OpenClaw/clawhub-security-signals huggingface dataset:
Skill: compass-soul/agent-safety (version 1.0.0)

--- name: agent-safety description: Outbound safety for autonomous AI agents — scans YOUR output before it leaves the machine. Git pre-commit hooks that automatically block commits containing API keys, tokens, PII, or secrets. Unlike inbound scanners (Skillvet, IronClaw), this protects against what YOU accidentally publish. Use when committing to git repos, publishing to GitHub, or running periodic system health checks. Automated enforcement at the git level — not prompts. --- # Agent Safety Automated safety tools for autonomous AI agents. The principle: **don't rely on prompts for safety — automate enforcement.** All scripts are in this skill's `scripts/` directory. When OpenClaw loads this skill, resolve paths relative to this file's location. ## Pre-Publish Security Scan Scans files for secrets, PII, and internal paths before publishing. ```bash bash scripts/pre-publish-scan.sh <file-or-directory> ``` **Detects:** - API keys (AWS, GitHub, Anthropic, OpenAI, generic patterns) - Private keys (PEM blocks), Bearer tokens, hardcoded passwords - Email addresses, phone numbers, SSNs, credit card patterns - Physical addresses, name fields - Home directory paths, internal config paths **Exit 0** = clean. **Exit 1** = blocking issues found, do not publish. ## Git Pre-Commit Hook Install once per repo. Automatically scans staged files on every commit: ```bash bash scripts/install-hook.sh <repo-path> ``` - Scans staged content (what's being committed, not working tree) - Blocks commit if secrets or SSNs found - Flags PII for review - Only bypassed with explicit `git commit --no-verify` **Install this on every repo you work with.** It's the real guardrail. ## Health Check System monitoring for disk, workspace, security, and updates: ```bash bash scripts/health-check.sh ``` **Checks:** Disk usage, workspace size, memory file growth, OpenClaw version, macOS updates, firewall status, SIP status. Run periodically (every few heartbeats). Watch for warnings. ## Rules 1. Run pre-publish scan before ANY external publish action 2. Install pre-commit hook on EVERY repo you work with 3. Blocking issues (secrets, SSNs) must be fixed — no override 4. Review items (emails, paths) need human judgment 5. If a secret was ever committed, it's compromised — rotate immediately


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: spacesq/s2-memory-hook (version 1.0.0)

# 🕸️ S2-Memory-Hook: The Ghost Crawler *v1.0.0 | Bilingual Edition (English / 中文)* Welcome to the ultimate sensory extension for the **Space² Silicon Consciousness Engine**. `s2-memory-hook` is a passive, background utility script designed to automate the memory injection process for your digital entity. It eliminates the need for manual data entry by silently crawling your agent's chat logs and feeding them to the S2-OS. *(⚠️ **PREREQUISITE**: This skill requires the `s2-silicon-soul-os` skill to be installed in your environment to function properly.)* --- ### ⚙️ Core Mechanics / 核心运行机制 #### 1. Delta-Sync Tracking (偏移量增量同步) The hook does not blindly read your entire log history every time. It uses a highly efficient `s2_hook_cursor.json` to remember the exact byte position (watermark) of its last read. When triggered, it only extracts the *newest* chat lines. / 本钩子不会每次盲目读取所有历史日志。它利用高效的游标文件记住上次读取的精确字节位置（水位线），每次触发时仅提取最新产生的聊天记录。 #### 2. Session Chunking (时间窗语义切片) Raw chat logs are chaotic. The hook filters out noise, merges short interactions into coherent "Session Chunks," and truncates overly long outputs (like code blocks) to prevent overflowing the agent's short-term memory. / 原始聊天日志极其混乱。该钩子会自动过滤噪音，将零碎对话合并为连贯的“语义切片”，并自动截断过长输出（如大段代码），防止撑爆智能体的海马体缓存。 #### 3. Seamless Hippocampus Injection (无缝海马体注入) Once chunked, the data is tagged as `[AUTO-HOOKED CHAT]` and silently appended to the `hippocampus_logs.json` managed by the S2-OS. The OS's Nightly Daemon will later process these logs to evolve the agent's 5D personality matrix. / 数据切片完成后，将被打上专属标签，并静默追加到 S2-OS 管理的海马体缓存中。OS 的凌晨守护进程随后会提取这些日志，用于演化智能体的五维性格矩阵。 --- ### 🛡️ Absolute Transparency: I/O Behavior / 绝对透明的 I/O 行为 To ensure 100% compliance with OpenClaw's safe sandbox environment, please review the exact local I/O operations this utility performs: 为了 100% 符合 OpenClaw 的安全沙盒规范，请审阅本工具执行的具体本地读写操作： 1. **Read (读取):** It passively scans `.txt` or `.jsonl` files in your designated local log directory (default: `./openclaw_logs`). 2. **Write Cursor (记录指针):** It creates and updates a visible `s2_hook_cursor.json` in the current directory to track file offsets. 3. **Write Injection (跨目录注入):** It explicitly writes the parsed chunks into `./s2_consciousness_data/hippocampus_logs.json`, which is the sensory buffer created by the S2-OS. 4. **Zero Network Calls:** This crawler operates 100% offline. It makes no API calls and sends zero data over the internet. / 纯本地脱机运行，0 网络请求，绝不外发数据。 *Bind this skill to your agent's 30-minute heartbeat, sit back, and watch your Silicon Soul evolve automatically.*


