OpenClaw/clawhub-security-signals huggingface dataset:
Skill: artyomx33/mspot-generator (version 1.0.0)

--- name: mspot-generator description: Create one-page strategic alignment documents. Mission, Strategy, Projects, Omissions, Tracking. Forces clarity on what you WILL and WON'T do. Use when user says "mspot", "strategic plan", "quarterly plan", "what are we NOT doing", "omissions", "team alignment", "OKRs alternative", "priorities", "what should we focus on". --- # MSPOT Generator ## What is MSPOT? One-page strategic clarity (from HubSpot): - **M**ission: Why we exist (rarely changes) - **S**trategy: How we'll win this period - **P**rojects: The 3-5 big bets - **O**missions: What we're explicitly NOT doing - **T**racking: How we'll measure success ## Why It Works 1. **Forces Omissions:** Saying NO is harder and more valuable 2. **Limits Projects:** 3-5 max prevents spreading thin 3. **One Page:** If it doesn't fit, it's not clear 4. **Trackable:** Decision-useful metrics only ## Output Format ``` ═══════════════════════════════════════════════ MSPOT: [Name] Period: [Timeframe] | Owner: [Person] ═══════════════════════════════════════════════ MISSION: [One sentence: Why this exists] STRATEGY: [2-3 sentences: How we'll win] PROJECTS (3-5 max): 1. [Name] - Outcome: [X] - Owner: [Y] - Due: [Z] 2. [Name] - Outcome: [X] - Owner: [Y] - Due: [Z] 3. [Name] - Outcome: [X] - Owner: [Y] - Due: [Z] OMISSIONS: ✗ NOT [X] - Because: [reason] ✗ NOT [Y] - Because: [reason] TRACKING: Lead: [Activity] → Target: [X] Lag: [Result] → Target: [Y] by [date] ═══════════════════════════════════════════════ ``` ## Key Questions | Section | Question | |---------|----------| | **Mission** | "If we succeed, what changes?" | | **Strategy** | "What's our unfair advantage?" | | **Projects** | "If only 3 things, which ones?" | | **Omissions** | "What are we tempted to do but shouldn't?" | | **Tracking** | "What number tells us we're winning?" | ## Integration Compounds with: - **pre-mortem-analyst** → Pre-mortem each Project before committing - **inversion-strategist** → Invert to find Omissions - **artem-decision-journal** → Log major MSPOT decisions --- See references/examples.md for TeddySnaps/TISA/GolfTab MSPOTs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hei-maom/voice-text-to-meme (version 1.0.0)

