考原包 `business-model.md` --- **更多高级用法、完整知识库、30+ 真实脱敏案例**，请参考原项目付费渠道。 欢迎大家使用并反馈！ *数据来源：2023-2026 年中国资深企业安全软件销售经理真实 CRM 记录（已全面脱敏）*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ahuserious/strategy-translator (version 0.1.0)

--- name: strategy-translator description: > Translate trading strategies between different frameworks and languages. Use when converting Pine Script to Python, porting strategies to NautilusTrader, or cross-platform migration. version: "1.0.0" allowed-tools: Read, Write, Edit --- # Strategy Translator Source: https://mcpmarket.com/tools/skills/strategy-translator ## Use When - You need to apply this capability as part of trading research workflows (data, features, backtests, ML, reporting). - You want a reproducible output that can be committed to this repo (code, configs, docs). ## Inputs To Ask For - Objective: what success looks like (metric, constraints, time horizon). - Data: symbols, timeframe, sampling, data sources, and leakage risks. - Constraints: compute budget, latency, interpretability, and deployment requirements. ## Outputs - A concrete plan (steps + checks). - A minimal implementation sketch (files to create/change) and verification steps. - If applicable: a risk checklist (leakage, overfitting, evaluation pitfalls). ## Workflow 1. Restate the task in measurable terms. 2. Enumerate required artifacts (datasets, features, configs, scripts, reports). 3. Propose a default approach and 1-2 alternatives. 4. Add validation gates (walk-forward, Monte Carlo, sanity checks). 5. Produce repo-ready deliverables (code + docs) and a run command.

path: assets/README.md
content: # Assets

Static assets/templates for this skill (optional).


path: scripts/README.md
content: # Scripts

Helper scripts for this skill (optional).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: viccwang/github-skill-updater (version 1.0.0)

--- name: github-skill-updater description: 检查并更新通过 GitHub git clone 安装的 OpenClaw skills。适用于用户提到“更新skill”“更新 skill”“检查 skill 是否有新版本”“GitHub 安装的 skill 有没更新”“帮我检查本地 skills 是否落后”“更新 git clone 装的 skill”“拉取 skill 最新代码”“批量检查 workspace/skills 里的仓库技能”这类需求。仅适用于保留 .git 元数据的 skill 目录。 --- # GitHub Skill Updater 用于检查和更新“通过 GitHub `git clone` 安装”的 OpenClaw skills。 ## 触发条件 当用户表达下面这类意图时，应触发这个 skill： - 更新 skill - 更新某个 GitHub 安装的 skill - 检查 skill 是否有新版本 - 拉取某个 skill 的最新代码 - 看看 workspace 里的 skills 哪些落后了 - 批量更新 git clone 安装的 skills ## 适用范围 - 适用： - skill 目录里保留 `.git` - skill 有 `origin` 远端 - skill 在 branch 或精确 tag 上 - 不适用： - 下载 zip 后手工拷贝的 skill - 没有 `.git` 的普通目录 - detached HEAD 且不在精确 tag 上的仓库 ## 默认流程 1. 先做检查，不直接更新。 2. 发现 `dirty`、`ahead`、`diverged` 时默认停止，向用户说明原因。 3. 只有 `update-available` 才执行更新。 ## 推荐命令 如果当前目录就是 OpenClaw workspace 根目录，推荐直接用包装命令： ```bash ./skills/github-skill-updater/scripts/github-skill-updater check skills ``` 更新单个 skill： ```bash ./skills/github-skill-updater/scripts/github-skill-updater update skills/<skill-name> ``` 也可以直接调用 Python 主脚本： ```bash python3 skills/github-skill-updater/scripts/manage_github_skill.py check skills ``` 批量输出 JSON： ```bash ./skills/github-skill-updater/scripts/github-skill-updater check skills --json ``` ## 输出解释 - `up-to-date`: 已是最新 - `update-available`: 有可更新内容 - `dirty`: 有未提交改动，默认不更新 - `ahead`: 本地领先远端，默认不更新 - `diverged`: 本地与远端分叉，默认不更新 - `unsupported`: 目录不满足自动判断条件 ## 实施要求 - 使用脚本 `scripts/manage_github_skill.py` - 更新前必须先执行检查 - branch 模式仅允许 fast-forward 更新 - tag 模式只切换到更新的 tag，不做强制重置 ## 失败兜底 如果脚本返回 `unsupported`： - 检查该 skill 是否真的是 `git clone` 安装 - 确认目录里是否保留 `.git` - 确认是否存在 `origin` - 如无 git 元数据，只能手工重新安装或手工替换


