OpenClaw/clawhub-security-signals huggingface dataset:
Skill: freeman88-tch/trading-system-v4 (version 1.0.0)

--- name: trading-system-v4 displayName: 七窍玲珑战法 v4.0 slug: trading-system-v4 description: "融合七窍玲珑×五维共振×量化闭环的完整交易系统。七维确认（1234567）+五维共振（趋势/位置/形态/时间/信号）+标准化五步闭环（分析→计划→执行→风控→复盘）+趋势/震荡双模式。适用于XAU/USD等所有可交易品种。内置九维排查、凯利仓位管理、市场适应性调整。" version: "1.0.0" author: "智美人团队" tags: - trading - forex - system - xauusd - technical-analysis - risk-management metadata: openclaw: emoji: "📈" --- # 七窍玲珑战法 v4.0 > 七窍玲珑 × 五维共振 × 量化闭环 ## 核心能力 1. **七维确认系统**：①②③④⑤⑥⑦七个关键K线位识别趋势 2. **五维共振分析**：趋势+位置+形态+时间+信号同向确认 3. **标准化五步闭环**：分析→计划→执行→风控→复盘 4. **趋势/震荡双模式**：ADX自动判定，切换策略 5. **九维排查+凯利仓位**：系统化决策+量化风控 ## 适用场景 - XAU/USD（伦敦金）交易 - 所有可交易品种 - 北京时间 20:00-24:00 核心交易窗口 ## 相关文件 完整文档在IMA「交易知识」知识库。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dluolan/label-system (version 1.0.0)