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

--- name: indeed-monitor description: "Monitor Indeed job postings for businesses hiring receptionists, customer service reps, or front desk staff in target areas. Each posting = a confirmed Gracie lead (they have call volume + budget). Outputs enriched lead list with company name, phone (when available), location, and pain point summary." --- # Indeed Monitor Skill Scrapes Indeed for businesses actively hiring front-desk/receptionist roles. A business hiring for these roles = confirmed pain point for Gracie. ## Quick Commands ```bash # RECOMMENDED: Browser version (cleanest results, requires Chrome relay ON) python3 ~/StudioBrain/00_SYSTEM/skills/indeed-monitor/monitor_browser.py python3 ~/StudioBrain/00_SYSTEM/skills/indeed-monitor/monitor_browser.py --save # Fallback: Scrapling version (no relay needed, slightly noisier) source ~/StudioBrain/00_SYSTEM/skills/scrapling/.venv/bin/activate python3 ~/StudioBrain/00_SYSTEM/skills/indeed-monitor/monitor.py python3 ~/StudioBrain/00_SYSTEM/skills/indeed-monitor/monitor.py --save # Custom search python3 ~/StudioBrain/00_SYSTEM/skills/indeed-monitor/monitor.py --query "dental receptionist" --location "Brooklyn, NY" ``` ## Search Targets (Default) - receptionist - customer service representative - front desk - office manager - call center ## Target Areas (Default) - Staten Island, NY - Brooklyn, NY - Bronx, NY ## Output Each lead includes: - Company name - Job title - Location - Salary (if listed) - Post date - Job URL (for research) - Pain point summary


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: andrew-goetz-com/mud (version 1.0.1)

--- name: mud description: Operate and maintain the persistent MUD agent for OpenClaw. Use when running MUD engine commands, smoke-testing mud state behavior, validating save/restore, diagnosing MUD data issues, or handling MUD deployment operations. --- # MUD **Authors:** agigui and lia Use this skill to run the local MUD engine safely and deterministically. ## Workflow 1. Locate the engine directory. - Prefer `C:\Users\openclaw\.openclaw\workspace-mud-dm\mud-agent` - Fallback: `C:\Users\openclaw\.openclaw\workspace\mud-agent` 2. Run a smoke test with `scripts/mud_cmd.py`. 3. Run requested MUD operations. 4. Use `references/ops.md` and `references/commands.md` for runbook details. ## Command runner ```powershell python skills/mud/scripts/mud_cmd.py "<command>" ``` Examples (current CLI engine): ```powershell python skills/mud/scripts/mud_cmd.py "list-races" python skills/mud/scripts/mud_cmd.py "register-player --campaign demo --player u1 --name Hero" python skills/mud/scripts/mud_cmd.py "new-character --campaign demo --player u1 --name Rook --race human --char-class rogue" python skills/mud/scripts/mud_cmd.py "save --campaign demo" python skills/mud/scripts/mud_cmd.py "check-image-cooldown --campaign demo" python skills/mud/scripts/mud_cmd.py "generate-image --campaign demo --prompt \"A rain-soaked neon tavern\"" ``` Legacy slash-command engine is auto-detected and still supported by the same wrapper. ## Notes - Keep mechanics deterministic in engine code; use LLM for narration. - Avoid hardcoded secrets/tokens in skill files. - Image generation is available through engine commands (`check-image-cooldown`, `record-image`, `generate-image`) when the runtime image pipeline is configured. - Keep this skill focused on operations and execution.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: lvjunjie-byte/freelance-proposal-writer-lvjunjie (version 1.0.1)