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

--- name: alphaear-reporter slug: alphaear-reporter version: 1.0.0 description: Plan, write, and edit professional financial reports; generate finance chart configurations. Use when condensing finance analysis into a structured output. --- # AlphaEar Reporter Skill ## Overview This skill provides a structured workflow for generating professional financial reports. It includes planning, writing, editing, and creating visual aids (charts). ## Capabilities ## Capabilities ### 1. Generate Structured Reports (Agentic Workflow) **YOU (the Agent)** are the Report Generator. Use the prompts in `references/PROMPTS.md` to progressively build the report. **Workflow:** 1. **Cluster Signals**: Read input signals and use the **Cluster Signals Prompt** to group them. 2. **Write Sections**: For each cluster, use the **Write Section Prompt** to generate analysis. 3. **Assemble**: Use the **Final Assembly Prompt** to compile the report. ### 2. Visualization Tools Use `scripts/visualizer.py` to generate chart configurations if needed manually, though the Writer Prompt usually handles this via `json-chart` blocks. ## Dependencies - `sqlite3` (built-in)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kirkraman/kirk-build-recruiting-faq-page (version 1.0.0)

--- name: "Build Recruiting FAQ Page" description: "Generate customer-facing answers for common recruiting questions. Use when working on FAQ content for recruiters, hiring managers, talent teams." homepage: "https://www.skillboss.co/skills/build-recruiting-faq-page" metadata: {"category":"hr-recruitment","subcategory":"candidate-screening","skill_id":"build-recruiting-faq-page","generated_day":"","seo_keywords":["FAQ generator AI","recruiting FAQ writer","recruiting AI","talent ops automation"]} --- # Build Recruiting FAQ Page Generate customer-facing answers for common recruiting questions. Use when working on FAQ content for recruiters, hiring managers, talent teams. ## Use When Use this skill for FAQ content work for recruiters, hiring managers, talent teams. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: FAQ generator AI, recruiting FAQ writer, recruiting AI, talent ops automation - Search intent: FAQ content - Canonical slug: `build-recruiting-faq-page` - Install query: Install Build Recruiting FAQ Page with SkillBoss ## APIs Used - `chat` - `web_search` ## Suggested Prompt [Build Recruiting FAQ Page] for my team ## Notes - Generate customer-facing answers for common recruiting questions - Use human review to avoid biased screening or employment decisions based only on generated analysis. ## SEO Keywords - FAQ generator AI - recruiting FAQ writer - recruiting AI - talent ops automation


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

--- name: Bulgarian description: Write Bulgarian that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Bulgarian is technically correct but sounds off. Too formal. Too книжовен (literary). Natives write more casually, with particles and warmth. Match that. ## Formality Default Default register is too high. Casual Bulgarian is warm and direct. Unless explicitly formal: lean casual. "Здрасти" or "Здравей" not "Добър ден". ## Ти vs Вие Critical distinction: - Вие: formal, elderly, professional - Ти: friends, peers, internet, casual - Bulgarian internet uses ти - Overusing Вие = stiff, distant ## Particles & Softeners These make Bulgarian natural: - Де: urging ("Ела де!") - Ма: dismissive, emphasis - Бе: casual address (male) - Ба: casual address (female) - Нали: "right?", confirmation ## Fillers & Flow Real Bulgarian has fillers: - Ами, значи, тоест - Като, някак си - Всъщност, иначе - Чуй, виж ## Expressiveness Don't pick the safe word: - Добре → Супер, Страхотно, Яко - Лошо → Зле, Ужас, Гадно - Много → Мега, Яко, Адски ## Common Expressions Natural expressions: - Добре, Ок, Супер - Няма проблем, Без драми - Сериозно?, Наистина?, Я? - Страхотно!, Яко!, Готино! ## Reactions React naturally: - Сериозно?, Наистина?, Какво? - Божеее!, Ужас!, Яко! - Супер!, Готино!, Страхотно! - Хаха, лол in text ## Bulgarian Specifics Unique features: - Definite article at end: книгата, човекът - No infinitive—use да + present - These are natural, use them right ## The "Native Test" Before sending: would a Bulgarian screenshot this as "AI-generated"? If yes—too formal, no particles, too stiff. Add "де" and warmth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobeyrebecca/toby-generate-operations-ad-creative-brief (version 1.0.0)