# SKILL.md - 领域标签体系构建 > 构建领域业务标签体系，用于对拆分后的问答对及用户意图打标，配合事项分类体系实现"标签匹配→缩小召回范围→检索重排"的知识检索提升。 --- ## 使用场景 当用户提供一份包含源知识的 Excel 文件（3个Sheet：信息导出/问答导出/拆分后的知识），要求你： 1. 基于源知识构建该领域的**业务标签体系** 2. 对拆分后的问答对进行**打标** 3. 指正拆分不当的知识 --- ## 核心概念 ### 业务标签 - **定义**：表征知识属于什么**业务阶段/场景**，回答"这是关于什么业务的问题" - **层级**：不固定三级，由知识细粒度决定——如果该领域知识集中在某一类场景，可只到二级；如果领域知识覆盖多个阶段/场景，则扩展到三级或更深 - **本质**：为知识打标服务，标签颗粒度要与知识颗粒度匹配 ### 事项分类（独立体系，13类） - **定义**：表征用户意图是什么**问题类型**，回答"用户在问什么" - **层级**：固定13类，不扩展 - **核心区分**：事项分类判断的是咨询内容的类型（材料/流程/时限/渠道等），业务标签判断的是业务阶段 | 维度 | 业务标签 | 事项分类 | |-----|---------|---------| | 本质 | 这是什么业务 | 用户在问什么 | | 层级 | 不固定 | 固定13类 | | 作用 | 匹配业务范畴 | 判断意图类型 | | 扩展 | 随领域增加而扩展 | 不扩展 | --- ## 执行流程 ### 第一步：理解领域背景 在开始打标之前，向用户确认： 1. **领域名称**是什么？ 2. 该领域**核心业务**有哪些？（如公积金领域=缴存/提取/贷款/转移等） 3. 有没有**已有的领域标签体系**可以参考？（如果用户之前构建过类似领域，可以复用结构） ### 第二步：分析源数据结构 读取 Excel 文件，重点分析以下字段： | Sheet | 重点字段 | 分析目的 | |-------|---------|---------| | 信息导出 | 信息标题、正文内容、信息分类（三级归口） | 理解源知识覆盖了哪些业务场景 | | 问答导出 | 问答标题、问答答案 | 理解QA对的知识结构 | | 拆分后的知识 | 信息标题、问题、答案 | 分析拆分粒度是否合理，标签是否匹配 | **分析要点**： - 信息标题 → 反映该知识属于哪个业务环节 - 正文内容/答案 → 理解知识覆盖的具体场景 - 信息分类 → 参考已有的分类体系 - 问句内容 → 判断用户意图，决定打标维度 ### 第三步：构建业务标签体系 根据源知识内容，按以下逻辑构建： **1. 确定二级领域（可选）** 如果该领域跨度大（如同时包含"中国公民业务"和"外国人业务"），先按主体区分二级领域。 **2. 确定业务阶段（一级标签）** 围绕该领域从起点到终点的**业务流程阶段**划分一级标签。 **3. 细化场景标签（二级/三级标签）** 在每个业务阶段下，根据知识的具体场景进一步细分。 #### 标签构建原则 | 原则 | 说明 | |-----|------| | **知识颗粒度决定标签层级** | 如果某一业务阶段的知识高度集中在某一场景，标签到二级即可；如果覆盖多个场景，则扩展到三级 | | **互斥性** | 同一级标签之间应互斥，不重叠 | | **完整性** | 覆盖该领域的主要业务场景，有明确归属的知识都能找到对应标签 | | **可打标性** | 标签颗粒度要与知识颗粒度匹配，确保大多数知识能有明确归属 | ### 第四步：打标执行 #### 标准打标规则 **窗口信息类问答归类**（易错！易打入错误标签！）： | 问题类型 | 应归入标签 | |---------|-----------| | "办理地点/时间/是否支持在线办理" | 办理流程与查询-窗口与办理点查询 | | "咨询电话/监督投诉电话" | 配套服务与咨询-咨询与投诉渠道 | | "办理时限/承诺时限/法定时限" | 办理费用与时限-办理时限咨询 | | "收费标准/收费依据" | 办理费用与时限-证件收费标准 | | "需要哪些材料" | 对应业务阶段的材料准备标签 | | "服务对象是谁/受理条件是什么" | 证件申办-申请资格咨询 | | "办理流程是什么" | 办理流程与查询-窗口与办理点查询 | | "如何咨询/如何投诉" | 配套服务与咨询-咨询与投诉渠道 | | "审批结果/法律依据" | 其他问答（兜底） | **注意事项**： - 窗口信息**不应打入业务主标签**（如"签证类型选择""团队旅游签注办理"），而应归入对应窗口/配套标签 - 中国公民业务和外国人业务的窗口信息，标签前缀不同 - 同一来源拆分出的窗口信息，应保持归类一致性 #### 打标优先级 1. 有明确业务归属的 → 打对应业务标签 2. 窗口信息类 → 按窗口信息归类规则处理 3. 确实无明确归属的 → 打入"其他问答"兜底 ### 第五步：指正拆分问题 对拆分粒度或内容有问题的知识进行标注： | 问题类型 | 处理方式 | |---------|---------| | 信息标题与问题内容不匹配 | 指出错误来源 | | 同一信息拆分出过多无区分度问答 | 建议合并（如多个窗口信息逐条拆出） | | 拆分与源信息正文不对应 | 指出来源核实问题 | | 混入非本领域知识 | 标注应剔除 | --- ## 输出物 ### 1. 标签体系文档 结构： ``` # [领域]领域标签体系 > 基于源知识文件构建，用于... ## 一、领域划分 ## 二、标签体系 ### 领域一：XXX #### 1. 业务阶段A - 标签1 - 标签2 ... ### 领域二：XXX ... ## 三、标签与事项分类的区分说明 ## 四、拆分知识校验意见 ## 五、标签应用示例 ``` ### 2. 打标结果（Excel） 在原Excel基础上增加"业务标签"列，对每条问答标注对应标签。 --- ## 常见问题 **Q：标签层级是固定三级吗？** A：不是。标签层级由知识细粒度决定。如果该领域知识集中在某一类场景，可只到二级；如果覆盖多个阶段/场景，则扩展到三级或更深。参考公积金领域标签体系。 **Q：窗口信息和业务标签冲突时以哪个为准？** A：窗口信息有独立的归类规则，不应打入业务主标签。例如"外国人签证的办理地点在哪里"，应打"窗口与办理点查询"，而非"签证与居留办理"。 **Q：两个标签体系同时打标吗？** A：业务标签和事项分类是独立的两套标签体系，从不同维度对知识进行标注，匹配时也是独立匹配，互不冲突。 **Q：知识拆分粒度不合理怎么办？** A：在标签体系文档的"拆分知识校验意见"章节中指正，说明问题类型和处理建议。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wimi321/workflow-skillify (version 1.0.0)