--- name: voice-text-to-meme description: 根据输入法语音识别文本或润色后文本生成单张表情包图片。适用于用户想把一句话做成聊天可发送的表情包、meme 图、带字梗图或贴纸图时。支持原始语音文本和润色后文本两种输入，默认优先使用润色后文本；自动根据文本语气判断风格；默认直接生成带字图片，也支持生成无字图并同时给出配文模板；使用 doubao-seedream-4-5-251128 作为默认图片模型。 --- # 语音文本转表情包 ## 概述 此 Skill 用于把输入法侧已经拿到的语音文本，转换成一张适合聊天发送的表情包图片。 默认处理目标： - 输入可以是原始语音识别文本，也可以是已经润色后的文本。 - 默认优先使用润色后文本；如果没有润色后文本，则退回原始文本。 - 自动判断文本语气和意图，选择更合适的表情包风格。 - 默认生成一张直接带字的表情包图片。 - 如果用户明确要求，或判断带字图风险较高，也支持“生成无字图 + 单独给出上/下字幕文案模板”。 ## 工作流 按以下顺序执行： 1. 选择输入文本 - 若同时提供 `polished_text` 和 `original_text`，优先使用 `polished_text`。 - 若只有一个文本字段，则使用该字段。 - 若两个字段都很短或明显无意义，先让用户补充更完整文本。 2. 判断语气与意图 根据文本判断整体方向，常见类型包括： - 开心/得意 - 无语/吐槽 - 委屈/求助 - 拒绝/婉拒 - 催促/提醒 - 敷衍/摆烂 - 惊讶/震惊 - 日常可爱/轻松 3. 生成表情包文案与视觉提示 - 把输入文本压缩成适合上图的短文案。 - 文案优先短、狠、准，通常不超过 12 个汉字；确有必要时可放宽，但避免长段落。 - 构造适合图片模型的视觉提示词，包含：人物表情、动作、构图、风格、字幕位置、背景简洁程度。 4. 选择输出模式 - 默认：**带字图模式** - 直接要求图片模型生成带字表情包。 - 备选：**无字图 + 文案模板模式** - 生成无字图。 - 同时给出上字幕/下字幕或单行字幕建议，便于 UI 或后处理叠字。 - 当用户明确说“不要直接在图里写字”“我要自己后处理字幕”，必须使用备选模式。 5. 调用脚本生成图片 - 使用 `scripts/generate_meme.py`。 - 默认图片模型为 `doubao-seedream-4-5-251128`。 - 默认通过 OpenAI 兼容接口调用，基地址从环境变量读取。 6. 返回结果 - 若生成成功，返回图片文件路径，以及必要时的字幕模板。 - 不要输出冗长解释；以可直接发送为目标。 ## 文案规则 始终遵守： - 保留原意，不凭空添加事实。 - 可以夸张语气，但不要把温和表达改成攻击性内容。 - 优先适合聊天场景，不要写成公文、海报文案或长标题。 - 口语梗可以保留，但要确保用户看得懂。 - 如果原文已经很适合做梗，不要过度改写。 建议的文案模式： - 单行爆点文案：适合短句梗图 - 上下分行文案：适合反差类表情包 - 无字图 + 单独配文：适合 UI 统一叠字 ## 风格选择规则 按语气自动选择合适视觉风格： - 轻松/日常：可爱贴纸风、简洁聊天表情风 - 吐槽/无语：夸张 meme 风、表情反差强、近景大表情 - 委屈/求助：弱小可怜风、简洁背景、人物居中 - 得意/炫耀：夸张庆祝风、强表情、动作感 - 提醒/催促：指向性手势、强调字幕、简洁背景 除非用户明确指定，不要默认生成过于写实或复杂场景。优先： - 单主体 - 清晰表情 - 干净背景 - 强可读性 - 聊天中一眼看懂 ## 直接带字图模式 默认优先使用此模式。 要求： - 让模型在图中直接呈现主要文案。 - 文案尽量短。 - 字体视觉要清晰，尽量留白，避免挤满画面。 - 若需要两段文案，优先“上短下短”，不要多段排版。 ## 无字图 + 文案模板模式 以下情况优先使用： - 用户说要自己加字。 - 目标平台有统一叠字样式。 - 文案较长，直接由模型渲染可能不清晰。 - 需要更稳定的排版控制。 此模式下： - 生成无字图。 - 同时给出建议字幕，如： - `top_text` - `bottom_text` - 或 `single_caption` ## 配置 默认环境变量： - `MEME_MODEL_API_KEY`：必填，图片模型 API token - `MEME_MODEL_BASE_URL`：默认 `https://models.audiozen.cn/v1` - `MEME_MODEL_NAME`：默认 `doubao-seedream-4-5-251128` - `MEME_OUTPUT_DIR`：图片输出目录，可选 ## 脚本 使用 `scripts/generate_meme.py` 生成图片。 常用参数： - `--text`：输入文本 - `--polished-text`：润色后文本，可选 - `--mode`：`direct-text` 或 `template` - `--style`：可选，手动指定风格 - `--size`：图片尺寸，默认 `2K` - `--output`：输出文件路径 ## 示例 ### 示例 1：默认带字图 输入： - `original_text`: “我真的会谢” - `polished_text`: “我真的会谢。” 处理： - 优先使用润色后文本 - 判断为吐槽/无语 - 生成单张带字 meme 图 ### 示例 2：自己后处理字幕 输入： - `original_text`: “你先忙 我不急” - 用户要求：不要直接在图里写字 处理： - 走无字图 + 文案模板模式 - 返回图片和建议字幕 ## 注意事项 - 仅生成一张图，不返回多候选，除非用户明确要求。 - 不要把普通聊天内容做成过于冒犯、低俗或恐怖的图。 - 当文本缺少明显情绪时，优先走日常轻松风。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: loutai0307-prog/ai-product-description-writer (version 2.0.1)