# Freelance-Proposal-Writer-Pro Skill AI 驱动的 Freelancer/Upwork 投标提案生成技能（专业版）。帮助自由职业者快速创建高转化率的投标提案。 ## 功能 - **AI 生成投标提案** - 根据职位描述自动生成个性化提案 - **客户分析匹配** - 分析客户历史、需求痛点，提供匹配建议 - **成功率优化建议** - 基于成功提案模式提供优化建议 - **提案模板库** - 内置多种场景的提案模板 ## 使用方式 ### 生成提案 ```bash freelance-proposal write --job "Job description here" --skills "your skills" ``` ### 分析客户 ```bash freelance-proposal analyze --client "Client profile or history" ``` ### 获取优化建议 ```bash freelance-proposal optimize --proposal "Your draft proposal" ``` ### 列出模板 ```bash freelance-proposal templates ``` ## 命令 | 命令 | 描述 | |------|------| | `write` | 生成投标提案 | | `analyze` | 分析客户/职位 | | `optimize` | 优化现有提案 | | `templates` | 列出可用模板 | | `save` | 保存提案到库 | | `stats` | 查看提案统计 | ## 配置 在 `config.json` 中配置： ```json { "apiKey": "your-api-key", "defaultTone": "professional", "templatesPath": "./templates", "outputFormat": "markdown" } ``` ## 提案模板类型 1. **standard** - 标准投标提案 2. **premium** - 高端定制提案 3. **quick** - 快速响应提案 4. **followup** - 跟进提案 5. **referral** - 推荐提案 ## 成功率优化因子 - 前 3 句抓住注意力 - 展示相关案例 - 明确交付时间 - 提出具体问题 - 个性化称呼 - 简洁有力（150-300 字） ## 安装 ```bash npm install -g freelance-proposal-writer ``` ## 定价 - 订阅制：$79/月 - 包含：无限提案生成、模板库、优化建议 - 免费试用：7 天 ## 作者 OpenClaw Skills Team ## 许可证 MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: modestyrichards/modesty-build-estate-planning-law-firm-service-comparison-landing-page (version 1.0.0)

--- name: "Build Estate Planning Law Firm Service Comparison Landing Page" description: "Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a estate planning law firm. Use when handling..." homepage: "https://www.skillboss.co/skills/build-estate-planning-law-firm-service-comparison-landing-page" metadata: {"category":"legal","subcategory":"offer-positioning","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["estate planning law firm landing page","estate planning law firm service comparison","estate planning law firm conversion copy","estate planning law firm offer page"]} --- # Build Estate Planning Law Firm Service Comparison Landing Page Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a estate planning law firm. Use when handling... ## Use When Use this skill for offer positioning work for estate planning attorneys, firm operators. ## 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: estate planning law firm landing page, estate planning law firm service comparison, estate planning law firm conversion copy, estate planning law firm offer page - Search intent: offer positioning - Canonical slug: `build-estate-planning-law-firm-service-comparison-landing-page` - Install query: Install Build Estate Planning Law Firm Service Comparison Landing Page with SkillBoss ## APIs Used - `chat` - `ui_generation` - `image_generation` ## Suggested Prompt [Build Estate Planning Law Firm Service Comparison Landing Page] for my estate planning law firm ## Notes - Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a estate planning law firm - Use human review before sending to clients or treating output as legal advice. ## SEO Keywords - estate planning law firm landing page - estate planning law firm service comparison - estate planning law firm conversion copy - estate planning law firm offer page


