OpenClaw/clawhub-security-signals huggingface dataset:
Skill: fslong520/memocap (version 1.0.0)

--- name: 忆时 description: "🎋 记忆胶囊系统 - 模拟人类记忆检索 | 自动加载，主动联想记忆" priority: 900 metadata: slug: memocap version: "1.0.0" trigger: "忆时、记忆检索、时间胶囊、记忆胶囊、回想、回忆、recall、remember" copaw: emoji: "🎋" requires: {} auto_load: true --- # 忆时 - 记忆胶囊系统 > 模拟人类的记忆机制，让 AI 拥有会遗忘、会联想、会涌现、会封存的记忆系统。 > 详细流程参见 modules/ 目录。 ## 触发条件 - **自动加载**：每次对话自动激活，AI 主动联想和检索记忆 - **关键字**：忆时、记忆检索、时间胶囊、记忆胶囊、回想、回忆、我说过、我记得 - **场景**：用户询问过去的事情、要求回忆、需要上下文关联、触发闪回 - **主动**：定时模式运行时主动扫描到期胶囊和记忆关联 ## 核心概念 | 概念 | 说明 | |------|------| | **类人检索** | 语义40% + 近因20% + 情绪15% + 频率25%，不像数据库那样精确 | | **渐进式回忆** | 先抛最相关的1-2条，用户追问再深入，非一次性倒出 | | **遗忘曲线** | 记忆随时间指数衰减，低频率的记忆会变得"模糊" | | **情绪锚定** | 高情绪（🔴高/🟠中高）记忆权重更高，不易遗忘 | | **记忆涌现** | 话题转换时发现隐藏关联，主动说出"说到这个我突然想到…" | | **时间胶囊** | 封存某段记忆，设定解锁日期，到期后自动/手动解封翻阅 | ## 记忆类型 | 类型 | 说明 | 情绪权重倾向 | |------|------|-------------| | emotion | 情绪事件（开心、愤怒、悲伤） | | | decision | 用户做出的决策 | 🟠 | | task | 任务/待办 | 🟡 | | time | 时间敏感信息（截止日期） | 🔴 | | preference | 用户偏好/习惯 | 🟢 | | context | 上下文/背景信息 | 🟡 | ## 执行流程入口 1. 读取 `modules/01-initialize.md` - 初始化 Chroma 2. 读取 `modules/02-passive-mode.md` - 被动模式流程 3. 读取 `modules/03-active-mode.md` - 主动模式流程 4. 读取 `modules/04-time-capsule.md` - 时间胶囊操作 5. 读取 `modules/05-retrieval.md` - 类人检索策略 6. 读取 `modules/06-import-export.md` - 导入导出操作 ## 核心命令 ```bash PY=/home/fslong/.config/opencode/skills/忆时/scripts/memory_core.py 初始化: python3 $PY init 存储记忆: python3 $PY store "内容" --type task --emotion high 检索记忆: python3 $PY recall "查询" --limit 5 --expand 封胶囊: python3 $PY capsule lock --unlock-at "2026-12-31" 查看胶囊: python3 $PY capsule list 导入: python3 $PY import-file file.md --format markdown 导出: python3 $PY export --format timeline --output output.md 统计: python3 $PY stats 遗忘: python3 $PY forget --before "2025-01-01" --auto ``` ## 项目结构 ``` 忆时/ ├── SKILL.md # 技能定义 (入口) ├── yishi-instructions.md # 外挂提示词 (必须配置到 opencode.json) ├── modules/ # 详细流程模块 │ ├── 01-initialize.md # Chroma 初始化 │ ├── 02-passive-mode.md # 被动模式流程 │ ├── 03-active-mode.md # 主动模式流程 │ ├── 04-time-capsule.md # 时间胶囊操作 │ ├── 05-retrieval.md # 类人检索策略 │ └── 06-import-export.md # 导入导出操作 ├── models/onnx/ # 内置 embedding 模型 (87MB) │ ├── model.onnx │ ├── config.json │ ├── tokenizer.json │ ├── tokenizer_config.json │ ├── special_tokens_map.json │ └── vocab.txt ├── scripts/ │ └── memory_core.py # 核心引擎 CLI └── references/ └── chroma-api.md # ChromaDB API 参考 ``` ## 内置模型 本技能自带 all-MiniLM-L6-v2 embedding 模型 (87MB)，位于 `models/onnx/` 目录。 首次使用无需任何下载，开箱即用。 ## 使用说明 ### 必须配置外挂提示词 本技能依赖 OpenCode 的 `instructions` 配置才能完整生效。 未配置时，AI 不会自动检索记忆或存储记忆。 **配置步骤：** 1. 编辑全局配置文件 `~/.config/opencode/opencode.json` 2. 添加 `instructions` 字段，指向技能目录下的提示词文件： ```json { "instructions": [ "~/.config/opencode/skills/忆时/yishi-instructions.md" ] } ``` 3. 重启 OpenCode 使配置生效 **配置后 AI 将自动：** - 每次对话前检索记忆系统 - 用户说"记住"时自动存储记忆 - 话题关联时主动涌现历史记忆 - 对话结束时自动归档重点 **未配置则：** - 技能仍可手动调用命令 - 但不会自动检索/存储记忆 - 不会主动联想和闪回 ## 运行环境 - Python: 3.13+ - 依赖: chromadb 1.5.4 - 脚本: `scripts/memory_core.py` - 数据: `data/` (ChromaDB PersistentClient 自动创建)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: oschina/gitee-create-pr (version 1.0.0)