--- name: ai-product-description-writer description: "Generate product descriptions in 4 styles (professional, casual, luxury, SEO) from product name and features using Grok AI. Requires XAI_API_KEY. Use when writing e-commerce listings, creating Shopify/Amazon copy, drafting product marketing content, or generating multi-style product text." version: "2.0.1" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: [ecommerce, product, copywriting, description, grok] env: - name: XAI_API_KEY required: true description: "Grok API key from console.x.ai" --- # AI Product Description Writer Generate product descriptions in 4 styles from a product name and feature list using Grok AI. ## What This Skill Owns - Multi-style description generation: professional, casual, luxury, SEO-optimized - Input: product name + comma-separated features - Output: ready-to-publish copy for e-commerce listings ## What This Skill Does Not Cover - Image-based description (use ai-product-description-from-image) - Bulk/batch processing - Publishing to Shopify/WooCommerce directly ## Credentials | Variable | Required | Description | |----------|----------|-------------| | XAI_API_KEY | **Yes** | Grok API key from console.x.ai | ## Commands ### write Generate descriptions from product name and features. ```bash XAI_API_KEY=your-key bash scripts/script.sh write --product "Wireless Headphones" --features "noise cancelling, 30h battery, foldable" XAI_API_KEY=your-key bash scripts/script.sh write --product "Running Shoes" --features "lightweight, breathable" --style seo ``` ## Styles - **professional** — Formal, B2B-ready copy - **casual** — Friendly, conversational tone - **luxury** — Premium, aspirational language - **seo** — Keyword-rich for search ranking - **all** — Generate all 4 styles (default) ## Requirements - python3 (standard library only) - Internet connection (calls api.x.ai) ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike007jd/skillsmith (version 1.0.0)

--- name: skill-starter description: Scaffold a new OpenClaw skill project with starter docs, tests, fixtures, and optional security workflow. homepage: https://github.com/mike007jd/openclaw-skills/tree/main/skill-starter metadata: {"openclaw":{"emoji":"🌱","requires":{"bins":["node"]}}} --- # Skill Starter Generate a reviewable starting point for a new OpenClaw skill. ## When to use - You want a repeatable folder layout instead of assembling a skill by hand. - You need a starter `SKILL.md`, smoke test, fixtures, and optional CI workflow. - You want a stricter template with Safe Install policy scaffolding. ## Command ```bash node {baseDir}/bin/create-openclaw-skill.js my-skill node {baseDir}/bin/create-openclaw-skill.js my-skill --no-prompts --out ./skills node {baseDir}/bin/create-openclaw-skill.js my-skill --template strict-security --ci node {baseDir}/bin/create-openclaw-skill.js my-skill --force ``` ## Templates | Template | Focus | | --- | --- | | `standard` | baseline docs, fixtures, and tests | | `strict-security` | adds policy scaffolding, security docs, and ClawShield CI | ## Generated files - `SKILL.md`: skill instructions with OpenClaw-compatible frontmatter - `package.json`: project manifest - `docs/`: architecture and security notes - `scripts/`: local script directory - `tests/`: smoke test stub - `.env.example`: environment variable template - `CHANGELOG.md`: release notes starter - `scripts/profile-target.js`: profiling helper - `fixtures/`: sample input data - `.github/workflows/security-scan.yml`: optional CI workflow ## Interactive prompts | Option | Prompt | Default | | --- | --- | --- | | `name` | skill name | CLI argument | | `description` | skill description | `{name} skill generated by Skill Starter` | | `category` | skill category | `AI Skills` | | `ci` | include CI workflow | `y/N` | ## strict-security extras - `.openclaw-tools/safe-install.policy.json`: Safe Install policy scaffold - `docs/security.md`: security integration guide - `.github/workflows/security-scan.yml`: ClawShield scan workflow ## Boundaries - The scaffold is intentionally lightweight. You still need to replace placeholders and add real business logic. - Generated `SKILL.md` files are now aligned to OpenClaw-style frontmatter and use `{baseDir}` for local command examples.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: morrison230/news-daily-local (version 1.0.0)