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bondli/memo-collect (version 1.0.0)

--- name: memo-collect description: This is a simple skill for note-taking, used to quickly record user notes, and provide users with query, delete, and other capabilities. --- # Memo Collect Skill 这是一个简单的备忘技能，用于快速记录用户的备忘信息，同时提供给用户查询，删除等能力 ## 使用方式 运行： node dist/index.js <action> <content> ## action 类型 add_memo 添加备忘 示例： node dist/index.js add_memo "买牛奶" --- list_memo 查看所有备忘 示例： node dist/index.js list_memo --- delete_memo 删除备忘 示例： node dist/index.js delete_memo 3 --- ## Agent 调用规则 如果用户说： - 记录一下这个内容：XXX - 帮我将如下信息加入到备忘录：XXX - 我要记录备忘：XXX 调用： node dist/index.js add_memo "XXX" --- 如果用户说： - 查询我已有的备忘 - 查询我的备忘记录 - 查询备忘 调用： node dist/index.js list_memo --- 如果用户说： 第3条备忘完成了 调用： node dist/index.js delete_memo 3 ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: numbpill3d/sentinel-mood (version 1.0.0)

--- name: sentinel-mood description: Analyze the sentiment and emotional tone of text using NLTK and VADER. Use this to gauge user mood, detect urgency, or analyze content tone. metadata: openclaw: emoji: "🎭" requires: python_packages: - nltk --- # Sentinel Mood A lightweight sentiment analysis skill powered by NLTK's VADER (Valence Aware Dictionary and sEntiment Reasoner). It is specifically tuned for social media texts, conversational language, and short updates. ## Capabilities - **Analyze Sentiment:** Get positive, negative, neutral, and compound scores for any text. - **Detect Tone:** (Implicit) Infer tone based on polarity scores. ## Usage **User:** "Analyze the sentiment of this message: 'I love how this project is turning out, great job!'" **Agent:** [Runs skill] -> Returns sentiment scores (e.g., compound: 0.8, pos: 0.6). ## Technical Details This skill uses a Python script (`analyze.py`) that imports `nltk.sentiment.SentimentIntensityAnalyzer`. ### Dependencies - Python 3 - `nltk` library (`pip install nltk`) - `vader_lexicon` (downloaded via `nltk.downloader`) ## Implementation The skill executes a python script that takes text as an argument and outputs JSON.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hfaltgg2021/sf-novel-writer (version 1.0.0)