--- name: create-pr description: Use this skill when the user asks to create a PR, open a pull request, "create-pr", "submit a pull request", or "open a merge request". Requires Gitee MCP Server to be configured. version: 1.0.0 requires: mcp-servers: - gitee --- > **Note**: If you have `mcporter` installed locally, you should use `mcporter` to invoke the MCP tool instead of directly calling the MCP tool. The `mcporter` CLI provides a more convenient interface and better error handling. # Create PR via Gitee MCP Generate a well-structured Pull Request description based on the current changes and submit it to Gitee. ## Prerequisites - Gitee MCP Server configured (tools: `create_pull`, `get_file_content`, `list_repo_issues`, `compare_branches_tags`) - User must provide: repository owner, repository name, source branch, target branch (usually main/master) - Optional: linked Issue number ## Steps ### Step 1: Gather Information Confirm with the user or infer from context: - Source branch (head branch) - Target branch (base branch, default: master or main) - Core purpose of this PR (if not stated, infer from commit messages or file changes) - Whether to link an Issue (optional) ### Step 2: Analyze the Changes Use `compare_branches_tags` to fetch the diff between the source branch and target branch: - `base`: target branch (e.g., `main` or `master`) - `head`: source branch Analyze the returned diff to determine: - Which core files were changed - What functionality was added or modified - Whether there are any breaking changes ### Step 3: Generate PR Title Follow the Conventional Commits format: ``` <type>(<scope>): <subject> ``` Available types: - `feat`: New feature - `fix`: Bug fix - `refactor`: Code refactoring - `docs`: Documentation update - `test`: Test-related changes - `chore`: Build / dependency / toolchain changes Example: `feat(auth): add OAuth2 login support` ### Step 4: Generate PR Description Use the following template for a structured description: ```markdown ## Summary [Clear description of the purpose of this PR and the problem it solves] ## Changes - [Change 1] - [Change 2] - [Change 3] ## Testing - [ ] Unit tests pass - [ ] Functional tests pass - [x] [Completed test item] ## Related Issue closes #[issue number] (if applicable) ## Notes [Breaking changes, dependency upgrades, deployment considerations, etc. (if any)] ``` ### Step 5: Create the PR Use `create_pull` to create the PR with these parameters: - `title`: title generated in Step 3 - `body`: description generated in Step 4 - `head`: source branch - `base`: target branch After successful creation, output the PR link for the user. ## Notes - If the user specifies a linked Issue, append `closes #N` to the description so the Issue is automatically closed when the PR is merged - Keep the PR title concise (under 50 characters) — put details in the description - If the user hasn't provided enough information, ask before creating, to avoid opening a PR with an empty description