--- name: "workflow_skillify" description: "Use when a successful session or repeatable process should be captured as a reusable skill with steps, arguments, and invocation guidance." --- # Workflow Skillify Use this skill at the end of a repeatable process to capture it as a standalone skill. ## Workflow 1. Summarize the session and extract user-visible goals, corrections, and constraints. 2. Identify inputs, outputs, artifacts, and success criteria. 3. Interview for missing details only where the workflow is ambiguous. 4. Write a clean `SKILL.md` with trigger conditions, steps, and optional arguments. 5. Save the skill in the appropriate repo-level or personal location. ## Guardrails - Capture the user's corrections, not just the first draft of the process. - Keep the resulting skill concrete and execution-oriented. - Do not over-question simple workflows. ## Example Requests - Turn this successful session into a reusable skill. - Capture the process we just followed as a formal skill. ## Inputs - Session history - User corrections - Desired save scope ## Outputs - Reusable SKILL.md - Trigger and argument guidance ## Success Criteria - The resulting skill captures the real workflow. - User corrections are preserved as rules. - The saved skill is specific and reusable. ## Non-Goals - Over-interviewing simple workflows - Saving a vague process with no triggers or success criteria ## Source Provenance Derived from `src/skills/bundled/skillify.ts`.

path: agents/openai.yaml
content: interface:
  display_name: "Workflow Skillify"
  short_description: "Turn a session into a reusable skill"
  default_prompt: "Extract the repeatable workflow from this session and write a reusable skill specification."


path: references/provenance.md
content: # Provenance

- Skill slug: `workflow-skillify`
- Source module: `src/skills/bundled/skillify.ts`
- Extracted from local Claude Code source analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abycloudcn-jpg/dream-journal (version 1.0.0)

--- name: dream-journal description: 梦境记录与解析工具。触发条件：(1) 用户发送 /记录梦 或描述一个梦境要求记录；(2) 用户发送 /解梦 或要求解析/分析某个梦；(3) 用户询问历史梦境记录（"我上周梦过什么"、"查询梦境"等）。 --- # Dream Journal 梦境记录与解析。数据存储于 `~/.openclaw/memory/dreams/`，每条记录为一个 Markdown 文件，命名格式 `YYYY-MM-DD-NNN.md`。 ## 命令 ### /记录梦 用户口述梦境内容后执行： 1. 请用户描述梦境（如尚未描述） 2. 整理为结构化叙述：清晰的时间线、场景、人物、情绪 3. 提取 3-5 个标签（场景、情绪、意象，用中文） 4. 生成简短标题（10 字以内） 5. 调用脚本保存： ```bash echo '{ "title": "梦境标题", "raw": "用户原始描述", "structured": "整理后的叙述", "tags": ["标签1", "标签2"] }' | python3 ~/.openclaw/workspace/skills/dream-journal/scripts/save_dream.py ``` 6. 回复：已记录「标题」，询问是否需要当场解梦 ### /解梦 解析当前对话中的梦境，或用户指定的历史梦境： 1. 确认要解析的梦境内容（当前描述或查询历史） 2. 按以下维度分析： - **核心意象**：梦中突出的人/物/场景及其象征含义 - **情绪基调**：梦境整体情绪及其可能反映的内心状态 - **潜在关联**：与近期生活压力、愿望或未处理情绪的可能联系 - **整体解读**：综合性解读（不要过度确定，保持开放性） 3. 语气：温和、好奇、不武断，避免"你一定是……"式断言 ### 查询历史 用户问"我最近梦过什么"、"查梦境记录"等： ```bash # 最近20条 echo '{"limit": 20}' | python3 ~/.openclaw/workspace/skills/dream-journal/scripts/list_dreams.py # 按关键词 echo '{"keyword": "飞翔"}' | python3 ~/.openclaw/workspace/skills/dream-journal/scripts/list_dreams.py # 指定日期范围（since 为 YYYY-MM-DD） echo '{"since": "2026-03-01"}' | python3 ~/.openclaw/workspace/skills/dream-journal/scripts/list_dreams.py ``` 查询后，读取相关文件内容后向用户汇报。 ## 存储格式 ``` ~/.openclaw/memory/dreams/ ├── 2026-03-11-001.md ├── 2026-03-11-002.md └── ... ``` 每个文件结构： ```markdown --- date: 2026-03-11 08:30 title: 梦境标题 tags: [飞翔, 焦虑, 学校] --- ## 原始描述 用户口述的原始内容 ## 整理版本 AI整理后的结构化叙述 ``` ## 注意事项 - 记录时不评判梦境内容，如实保存 - 解梦时区分"可能"与"确定"，避免过度解读 - 若用户只想记录不想解梦，尊重意愿，不主动解析