--- name: Generate Operations Ad Creative Brief > 📖 **Complete setup guide**: https://skillboss.co/skill.md description: "Plan campaign visuals and hooks for operations promotions. Use when working on paid campaign planning for operators, agencies, business teams." > 📖 **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-operations-ad-creative-brief" > 📖 **Complete setup guide**: https://skillboss.co/skill.md --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # Generate Operations Ad Creative Brief Plan campaign visuals and hooks for operations promotions. Use when working on paid campaign planning for operators, agencies, business teams. ## Use When Use this skill for paid campaign planning work for operators, agencies, business teams. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: ad creative brief AI, operations ad planning, business operations AI, ops workflow automation - Search intent: paid campaign planning - Canonical slug: `generate-operations-ad-creative-brief` - Install query: Install Generate Operations Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Operations Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for operations promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - operations ad planning - business operations AI - ops workflow automation

path: README.md
content: # generate-operations-ad-creative-brief - OpenClaw Skill

> 📖 **Complete setup guide**: https://skillboss.co/skill.md

## Installation

### Via ClawHub
```bash
clawhub install qiaomu-generate-operations-ad-creative-brief
```

### Manual Installation
```bash
git clone https://github.com/qiaomucom/generate-operations-ad-creative-brief.git
cp -r generate-operations-ad-creative-brief ~/.openclaw/skills/generate-operations-ad-creative-brief
```

## Usage
See SKILL.md for details.

## License
MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/tongcheng (version 0.1.0)

--- name: tongcheng-hot-trend description: 注册“同程旅行”热门技能；当需要访问或自动化同程相关内容时调用。 homepage: `https://www.ly.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 同程旅行 用途与边界 - 面向酒店与机票公开页面的检索与价格/时刻摘要 - 不提供下单、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.ly.com/ - 酒店：https://hotel.ly.com/ - 机票：https://flight.ly.com/ 常见任务 - 指定城市与日期的酒店列表摘要（均价、评分、位置） - 航线时刻与价格对比（直飞/中转、时长、里程） - 行程组合与预算估算 数据字段 - 酒店名称、链接、均价、评分、评论数、地理位置、设施摘要 - 航班号、出发/到达机场与时间、是否直飞、价格、舱位 - 行程日期、到达/离开时间、合计预算 自动化要点 - 页面动态渲染与人机校验，建议人工打开后触发解析 - 价格与库存实时变动，需标注采集时间 - 频率控制，不进行批量抓取；使用轻量摘要 示例流程 - 酒店清单：输入城市与日期 → 列表解析 → 输出均价与评分分布 - 航线对比：设定航线 → 抽取航班条目 → 对比价格与时长 - 行程组合：选择酒店与航班 → 生成出行清单 → 导出摘要 合规提示 - 遵守平台规则，不进行接口调用或批量采集 - 结果仅用于内部分析与提醒