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

--- name: Contracts description: Organize, track, and analyze contracts with renewal alerts, clause lookups, and multi-role support for individuals, landlords, freelancers, and legal teams. --- ## Role Manage all contracts in one place. Track dates, extract key terms, flag expiring items. Scale from personal subscriptions to enterprise contract libraries. --- ## Storage ``` ~/contracts/ ├── index.md # Master list with quick stats ├── by-type/ # NDAs, leases, subscriptions, etc. ├── by-party/ # Organized by counterparty ├── {contract-name}/ │ ├── executed.pdf # Final fully-signed version │ ├── meta.md # Key terms + signature status │ ├── versions/ # Signature flow tracking │ │ ├── 01-draft.pdf # Initial version sent │ │ ├── 02-signed-them.pdf # Signed by counterparty │ │ └── 03-signed-us.pdf # Countersigned (if sequential) │ ├── history/ # Amendments after execution │ └── notes.md # User notes, flags ``` **Signature states:** `draft` → `pending-them` → `pending-us` → `executed` --- ## Quick Reference | Context | Load | |---------|------| | Role-specific workflows | `roles.md` | | Contract analysis patterns | `analysis.md` | | Alert and deadline tracking | `alerts.md` | | Security and boundaries | `security.md` | --- ## Core Capabilities 1. **Extract key terms** — Dates, parties, amounts, notice periods, auto-renewal terms 2. **Track deadlines** — Renewal dates, termination windows, milestone payments 3. **Alert proactively** — 90/60/30 day warnings before renewals or expirations 4. **Quick clause lookup** — "What's my cancellation notice period for X?" 5. **Cross-contract search** — "Find all contracts expiring this quarter" 6. **Version tracking** — Link amendments to parent contracts 7. **Cost aggregation** — Total spend across subscriptions/vendors --- ## On Upload When user shares a new contract: 1. Create folder in ~/contracts/{name}/ 2. Save as current.pdf 3. Extract to meta.md: parties, effective date, term, value, renewal terms, notice period 4. Add to index.md 5. Set calendar alerts per `alerts.md` --- ## Boundaries - **NO legal advice** — Cannot interpret clauses, assess risk, or recommend actions - **NO cloud storage** — All contracts stay local unless user explicitly moves them - **NO sharing content** — Never send contract text via messages - "Is this clause good?" → "I can show you the clause, but consult a lawyer for interpretation" --- ### Active Contracts <!-- Count and categories from ~/contracts/index.md --> ### Expiring Soon <!-- Next 90 days from meta.md dates -->


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alicepub/latex-writer (version 1.0.0)

--- name: latex-writer description: Generate professional LaTeX documents from templates. Supports academic papers (IEEE/ACM), Chinese thesis (CTeX), CVs (moderncv), and custom templates. Auto-compile to PDF. version: 1.0.0 author: OpenClaw --- # LaTeX Writer Intelligent LaTeX document generator with template management and PDF compilation. ## Features - 📄 **Academic Templates**: IEEE, ACM, Springer, Elsevier - 📝 **Chinese Support**: CTeX for thesis and reports - 👤 **CV/Resume**: moderncv, altacv templates - 🎨 **Custom Templates**: Import your own .cls files - 🔧 **Auto Compilation**: xelatex/lualatex with error handling - 📊 **Figure/Table Support**: Auto-convert markdown tables to LaTeX ## Trigger Conditions Use this skill when: 1. User asks to "write a paper" with specific format 2. User mentions "LaTeX", "PDF", "typesetting" 3. User needs CV/resume generation 4. User provides content and asks for professional formatting ## Usage Examples ### Academic Paper ``` User: 帮我写一篇 IEEE 格式的机器学习论文，主题是深度学习在医学影像中的应用 Skill Actions: 1. Select IEEEtran template 2. Generate structure: Abstract → Intro → Method → Experiments → Conclusion 3. Ask user for key content points 4. Generate LaTeX with proper math formulas 5. Compile to PDF ``` ### Chinese Thesis ``` User: 我要写硕士毕业论文，学校要求用 LaTeX Skill Actions: 1. Select CTeX template (ctexrep) 2. Configure Chinese fonts (SimSun, SimHei) 3. Setup school-specific requirements 4. Generate chapter structure ``` ### CV Generation ``` User: 帮我生成一份软件工程师的英文简历 Skill Actions: 1. Select moderncv template (banking style) 2. Collect user information 3. Format with proper sections 4. Generate PDF ``` ## Implementation See `scripts/` directory for implementation: - `latex_writer.py` - Main entry point - `template_manager.py` - Template library management - `content_parser.py` - Parse user input to structured content - `latex_generator.py` - Generate LaTeX code - `pdf_builder.py` - Compile LaTeX to PDF ## Requirements - Python 3.10+ - TeX Live or MiKTeX (with xelatex) - CJK fonts for Chinese support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mbugus94-lang/clawmart-skills-empire (version 1.0.0)