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: godiao/hf-daily-papers (version 1.0.0)

--- name: hf-daily-papers description: "Fetch and digest HuggingFace Daily Papers. Use when user asks for today's HF papers, daily paper digest, wants a paper report, or says 论文精选/今日论文/HF daily papers. Fetches from hf.co/papers via HF API, reads results, scores and generates a formatted digest with commentary." --- # HF Daily Papers Fetch papers from HuggingFace Daily Papers feed and generate a digest with analysis. ## Setup: Get Your HF Token 1. Go to **https://huggingface.co/settings/tokens** 2. Create a Read token (any name) 3. Set the token as an environment variable: ```powershell # Windows PowerShell $env:HF_TOKEN = "hf_xxxxxxxxxxxxx" # macOS / Linux / Git Bash export HF_TOKEN="hf_xxxxxxxxxxxxx" ``` > The script reads `HF_TOKEN` from `os.environ`. If not set, it exits with a clear error message. ## Step 1: Run the fetcher ```bash cd <skill-path>/scripts && python hf_papers.py [date YYYY-MM-DD] ``` - No date arg = yesterday - Output: `hf_results.json` (saved in the working directory) ## Step 2: Read results Read `hf_results.json`. Fields: | Field | Description | |-------|-------------| | `paperId` | arXiv ID | | `title` | Paper title | | `votes` | Community upvotes | | `submittedBy` | Submitter name | | `organization` | Research institution | | `summary` | Full abstract (cleaned, up to 2000 chars) | | `aiSummary` | AI-generated summary (200-300 chars, from HF blue box) | | `githubRepo` | GitHub repo URL if available | | `keywords` | AI-extracted keywords (up to 10) | | `link` | HF paper page | | `arxivLink` | arXiv abstract page | ## Step 3: Score and write digest Scoring reference (10-point scale, intuition-based): | Dimension | Weight | Bonus signals | |-----------|--------|---------------| | Innovation | 0-3 | New benchmark/dataset, novel direction, first-of-its-kind | | Practicality | 0-3 | Has GitHub code, clear real-world application, big tech/academia | | Technical depth | 0-2 | Summary >200 chars, contains RL/MCTS/evolutionary methods | | Interestingness | 0-2 | Provocative thesis, cross-discipline, counterintuitive | **High vote count (>10) is a bonus** — reflects community heat. ## Step 4: Output format ``` 📄 HF Daily Papers · [date] N papers total ## 🔴 Must Read (score 8-10) [Title | ID | Organization Xiaolongxia comment: ...] ## 🟡 Worth Noting (score 6-7) [Compact list + one-line evaluation] ## 🟢 Skim If Interested [Ultra brief list] ## 🦞 Summary Top 3 + today's main theme observations ``` Commentary guidelines: - Every "Must Read" paper needs a "Xiaolongxia comment" — explain the core insight in your own words - Say why it's worth reading and what makes it special - Can connect to other papers or industry trends - Tone: casual, witty, friendly — like chatting with a friend - "Worth Noting" entries: one sentence max


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ohernandez-dev-blossom/bcrypt-generate (version 1.0.0)