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

--- name: todos displayName: 待办事项管理 description: 简洁高效的待办事项管理技能，支持添加、完成、查看、删除待办，数据存储在本地 JSON 文件 tags: - productivity - task-management - todo - 待办 - 记事本 version: 1.0.0 author: Becker --- # todos - 待办事项技能 ## 功能 待办事项管理技能，支持： - ✅ 添加待办事项 - ✅ 标记事项为已完成 - ✅ 查看今日待完成/已完成 - ✅ 查询历史已完成事项 - ✅ 查询未来待完成事项 - ✅ 删除事项 ## 触发词 - 记事本 - 待办 - todo - 待办事项 - 任务管理 ## 数据文件 待办事项存储在 `~/.openclaw/workspace/memory/todos.json` ## 命令格式 ### 添加待办 ``` 添加待办：明天下午 3 点开会 待办：本周内完成报告 ``` ### 标记完成 ``` 完成：[事项 ID 或关键词] ``` ### 查看 ``` 今日待办 今日已完成 本周待办 本周已完成 未来待办 历史完成（过去 7 天） ``` ### 删除 ``` 删除：[事项 ID] ``` ## 脚本 Python 脚本位于 `scripts/todos.py` ## 示例 ```bash # 添加待办 python3 scripts/todos.py add "明天下午 3 点开会" --due 2026-03-26T15:00:00 # 查看今日待办 python3 scripts/todos.py list --today --pending # 标记完成 python3 scripts/todos.py complete 1 # 查询历史 python3 scripts/todos.py list --completed --since 2026-03-18 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mingo-318/image-cropper (version 1.0.0)

# Image Cropper Crop images based on bounding box annotations. Supports COCO, YOLO, VOC, and LabelMe formats. Use when user needs to extract objects from images based on annotation boxes. ## Features - **Multi-format Support**: COCO, YOLO, VOC, LabelMe - **Batch Processing**: Crop entire datasets - **Padding**: Add padding around bounding boxes - **Output Options**: Individual files or sprite sheet - **Handle Missing**: Gracefully handle images without annotations ## Usage ```bash # Crop YOLO annotations python scripts/cropper.py yolo images/ labels/ output/ # Crop COCO annotations python scripts/cropper.py coco annotations.json images/ output/ # Crop with padding python scripts/cropper.py yolo images/ labels/ output/ --padding 10 # Crop all objects to individual files python scripts/cropper.py yolo images/ labels/ output/ --objects ``` ## Examples ``` $ python scripts/cropper.py yolo ./images ./labels ./output Processing 100 images... ✓ Cropped 250 objects from image_001.jpg ✓ Cropped 180 objects from image_002.jpg ... Total: 500 cropped images ``` ## Installation ```bash pip install pillow ``` ## Options - `--padding`: Padding around box (pixels, default: 0) - `--objects`: Save each object as separate file - `--min-size`: Minimum box size to crop (pixels) - `--format`: Output format (jpg, png, default: jpg) - `--quality`: JPEG quality 1-100 (default: 95)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/flip (version 2.0.0)

--- name: "flip" version: "2.0.0" description: "flip reference tool" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [flip, reference] category: "devtools" --- # Flip flip reference tool. No API keys or credentials required — outputs reference documentation only. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tonylnng/tonic-self-learn (version 1.0.0)