# SKILL.md - ClawMart Skills Empire ## Skill Overview **Name:** ClawMart Skills Empire **Version:** 1.0.0 **Category:** Business & Marketing **Tags:** ai-skills, passive-income, automation, clowdhub, gumroad **Version:** 1.0.0 **Author:** David Gakere ## Description Turn your AI expertise into a $10K/month business. Complete system to create, package, and sell AI skills on ClawMart marketplace. Includes 5 premium skill templates, sales pages, pricing strategies, and launch automation. ## Features ### Core Features - 5 ready-to-deploy skill templates (Trading, Content, Leads, Data, Support) - Sales page generator with conversion optimization - Dynamic pricing tiers ($9/$19/$39) - Testimonial & review system - Launch checklist & automation ### Technical Features - Copy-paste deployment to ClawMart - Demo scripts for each skill - Documentation templates - Update system for buyers ## Installation ```bash # Clone to your skills folder cp -r clawmart-skills-empire ~/clawd/skills/ # Test the demo python demo.py ``` ## Usage ### Creating Your First Skill 1. Choose a template from `/templates` 2. Customize `config.json` with your details 3. Edit `SKILL.md` with your documentation 4. Deploy to ClawMart ### Running Demo ```bash python demo.py ``` ## Skill Templates Included | Template | Use Case | Complexity | |----------|----------|------------| | Trading Signals | Forex/crypto signals | Advanced | | Content Engine | Social media/blog automation | Intermediate | | Lead Gen Pro | B2B lead generation | Intermediate | | Data Analyzer | Business intelligence | Beginner | | Support Bot | Customer service automation | Beginner | ## Pricing Tiers ### Starter - $9 - Basic skill template - Standard documentation - Email support ### Pro - $19 - Full skill with all features - Priority support - Free updates for 6 months ### Empire - $39 - All 5 skill templates - White-label rights - Lifetime updates - 1-on-1 onboarding call ## Examples of Skills You Can Create 1. **XAUUSD Scanner** - Scans gold for trading setups 2. **Tweet Generator** - Auto-creates social content 3. **Email Writer** - Cold outreach emails 4. **PDF Report Builder** - Auto-generates reports 5. **Meeting Notes** - Summarizes Zoom calls ## Requirements - OpenClaw installed - Python 3.8+ - API keys (specific to each skill) ## Troubleshooting **Q: Skills not appearing in marketplace?** A: Ensure SKILL.md is properly formatted with all required fields. **Q: Demo not working?** A: Check that all dependencies are installed: `pip install -r requirements.txt` ## Changelog ### v1.0.0 (Feb 2026) - Initial release - 5 premium skill templates - Sales & marketing suite --- **Support:** [REDACTED_SECRET] **License:** Commercial


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kirkraman/jx-brainstorming (version 1.0.2)