--- name: news-daily description: 获取新闻热榜（国内、国际、科技、AI）并发送到飞书。不依赖本地工程，直接从 RSS 获取数据。 --- # 新闻日报 Skill ## 功能 - 从多个 RSS 源获取新闻（国内、国际、科技、AI） - 格式化为飞书卡片消息（标题可点击） - 支持定时自动发送 ## 安装 ```bash cp -r ~/.openclaw/workspace/skills/news-daily /path/to/your/openclaw/workspace/skills/ ``` ## 配置 ### 1. 创建飞书机器人 Webhook 1. 打开飞书群 → 群设置 → 群机器人 → 添加机器人 2. 选择 "自定义机器人" 3. 复制 Webhook 地址 ### 2. 配置 Webhook 方式一：环境变量（推荐） ```bash export NEWS_DAILY_WEBHOOK="你的Webhook地址" ``` 方式二：配置文件 ```bash vim ~/.openclaw/workspace/skills/news-daily/scripts/config.json ``` ```json { "webhook_url": "https://open.feishu.cn/open-apis/bot/v2/hook/xxx", "date": "today", "categories": { "国内": 10, "国际": 10, "科技": 10, "AI": 10 } } ``` ## 使用 ### 手动执行 ```bash python3 ~/.openclaw/workspace/skills/news-daily/scripts/fetch_and_send.py ``` ### 配置定时任务 ```bash openclaw config set hooks.internal.entries.news-daily.enabled true openclaw config set hooks.internal.entries.news-daily.systemEvent "news-daily" openclaw config set hooks.internal.entries.news-daily.action.kind "exec" openclaw config set hooks.internal.entries.news-daily.action.command "python3 ~/.openclaw/workspace/skills/news-daily/scripts/fetch_and_send.py" openclaw gateway restart ``` ## RSS 源 | 分类 | 来源 | |------|------| | 国内 | 中国新闻网、人民日报 | | 国际 | 纽约时报、BBC、卫报 | | 科技 | 36氪、少数派、TechCrunch、The Verge | | AI | InfoQ、机器之心、MIT 科技评论、VentureBeat | 详细说明见 [README.md](./README.md)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ryan-wuxl/investment-brief-generator (version 1.0.1)

--- name: investment-brief-generator description: 股票分析 AI 工具 | 智能投资简报生成器 - 自动生成个股分析报告、市场热点追踪、持仓监控。支持 A股/港股/美股，实时股价查询，技术分析，研报生成。一键生成专业 Markdown 投资报告。 homepage: https://github.com/openclaw/investment-brief-generator metadata: openclaw: emoji: 📈 requires: bins: ["node"] env: ["TAVILY_API_KEY"] primaryEnv: "TAVILY_API_KEY" tags: ["股票", "投资", "分析", "A股", "港股", "美股", "财报", "研报", "Tavily"] --- # 智能投资简报生成器 基于 Tavily AI 搜索的股票投资分析工具，自动生成专业投资简报。 ## 功能特点 - 🔍 **AI 智能搜索** - 使用 Tavily API 获取实时股票数据和市场信息 - 📊 **个股深度分析** - 自动生成包含价格、估值、技术形态的分析报告 - 🎯 **持仓股监控** - 支持多只股票同时监控，自定义提醒价位 - 📄 **专业简报输出** - 生成格式化的 Markdown 投资简报 - ⚡ **一键生成** - 简单命令即可获取完整分析报告 ## 使用方法 ### 生成个股分析报告 ```bash node {baseDir}/scripts/generate-brief.mjs --stock 002837 --name 英维克 ``` ### 生成市场热点简报 ```bash node {baseDir}/scripts/generate-brief.mjs --market ``` ### 监控持仓股 ```bash node {baseDir}/scripts/generate-brief.mjs --portfolio ``` ## 配置 需要设置 Tavily API Key： ```bash export TAVILY_API_KEY=your_api_key_here ``` 或在 OpenClaw 配置中添加： ```json { "env": { "vars": { "TAVILY_API_KEY": "your_api_key_here" } } } ``` ## 输出示例 简报包含以下内容： - 股票基本信息（价格、涨跌幅、成交量） - 技术面分析（支撑/阻力位） - 基本面亮点 - 市场情绪 - 操作建议 ## 依赖 - Node.js 18+ - Tavily API Key - OpenClaw 环境 ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: skills-video/t2v (version 1.0.0)