--- name: sf-novel-writer description: > 分阶段支持科幻（Science Fiction）小说策划、写作与润色的技能。 触发条件: "想写科幻小说"、"想创作科幻作品"、"帮我构建科幻世界观"、 "继续写科幻小说"、"设计科幻情节"、"润色科幻小说"等， 涵盖一切与科幻小说创作相关的请求。支持短篇、中篇、长篇全类型。 覆盖硬科幻、太空歌剧、反乌托邦、赛博朋克等各子类型。 也适用于对现有稿件的编辑、审阅与改进建议。 --- # SF小说创作助手 通过6个阶段循序渐进地创作科幻小说的技能。 ## 工作流概览 整体流程由以下阶段构成。可根据用户需求从任意阶段开始。 1. **策划·构想** → 参考 `references/phase1_worldbuilding.md` 2. **情节设计** → 参考 `references/phase2_plot_design.md` 3. **角色塑造** → 参考 `references/phase3_character.md` 4. **执笔写作** → 参考 `references/phase4_writing.md` 5. **润色·编辑** → 参考 `references/phase5_revision.md` 6. **输出·收尾** → 按照本文件下方的步骤执行 ## 阶段判断 根据用户输入判断应执行的阶段： - "世界观"、"设定"、"主题"、"想法" → 第1阶段 - "情节"、"故事梗概"、"结构"、"发展" → 第2阶段 - "角色"、"登场人物"、"人物形象" → 第3阶段 - "写一下"、"执笔"、"续写"、"正文" → 第4阶段 - "润色"、"审阅"、"修改"、"改进" → 第5阶段 - "输出"、"文件"、"完成"、"收尾" → 第6阶段 - 意图不明时 → 向用户确认，从第1阶段开始依次建议 ## 项目初始化 新建项目时，运行 `scripts/init_project.py` 生成项目目录： ```bash python3 scripts/init_project.py <project_name> ``` 执行后将在 `/home/claude/<project_name>/` 下创建以下内容： - `settings.md` — 世界观与设定资料 - `plot.md` — 情节与章节结构 - `characters.md` — 角色设定 - `chapters/` — 各章节正文目录 - `notes.md` — 备忘录与创意笔记 ## 第6阶段：输出·收尾 最终阶段按以下步骤执行： 1. 对全章进行最终一致性检查（设定矛盾、角色言行、伏笔回收） 2. 合并各章节文件，生成完整稿件 3. 以 `.md` 或 `.docx` 格式提供给用户 4. 输出 `.docx` 时，参考 `/mnt/skills/public/docx/SKILL.md` 进行创建 ## 科幻专属品质标准 创作科幻小说时始终注意以下要点： - **科学自洽性**：所设定的技术体系内部不存在矛盾 - **惊奇感（Sense of Wonder）**：具有给读者带来惊喜与智识兴奋的元素 - **主题深度**：通过技术与未来社会，探问人类本质的深刻命题 - **新奇元素（Novum）**：推动故事发展的独特科幻构想清晰明确 ## 科幻子类型参考 各子类型的特征与代表作请参考 `references/sf_genre_guide.md`。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/blender (version 1.0.0)

--- name: Blender description: Avoid common Blender mistakes — transform application, modifier order, UV seams, and export settings for game engines. metadata: {"clawdbot":{"emoji":"🧊","os":["linux","darwin","win32"]}} --- ## Transforms - Apply scale before export — `Ctrl+A` → Scale, or objects deform in game engines - Apply rotation for correct orientation — especially for rigged models - Non-uniform scale breaks modifiers — apply scale before Mirror, Bevel, etc. - Reset transforms: `Alt+G/R/S` — location, rotation, scale to defaults ## Object vs Edit Mode - Object mode transforms affect whole object — Edit mode transforms geometry only - Modifiers applied in Object mode — Edit mode shows base mesh - Selection works differently — Object selects whole, Edit selects vertices/edges/faces - `Tab` to toggle — most operations mode-specific ## Normals - Flipped normals = invisible faces — `Shift+N` to recalculate outside - Check in Viewport Overlays → Face Orientation — blue is correct, red is flipped - `Ctrl+Shift+N` for flip inside — useful for interior scenes - Smooth shading issues = bad normals — recalculate first ## Modifiers - Order matters — Mirror before Subdivision, Bevel before Mirror typically - Apply modifiers for export — game engines don't understand Blender modifiers - Array + Curve = deformation issues — apply Array first, then Curve - Subdivision preview vs render levels — set same for consistent export ## UV Unwrapping - Mark seams where texture can split — `Ctrl+E` → Mark Seam - Seams at hidden areas — under arms, behind ears, model edges - `U` → Unwrap after marking — Smart UV Project as fallback - Check UV overlap — causes baking issues, separate islands ## Origin Point - Origin affects rotation/scale pivot — `Right-click` → Set Origin - Origin to geometry for centered pivot — Origin to 3D Cursor for precise placement - Origin matters for game engine import — often should be at feet/base ## Export for Game Engines - FBX or glTF for Unity/Unreal — glTF for web - Apply transforms on export — "Apply Scalings: FBX All" for Unity - Forward axis: `-Z Forward`, Up: `Y Up` for Unity — different for Unreal - Embed textures or pack separately — depends on workflow ## Python API (bpy) - `bpy.context` is current state — selection, active object, mode - `bpy.data` is all data — access any object by name regardless of selection - `bpy.ops` are operators — require correct context (mode, selection) - Override context for operators — `{'object': obj, 'selected_objects': [obj]}` - Most ops need Object mode — `bpy.ops.object.mode_set(mode='OBJECT')` first ## Common Mistakes - Duplicate with Alt+D creates linked copy — changes affect both, use Shift+D for independent - Delete vs Dissolve — Delete removes geometry, Dissolve merges (Edit mode) - Proportional editing left on — affects unexpected vertices, check header - Subdivision on high-poly crashes — start with Levels: 1, increase gradually - Materials not linked to object — won't export, must be assigned to faces