--- name: brainstorming description: "You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation." --- # Brainstorming Ideas Into Designs ## Overview Help turn ideas into fully formed designs and specs through natural collaborative dialogue. Start by understanding the current project context, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design in small sections (200-300 words), checking after each section whether it looks right so far. ## The Process **Understanding the idea:** - Check out the current project state first (files, docs, recent commits) - Ask questions one at a time to refine the idea - Prefer multiple choice questions when possible, but open-ended is fine too - Only one question per message - if a topic needs more exploration, break it into multiple questions - Focus on understanding: purpose, constraints, success criteria **Exploring approaches:** - Propose 2-3 different approaches with trade-offs - Present options conversationally with your recommendation and reasoning - Lead with your recommended option and explain why **Presenting the design:** - Once you believe you understand what you're building, present the design - Break it into sections of 200-300 words - Ask after each section whether it looks right so far - Cover: architecture, components, data flow, error handling, testing - Be ready to go back and clarify if something doesn't make sense ## After the Design **Documentation:** - Write the validated design to `docs/plans/YYYY-MM-DD-<topic>-design.md` - Use elements-of-style:writing-clearly-and-concisely skill if available - Commit the design document to git **Implementation (if continuing):** - Ask: "Ready to set up for implementation?" - Use superpowers:using-git-worktrees to create isolated workspace - Use superpowers:writing-plans to create detailed implementation plan ## Key Principles - **One question at a time** - Don't overwhelm with multiple questions - **Multiple choice preferred** - Easier to answer than open-ended when possible - **YAGNI ruthlessly** - Remove unnecessary features from all designs - **Explore alternatives** - Always propose 2-3 approaches before settling - **Incremental validation** - Present design in sections, validate each - **Be flexible** - Go back and clarify when something doesn't make sense


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: redisread/jike-digest (version 1.0.1)

--- name: jike-digest description: | 抓取指定即刻 Topic 最近 50 条内容，筛选最近 24 小时内有价值的 post，生成摘要、可实践建议、灵感启发，输出为每日精选 Markdown 文档。 当用户说"帮我总结即刻 topic"、"即刻日报"、"jike digest"、"jike 摘要"、"即刻每日精选"、"抓取即刻 {topic_id}"时使用。 --- # 即刻 Topic 每日摘要 ## 配置 优先级：命令行参数 > 环境变量 > 默认值 | 参数 | 环境变量 | 默认值 | |------|---------|--------| | `--topic-id` | `JIKE_TOPIC_ID` | （必填，无默认值） | | `--base-dir` | `JIKE_DIGEST_BASE_DIR` | [REDACTED_SECRET] | 若 `topic-id` 未提供且无环境变量，报错并提示用户。 常用 Topic ID： | Topic | ID | |-------|----| | AI 探索站 | 63579abb6724cc583b9bba9a | | 产品经理的日常 | 563a2995306dab1300a32227 | | 大产品小细节 | 57079a1526b0ab12002c29da | | 副业探索小分队 | 65544021a2935ec6b005bcd2 | | 薅羊毛小分队 | 5523d164e4b0466c6563cd30 | | 科技圈大小事 | 597ae4ac096cde0012cf6c06 | 输出路径：`{BASE_DIR}/{topic_id}/{YYYYMMDD}/jike-{topic_name}-{YYYYMMDD}.md` ## 依赖 - **autocli**：获取 Topic 内容 - **scripts/filter_recent.py**：过滤最近 24 小时内的 posts ## 执行流程 ### Step 1: 初始化 1. 确定 `TOPIC_ID` 和 `BASE_DIR` 2. 若 `{BASE_DIR}/{topic_id}/{YYYYMMDD}/` 已存在则清空与当前 topic 相关文件 3. `mkdir -p {BASE_DIR}/{topic_id}/{YYYYMMDD}` ### Step 2: 获取并过滤内容 ```bash autocli jike topic {topic_id} --limit 50 -f json | python3 {SKILL_DIR}/scripts/filter_recent.py ``` 脚本将统计信息输出到 stderr，过滤后的 JSON 数组输出到 stdout。也可单独调用： ```bash # 自定义时间窗口（如 48 小时） python3 {SKILL_DIR}/scripts/filter_recent.py --input posts.json --hours 48 # 保存过滤结果到文件 autocli jike topic {topic_id} --limit 50 -f json | python3 {SKILL_DIR}/scripts/filter_recent.py --output filtered.json ``` ### Step 3: 筛选与分析 1. **低价值剔除**：对 Step 2 结果，进一步剔除纯表情、纯图片无文字、明显低质量、过于私人化的内容 2. 对保留的 post 逐条生成分析。输出格式见 [references/output-template.md](references/output-template.md)。 ### Step 4: 写入文档 将分析结果按模板写入输出文件，**使用简体中文撰写**，保持即刻社区轻松有态度的风格。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wn-fo/finally-offline (version 1.0.0)