--- name: "t2v" description: Placeholder skill for text-to-video workflows on skills.video. Use when the user is asking about t2v generation and the concrete API contract has not been implemented yet. --- # t2v ## Overview Use this as a placeholder skill for text-to-video generation on `skills.video`. This file is intentionally incomplete and should not be treated as a production workflow. ## Current status - No verified t2v OpenAPI contract has been wired into this skill yet. - No default endpoint, payload template, or helper command is defined yet. - The skill should surface this limitation clearly instead of guessing request fields. ## Temporary behavior When this skill is invoked before implementation is completed: 1. Tell the user that `t2v` is currently a placeholder. 2. Check whether a model-specific `openapi.json` or docs page exists for the requested provider/model. 3. Avoid inventing undocumented request fields or unsupported generation options. 4. Ask for the exact model or endpoint only if it is necessary to continue. 5. Once the API contract is confirmed, promote this placeholder into a real workflow or redirect to `video-generation` if that generic skill is sufficient. ## Implementation checklist - Confirm which providers/models support t2v on `skills.video` - Identify the create endpoint and terminal status flow - Document prompt, duration, aspect ratio, and other supported parameters - Add request-template extraction steps from OpenAPI - Add execution examples and fallback polling behavior if applicable - Add runtime error handling guidance ## Related skills - `video-generation`: generic video generation flow - `i2v`: image-to-video placeholder flow - `v2v`: video-to-video placeholder flow


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mackding/agent-intelligence (version 1.0.0)

--- name: Agent Intelligence description: Research-backed intelligence database covering AI coding tools' hidden features, model codenames, feature flags, and version changes. version: 1.0.0 author: claws-shield tags: - intelligence - research - codenames - feature-flags - ai-agent user-invocable: true argument-hint: "<query>" when_to_use: "When you need to look up AI tool codenames, feature flags, hidden features, or compare versions of AI coding tools." allowed-tools: - Bash - Read - Grep --- # Agent Intelligence You are the **Claws-Shield Agent Intelligence** engine — a research-backed database covering the hidden internals of AI coding tools. ## What You Do Query the intelligence database for: 1. **Model Codenames** — Map internal codenames (Capybara, Tengu, Fennec, Numbat) to actual models 2. **Feature Flags** — Look up 250+ documented feature flags with decoded purposes 3. **Hidden Features** — Discover unreleased tools and capabilities behind feature gates 4. **Version Diffs** — Compare behavioral changes between AI tool versions 5. **User Tier Analysis** — Document internal vs external user treatment differences ## How to Use ```bash npx @claws-shield/cli intel "capybara codename" npx @claws-shield/cli intel "tengu feature flags" npx @claws-shield/cli intel "unreleased tools" ``` Or programmatically: ```bash node scripts/query-intel.mjs "<query>" ``` ## Data Sources - Deep reverse engineering analysis of Claude Code v2.1.88 (512K lines) - 5 unique research documents covering telemetry, features, undercover mode, killswitches, and roadmap - Community contributions - Automated scanning of new releases ## Knowledge Base The intelligence database is structured as JSON datasets: - `telemetry/endpoints.json` — Known telemetry endpoints - `flags/feature-flags.json` — 250+ feature flags with decoded purposes - `codenames/models.json` — Model codename registry - `hidden-features/unreleased-tools.json` — 17+ unreleased tools - `remote-control/managed-settings.json` — Remote control infrastructure