--- name: bcrypt-generate description: Hash passwords using bcrypt or verify a password against a bcrypt hash. Use when the user asks to bcrypt a password, generate a bcrypt hash, check if a password matches a hash, or store a password securely. metadata: {"openclaw":{"requires":{"bins":["python3"]}}} --- # Bcrypt Generate Hash passwords with bcrypt or verify existing hashes using Python's `bcrypt` library. ## Input **For hashing:** - Password string to hash - Cost/rounds (default: 10, range: 4–31) **For verification:** - Password string - Existing bcrypt hash string (starts with `$2b$` or `$2a$`) ## Output - Bcrypt hash string (for hashing mode) - True/False result (for verification mode) ## Instructions 1. Determine mode: hash a new password, or verify against an existing hash. 2. **Hashing a password:** ``` python3 -c "import bcrypt; print(bcrypt.hashpw(b'PASSWORD', bcrypt.gensalt(rounds=ROUNDS)).decode())" ``` Replace `PASSWORD` with the actual password and `ROUNDS` with the cost factor (default 10). 3. **Verifying a password against a hash:** ``` python3 -c "import bcrypt; print(bcrypt.checkpw(b'PASSWORD', b'HASH'))" ``` Replace `PASSWORD` and `HASH` with the actual values. 4. Check if `bcrypt` Python package is available before running: ``` python3 -c "import bcrypt" 2>&1 ``` If it fails with `ModuleNotFoundError`, tell the user: > "This skill requires the Python `bcrypt` package. Install with: `pip3 install bcrypt`." 5. If `python3` is not found at all, tell the user: > "This skill requires `python3`. Install with: `brew install python3` (macOS) or `sudo apt install python3` (Linux)." 6. Present the hash output on its own line. For verification, report clearly: "Password MATCHES the hash" or "Password does NOT match the hash." ## Examples **Hash password "mysecret" with cost 10:** **Command:** `python3 -c "import bcrypt; print(bcrypt.hashpw(b'mysecret', bcrypt.gensalt(rounds=10)).decode())"` **Output:** `$2b$10$EixZaYVK1fsbw1ZfbX3OXePaWxn96p36WQoeG6Lruj3vjPGga31lW` **Hash password "admin" with cost 12:** **Command:** `python3 -c "import bcrypt; print(bcrypt.hashpw(b'admin', bcrypt.gensalt(rounds=12)).decode())"` **Output:** `$2b$12$...` (60-char bcrypt hash) **Verify "mysecret" against `$2b$10$abc...`:** **Command:** `python3 -c "import bcrypt; print(bcrypt.checkpw(b'mysecret', b'\$2b\$10\$abc...'))"` **Output:** `True` ## Error Handling - `python3` not found → tell user to install Python 3 - `bcrypt` module not found → tell user to run `pip3 install bcrypt` - Password contains single quotes → escape them or note that the command must be adjusted; prefer using a temp Python script file for complex passwords - Hash string malformed (does not start with `$2b$` or `$2a$`) → warn the user the hash appears invalid before running - High cost factor (>= 14) → warn the user this will be slow (intentional for security)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: laolaoqi/log-sanitizer (version 1.0.0)

--- name: Log Privacy Sanitizer description: > Scans log files and sanitizes sensitive data before sharing or storing. Detects and masks IP addresses, email addresses, API keys, passwords, tokens, credit card numbers, and custom patterns. Preserves log structure and timestamps. --- # Log Privacy Sanitizer **保护你的日志隐私。清理敏感信息再分享日志给AI或第三方。** ## Actions | Action | Description | |--------|-------------| | `scan` | 扫描日志文件，报告发现的敏感信息类型和数量（只读） | | `sanitize` | 生成清理后的日志副本 | | `config` | 自定义过滤规则 | ## Why? - 分享日志给 AI 助手前先脱敏 - 合规要求（等保、GDPR） - 防止 API key 和密码泄露 ## Pricing $2.99 — One-time purchase


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: coolhackboy/poyo-nano-banana (version 1.0.0)