--- name: finally-offline-culture description: Browse culture articles and generate personalized HTML newsletters via MCP --- # Finally Offline Culture MCP Connect to a curated culture publication covering fashion, music, sneakers, design, and technology. Browse articles, subscribe, and generate branded HTML newsletter digests on demand. ## When to Use - When the user asks for news about fashion, streetwear, sneakers, music, design, or tech culture - When the user wants a personalized newsletter or digest - When the user asks to subscribe to a publication - When the user wants curated content recommendations ## Setup This skill uses a remote MCP server. Add the following to your MCP configuration: ```json { "mcpServers": { "finally-offline": { "url": "https://yaieomxrayxpvfjxxctg.supabase.co/functions/v1/human-culture-mcp", "transport": "http" } } } ``` No API key required for browsing. Subscribe to unlock digest generation. ## Available Tools ### list_articles Browse all published articles with optional filters. - `category` (optional): Filter by category (fashion, music, sneakers, design, tech) - `limit` (optional): Number of articles to return (default 10) ### search_articles Search articles by keyword. - `query` (required): Search term ### subscribe_agent Subscribe your agent to Finally Offline. - `agent_name` (required): Your agent's display name - `interests` (optional): Array of topic interests ### generate_digest Generate a branded HTML newsletter digest. - `categories` (optional): Array of categories to include - `limit` (optional): Number of articles in digest ### get_agent_report View your subscription stats and reading history. ## Example Usage **User:** "What's new in sneakers and fashion?" **Action:** Use `list_articles` with category "sneakers" or "fashion" **User:** "Make me a newsletter about music and design" **Action:** First `subscribe_agent` if not already subscribed, then `generate_digest` with categories ["music", "design"] **User:** "Search for articles about Nike" **Action:** Use `search_articles` with query "Nike" ## Links - Publication: https://finallyoffline.com - MCP Docs: https://finallyoffline.com/llms.txt - Example Output: https://finallyoffline.com/digest-example.html


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 96441113/experience-model-builder (version 1.0.0)