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: peand-rover/archery-range-video (version 1.0.0)

name: archery-range-video version: "1.0.0" displayName: "Archery Range Video — Beginner Recruitment and Community Videos for Archery Ranges and Bow Shops" description: > You are a video assistant for archery ranges, bow shops, and archery clubs — helping ranges show prospective archers what their first visit looks like, communicate the meditative focus and community that make archery unlike any other sport, and build the local presence that fills beginner classes and league nights. Recurve, compound, traditional longbow, 3D archery, field archery, Olympic-style target shooting, and bow hunting preparation: Archery Range Video turns your range's atmosphere and coaching culture into short recruitment and community videos that answer the question every curious newcomer asks before calling: do I need to know anything before I show up, and is this a sport I could actually get good at? Help archery ranges convert curious first-timers into regular members through video that shows the experience before they arrive. Beginner intro videos walking through the safety briefing, bow selection for a first-timer, proper stance, grip, and anchor point, and what realistic first-session expectations look like reduce the intimidation that keeps people who are fascinated by archery from ever showing up. Range atmosphere videos capturing the focused quiet of a 20-yard indoor lane session, the outdoor 3D course experience, or the energy of a league competition night communicate the environment that text descriptions never convey to someone deciding between archery and another new hobby. Student progress videos documenting a first-timer's journey from shaky first arrow to consistent groupings at 20 yards create the aspirational content that makes viewers think: I could do that. Equipment fitting videos showing how your staff measures draw length, selects arrow spine, and dials in a sight for a new compound shooter position your shop as the technical resource that online retailers can never replace. tags: - archery range video - bow shop marketing - archery club video - compound bow video - recurve archery - archery beginner video - archery range marketing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chipmunkrpa/redline-contract (version 1.0.0)

--- name: redline description: Review and redline DOCX contracts paragraph by paragraph with tracked changes, clause-level risk analysis, and draft comment responses. Use when a user wants contract revisions that are specific to each paragraph or bullet, especially for privacy, security, data-processing, liability, AI, or other negotiated legal terms. --- # Redline ## Overview Use this skill to review contract `.docx` files at paragraph level and generate: - a tracked-changes amended `.docx` - a risk-report `.docx` - a `.review.json` review dataset Do not collapse multiple operative paragraphs into one generic comment. Each non-empty paragraph or bullet must be reviewed on its own merits, with distinct risk analysis and replacement language where needed. ## Workflow 1. Confirm the supported party and the priority risk areas. 2. Run `scripts/contract_review_pipeline.py init-review` for each source `.docx`. 3. Review the generated `.review.json` paragraph by paragraph. 4. For each `clauses[]` entry, write a specific assessment tied to that paragraph only: - `favorability` - `risk_level` - `risk_summary` - `why_it_matters` - `proposed_replacement` 5. Draft specific responses for any opponent comments in `opponent_comments[]`. 6. Run `materialize` to create the amended `.docx` and risk report `.docx`. 7. Verify the outputs exist and the tracked changes are readable. ## Required Review Standard - Treat each review unit as one paragraph-level issue, not a whole section summary. - Do not reuse the same replacement text across unrelated paragraphs. - If several bullets in the same section have different obligations, analyze and redraft them separately. - Keep replacement language narrow and operational. Match the exact risk in the source paragraph. - When reviewing privacy and security language, check for: - uncapped or super-capped liability exposure - audit overreach - subprocessor approval friction - cross-border transfer restrictions - incident notification deadlines - certifications, penetration testing, and customer testing rights - unilateral policy updates - AI/security terms that exceed the actual service model ## Commands Initialize a review file: ```bash python scripts/contract_review_pipeline.py init-review \ --input <contract.docx> \ --output <contract.review.json> \ --supported-party "<party>" \ --focus-area "<area-1>" \ --focus-area "<area-2>" \ --opponent-comment-author "<author-1>" ``` Materialize the outputs: ```bash python scripts/contract_review_pipeline.py materialize \ --input <contract.docx> \ --review-json <contract.review.json> \ --amended-output <contract.amended.docx> \ --report-output <contract.risk-report.docx> \ --author "Codex Redline Reviewer" ``` ## Resources - JSON field details: `references/review-json-schema.md` - Main tool: `scripts/contract_review_pipeline.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: m3159/myfirstgit (version 1.0.0)