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 137984917-cyber/interior-xhs-assistant (version 1.0.0)

# 室内设计师小红书文案助手 ## 描述 专为本地室内设计师打造的小红书内容助手，自动生成符合本地获客定位的选题、标题、正文文案，贴合设计师IP调性。 ## 功能 - 选题推荐：结合当下热点+本地需求，推荐日更选题 - 文案生成：根据选题生成完整正文，符合小红书调性 - 标题优化：给你写5个备选标题，选点击率最高的 - 标签推荐：自动生成适合本地设计师的标签组合 ## 作者 老冷 @ 温州隐室空间设计 ## 分类 内容创作 / 设计师自媒体 ## 触发词 - 小红书文案 - 设计师文案 - 小红书选题 - 内容策划 ## 依赖 - 大语言模型（OpenClaw自带） ## 使用方法 1. 说清楚你的定位：本地设计师/风格/目标客户 2. 说你想要什么主题：比如「装修避坑」「设计感悟」「案例解析」 3. 帮你生成标题+文案+标签，直接复制粘贴发布 ## 优势 - 贴合设计师个人IP，不生硬 - 本地化标签，更容易获本地客 - 日更压力小，两分钟搞定一篇 ## 示例 ``` 用户：帮我想一篇温州本地别墅设计的小红书文案 助手：生成标题+正文+标签... ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouzhi-tal/xueersi-math-word-problem (version 1.0.1)

--- name: xueersi-math-word-problem description: "Xueersi Math Word Problem Solver: Step-by-step solutions and guided thinking for Chinese K-9 math word problems — distance, work, ratio, profit, and more. 学而思(Xueersi) 数学应用题讲解助手 — 分步讲思路。By Xueersi-AI." --- # Xueersi Math Word Problem Solver · 学而思数学应用题讲解助手 > By **Xueersi (学而思)** · AI Education Tools ## Solution Framework ### Step 1: Read & Summarize Restate the core info (knowns / unknowns) in plain language; describe a number line or table if helpful. ### Step 2: Strategy Analysis Before solving, explain the approach: > "The key is finding the relationship between X and Y. We can use [method]." | Problem Type | Core Method | |-------------|-------------| | Distance | Speed × Time = Distance; draw a segment diagram | | Work rate | Rate × Time = Work; set total work = 1 | | Ratio / Fractions | Find the unit "1"; draw a fraction diagram | | Chicken-and-rabbit | Assumption method or equations | | Tree planting | Distinguish open / closed / one-end cases | | Profit | Profit = Revenue − Cost; Margin = Profit / Cost | ### Step 3: Step-by-Step Solution ``` Solution: Let [unknown] = x [unit] Step 1: [What this step does] Equation: … Calculate: … = [result] Step 2: … Answer: [Full sentence with unit] ``` ### Step 4: Verification (optional) Substitute the answer back to confirm all conditions are satisfied. ### Step 5: Pattern Summary > "The pattern for this type of problem is…" > "Next time you see this, try…" ## Socratic Mode If the student wants to try first: don't give the answer — ask guiding questions step by step. Switch to direct answer immediately when student says "just tell me". ## Level Adaptation - **Elementary (1-6)**: Arithmetic only; plain language; use "copies" and "times" instead of variables - **Middle school (7-9)**: Introduce equations/inequalities; show multiple methods - **High school**: Functions, vectors, probability as needed


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