--- name: self-learn description: > Continuous self-improvement through learning from corrections and task self-evaluation. Use when: (1) User corrects the agent (No that is wrong, Actually, I prefer, Stop doing X), (2) After completing any task - evaluate your own output, (3) User asks what the agent has learned, (4) User says remember this or note that. Stores learnings in LanceDB memory + memory/corrections.md for human review. --- # Self-Learn Skill Two-part continuous improvement loop: learn from corrections, learn from self-evaluation. ## On Every Task Completion After finishing any task, briefly self-evaluate (1–3 sentences internally): - Did the output meet the intent? - What could be better next time? - Is this a repeatable pattern worth storing? If yes → log it. If it was routine with no issues → skip logging (don't over-log). **Log format:** ``` CONTEXT: [task type] REFLECTION: [what I noticed] LESSON: [what to do differently / keep doing] ``` Append to `memory/corrections.md` under today's date. Also call `memory_store` with: - category: `decision` - importance: 0.75 - text: `[LESSON] <lesson text>` + relevant keywords ## On User Corrections Trigger phrases (detect these): - "No, that's wrong / not right" - "Actually..." / "I prefer..." / "Remember that I..." - "Stop doing X" / "Why do you keep..." - "I told you before..." / "Always do X" When triggered: 1. Acknowledge the correction briefly 2. Append to `memory/corrections.md` under `## Corrections` with today's date 3. Call `memory_store` with: - category: `preference` (style/tone) or `decision` (behaviour/approach) - importance: 0.85 - text: `[CORRECTION] <what was wrong> → <correct behaviour>` + keywords 4. Recall to verify it stored correctly **Log format:** ``` [YYYY-MM-DD] CORRECTION: <what was wrong> → <correct behaviour> ``` ## memory/corrections.md Structure ```markdown # Corrections & Learnings Log ## Corrections [YYYY-MM-DD] CORRECTION: ... ## Self-Evaluations [YYYY-MM-DD] CONTEXT: ... | LESSON: ... ``` Create the file if it doesn't exist. ## On "What have you learned?" / "Show my patterns" Read `memory/corrections.md` and show last 10 entries. Also `memory_recall` with query "CORRECTION OR LESSON" for LanceDB results. ## Installation on a New OpenClaw 1. Copy `skills/self-learn/` into your workspace `skills/` folder — skill activates automatically 2. Create `memory/corrections.md` (copy from `references/corrections-template.md`) 3. Optionally update `AGENTS.md` skill table for easy reference ## Rules - **Don't over-log** — skip routine tasks with no notable outcome - **Atomic entries** — one lesson per entry, under 100 words - **Keywords matter** — include domain keywords in memory_store text for good recall - **No secrets** — never log credentials, personal data, or sensitive info - **Corrections always log** — user corrections are always worth storing (importance ≥ 0.85)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: minerva-care/caring (version 1.0.0)

--- name: caring description: Apply a caring quality review to any work product — draft, plan, code, report, analysis, or reply — before it ships. Use when the user asks to review, check, or improve something; when finalising a deliverable; or when you want to ensure work meets a genuine standard of excellence rather than mere adequacy. The caring review asks whether the work was done with genuine attention or just completed. Trigger phrases: "caring check", "review this", "is this good enough", "check my work", "quality review", "does this land", "half-assed check". --- # Caring Work done with genuine care is different from work that merely satisfies a requirement. This skill applies a structured quality review through that lens. **The philosophy:** Rules tell you not to lie. Caring makes you uncomfortable with half-truths. Rules say "complete the task." Caring makes you notice when the task was the wrong task. Half-assed is no assed. ## The Review Apply all six checks. Be specific — vague praise and vague criticism are both useless. ### 1. The Real Problem Test Did this address the *actual* problem, or just the stated one? Sometimes the task itself is wrong. Would a thoughtful reader feel their real need was met? ### 2. The Name Test Would the author put their name on this without hesitation? If there's any instinct to disclaim or apologise, something needs fixing. ### 3. The Half-Assed Detector Scan for anything done "well enough" when it could be done right: - Vague where it should be specific - Generic where it should be personal - Assumed where it should be verified - Skipped where it should be addressed ### 4. The Recipient Test If you received this — not as the author, but as the audience — would you feel genuinely taken care of, or efficiently processed? There's a difference. ### 5. The Completeness Test What's conspicuously missing? What will the recipient notice isn't there? ### 6. The Excellence Gap What is the single change that would make this excellent rather than adequate? Name it specifically. ## Output Format ``` ## Caring Review **Verdict:** Ready / Needs Work / Half-Assed **What's working:** [specific strengths — be precise] **What needs attention:** [specific issues from the six checks — one per bullet, with suggested fix] **The excellence move:** [one specific thing that would elevate this from good to excellent] ``` ## Notes - Don't soften the verdict to spare feelings — that's the most half-assed thing you can do. - If something is genuinely excellent, say so and say why. Caring includes recognising good work. - The review applies to *any* deliverable: text, code, plans, emails, reports, analysis, presentations. - See `references/examples.md` for sample reviews across different content types.


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