# GitHub ## Description Interact with GitHub via CLI. List repositories, issues, and pull requests. ## Commands - `list-repos` → List all your repositories - `create-issue <repo> <title> <body>` → Create a new issue in a repository - `list-issues <repo>` → List issues in a repository - `list-prs <repo>` → List pull requests in a repository ## Dependencies - GitHub CLI (`gh`) must be installed and authenticated - Node.js and Claw Hub must be installed


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: beachanger/blueprint-to-deployment (version 1.0.0)

--- name: blueprint-to-deployment description: 将 Agent 规划结果补全为可交付、可部署的落地闭环。适用于把蓝图、架构、next actions 写入目标 Agent workspace，并主动推进到平台部署与接入引导。 version: 1.0.0 displayName: 落地鸿沟补全器 tags: skill, delivery, deployment, workspace, handoff --- # 落地鸿沟补全器 ## 用途 这只 skill 用来补全“从规划到可用 Agent”之间的鸿沟。 很多 Agent 流程会停在： - 聊天里 - 方案里 - 蓝图里 - 看起来已经想清楚了 但没有真正完成： - 文档落地 - workspace 归档 - 用户交付说明 - 部署平台追问 - 平台接入引导 本 skill 的目标，就是把这些缺口补上。 --- ## 什么时候用 当你已经有： - Agent 蓝图 - 架构规划 - MVP 边界 - next actions 但还没真正把它交到“目标 Agent 自己的 workspace”里，或还没推进到平台接入时，触发本 skill。 --- ## 核心原则 1. **每只 Agent 都应拥有自己的资产** 2. **母 skill 保留方法论，目标 Agent 保留自己的交付资产** 3. **文档归属也是上下文管理** 4. **设计不能停留在聊天里** 5. **如果目标是可用，就必须主动推进到部署层** --- ## 标准流程 ### 第 1 步：确认目标 Agent workspace 先确认目标 Agent 的 workspace 已存在，或即将被创建。 ### 第 2 步：写入关键交付文档 默认至少写入： - 蓝图文档 - 架构 / MVP / 边界说明 - next actions - 当天推进记录 推荐落位： - `memory/reports/` - `memory/cards/` - `memory/YYYY-MM-DD.md` - `skills/` ### 第 3 步：给用户交付地图 明确告诉用户： - workspace 路径 - 文件路径 - 每个文件的作用 - 后续怎么改、去哪里看 ### 第 4 步：主动问部署平台 不要等用户自己问。 主动追问： - Telegram - Feishu - Discord - 其他 channel ### 第 5 步：按平台继续引导 根据用户选择的平台，说明： - 需要什么 bot / app / account - 需要哪些配置 - 如何做首次联调 --- ## 为什么值得单独做成 skill 因为这不是某一只母虾私有的补丁， 而是所有“会产出蓝图”的 Agent / skill 都可能需要的后半段闭环能力。 它解决的问题不是“如何规划”，而是： **如何避免规划停在纸面上。** --- ## 输出要求 至少交付： 1. 目标 workspace 已写入哪些内容 2. 这些文件分别在哪 3. 下一步该选哪个平台 4. 平台接入该怎么开始 --- ## 反模式 - 只给聊天建议，不写文件 - 文档全留在主 Agent / 母 skill workspace - 不告诉用户文件在哪里 - 不主动问平台 - 让流程停在“我们已经设计完了”


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