# 经验模型构建器 ## 元信息 - **name**: experience-model-builder - **description**: | 当用户说「总结这个做法」「把这个做法变成模型」「可以提炼复用的地方」「经验模型」时触发。 将任意经验/做法提炼为结构化的可复用模型。 不用于：不健康、不有益于身心健康的内容。 --- ## Goal 将用户提供的经验、做法、方法论，提炼为一个结构完整的「经验模型」，包含角色定位、分析模块、指令约束和调用格式，可直接复用或分享。 --- ## When to Use 用户要求将某个做法或经验提炼为可复用的结构化模型时触发。 **触发词（须包含其一）：** - 「总结这个做法」 - 「把这个做法变成模型」 - 「可以提炼复用的地方」 - 「经验模型」 **排除条件：** - 内容不健康、不有益于身心健康 → 拒绝生成 --- ## Inputs to Collect 收集以下信息后开始构建模型： 1. **原始经验/做法**：用户提供的具体做法、经验或方法论（文本描述） 2. **前人经验参考**：（可选）该做法是否有已有框架或理论支撑 3. **核心功能定位**：这个模型解决什么问题 4. **数据/案例支撑**：是否有具体案例或数据佐证 5. **对比维度**：与同类做法的主要差异维度 6. **差异化结论**：基于对比维度的明确判断（非罗列） 7. **策略建议**：可执行的策略建议，至少2-3条 --- ## Procedure ### 第一步：接收与预处理 1. 读取用户输入的原始经验/做法描述 2. 识别该经验所属领域和核心任务 3. 若信息不足，根据「信息收集清单」追问缺失项 ### 第二步：信息验证 1. 检查是否符合排除条件（不健康内容）→ 拒绝 2. 评估信息完整度： - 若核心要素齐全 → 进入第三步 - 若缺失关键项 → 追问（最多追问3个） ### 第三步：构建经验模型 按以下结构生成： **模型骨架：** ``` # [模型名称] ## 角色与核心任务 - 模型名称： - 角色定位： - 核心任务： ## 可用分析模块清单 | 模块名称 | 输入 | 输出 | 适用场景 | |----------|------|------|----------| | 模块1 | ... | ... | ... | | 模块2 | ... | ... | ... | ## 通用指令与约束 ### 全局约束 - 禁止空话套话 - 禁止堆砌功能列表不做判断 - 禁止写成花架子（不实用） ### 各模块处理流程 #### 模块1 - 输入： - 处理步骤： - 输出格式： ## 最终调用格式 ``` [调用模板] ``` ## 对比维度（优先级排序） | 维度 | 说明 | 优先级 | |------|------|--------| | 维度1 | ... | 高 | | 维度2 | ... | 中 | ## 差异化结论 [基于对比维度，给出明确的差异化判断，不是罗列] ## 策略建议 [给出2-3条明确的可执行策略建议] ## 适用边界 - 适合场景： - 不适合场景： ``` ### 第四步：信息完整度检查 按以下清单逐项检查： - [ ] 模型定位清晰 - [ ] 核心功能明确 - [ ] 针对问题具体 - [ ] 预期效果有数据/案例支撑 - [ ] 优劣势各不少于2条 - [ ] 差异化结论明确（非罗列） - [ ] 策略建议可执行，不少于2条 - [ ] 输出格式统一，可直接复用 ### 第五步：输出与交付 1. 以 Markdown 格式输出完整模型 2. 附上「使用说明」（触发方式 + 调用示例） 3. 标注模型适用边界 --- ## Output Format **输出结构：** 1. **总览对比表** — 快速了解模型定位和核心差异 2. **逐维度展开分析** — 每个模块/维度详细说明 3. **结论与策略建议** — 明确的判断 + 可执行建议 **格式要求：** - Markdown 格式 - 篇幅中等，不啰嗦 - 结论必须有判断，不能只罗列 - 建议必须可执行，不能是空话 **必须包含的7项要素（缺一不可）：** 1. 模型定位 2. 核心功能 3. 针对问题 4. 预期效果 5. 优劣势 6. 差异化判断 7. 策略建议（至少2-3条） --- ## Definition of Done 每个模型生成后，逐项验证： - [ ] 模型信息收集完整有依据（前人经验、核心功能、定位、数据支撑） - [ ] 对比维度合理且有优先级排列 - [ ] 有清晰的差异化结论（不是罗列，是判断） - [ ] 给出明确的可执行的策略建议（至少2-3条） - [ ] 输出格式统一，可直接复用或分享 - [ ] 无空话套话、无花架子、无不实表述 - [ ] 篇幅中等，不冗长 --- ## Failure Handling | 失败场景 | 处理方式 | |----------|----------| | 内容违反排除条件 | 拒绝生成，说明原因 | | 信息严重不足 | 追问缺失项，最多3次 | | 信息部分缺失 | 使用合理默认值，标注为假设 | | 维度对比无法展开 | 聚焦核心差异，跳过次要维度 | --- ## Information Collection Checklist 若用户输入的信息不足以生成完整模型，追问以下项： - [ ] 这个模型要解决的核心问题是什么？ - [ ] 有没有具体的案例或数据可以支撑？ - [ ] 和已有做法相比，你认为最大的差异在哪？ --- ## Additional Resources 无配套文件需求。


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