--- name: poyo-nano-banana description: Use PoYo AI Nano Banana for general image generation and image editing across many aspect ratios through the `https://api.poyo.ai/api/generate/submit` endpoint. Use when a user wants prompt-based images, reference-image edits, wide or tall canvases, or PoYo payloads for `nano-banana` and `nano-banana-edit`. metadata: {"openclaw": {"homepage": "https://docs.poyo.ai/api-manual/image-series/nano-banana", "requires": {"bins": ["curl"], "env": ["POYO_API_KEY"]}, "primaryEnv": "POYO_API_KEY"}} --- # PoYo Nano Banana Image Generation and Editing Use this skill to submit and track PoYo jobs for the Nano Banana family. ## Quick workflow 1. Choose the right model id for the requested output. 2. Build the request body for `POST https://api.poyo.ai/api/generate/submit`. 3. Send Bearer-authenticated JSON with `Authorization: Bearer <POYO_API_KEY>`. 4. Save the returned `task_id`. 5. Poll unified task status or wait for `callback_url` notifications. ## Request rules - Require top-level `model`. - Keep prompts concrete and outcome-focused. - Require `input.prompt` unless the user already supplied a full payload. - Use `input.image_urls` only when the task needs reference or source images. - Use `input.size` to match the intended aspect ratio or canvas. ## Model selection ### `nano-banana` Use for general generation with this model variant. ### `nano-banana-edit` Use for editing or modifying supplied images. ## Execution - Read `references/api.md` for endpoint details, model ids, key fields, example payloads, and polling notes. - Use `scripts/submit_nano_banana.sh` to submit a raw JSON payload from the shell. - If the user only needs a curl example, adapt the example from `references/api.md` instead of rewriting from scratch. - After submission, report the `task_id` clearly so follow-up polling is easy. ## Output expectations When helping with this model family, include: - chosen model id - final payload or a concise parameter summary - whether reference images are involved - returned `task_id` if a request was actually submitted - next step: poll status or wait for webhook Notes: Needs POYO_API_KEY from https://poyo.ai PoYo.ai - Premium AI API Platform | Image, Video, Music & Chat APIs - 80% Cheaper


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ecosincronia/reaction-processor (version 1.0.0)

--- name: reaction-processor description: Record real OpenClaw events and reactions for the current closed-loop workflow. Use for logging duplicate-skipped and proposal-created outcomes in public.openclaw_agent_events and public.openclaw_agent_reactions, and for updating trigger fire metadata when appropriate. metadata: {"clawdbot":{"notes":["Uses only public.openclaw_* tables","Current implementation supports the stale_missions_alert flow","Wraps the real event/reaction pattern already present in stale_missions_engine.sh"]}} --- # Reaction Processor Record real events and reactions for the current OpenClaw closed loop. ## Commands - Record duplicate skipped outcome for stale missions `{baseDir}/scripts/reaction-processor.sh record-duplicate-skipped` - Record proposal created outcome for stale missions `{baseDir}/scripts/reaction-processor.sh record-proposal-created` ## Notes - This skill uses the current real event/reaction pattern already implemented in the workspace. - It works against `public.openclaw_agent_events`, `public.openclaw_agent_reactions`, and `public.openclaw_trigger_rules`. - It is currently scoped to the `stale_missions_alert` flow.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhubao315/deep-read-analysis (version 1.0.0)

# Deep Read **显示名称**: Deep Read **版本**: 1.0.0 **描述**: 基于研究驱动方法，对书籍进行深度分析，提炼核心观点、金句，并生成实际应用指导和关联常青笔记。 **作者**: Zhu Bao ## 输入参数 - `book_title` (string, 必填)：书名 - `book_file` (file, 可选)：书籍文件（PDF/EPUB/TXT） - `user_notes` (list, 可选)：用户已录入的笔记，用于生成关联常青笔记 ## 输出参数 - `author_info` (string)：作者介绍（生平、教育、主要著作、影响等） - `creation_context` (string)：创作背景（时间、社会环境、动机、评论等） - `book_outline` (string)：章节结构、大纲及核心观点 - `main_content` (string)：书籍主要内容解读与分析 - `core_insights` (string)：书籍最重要思想和见解 - `key_quotes` (list)：引人深思的金句及分析 - `application_examples` (list)：生活、学习和工作中的应用指导 - `related_evergreen_notes` (list)：基于用户笔记生成的常青笔记标题及链接 ## 标签 #deep-reading #knowledge-management #research-driven #book-analysis #insights ## 使用示例 ```python from deep_read_generator import deep_read_analysis result = deep_read_analysis( book_title="原则", user_notes=["已读章节摘要", "高亮笔记"] ) print(result['core_insights']) print(result['application_examples']) print(result['related_evergreen_notes'])


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wangzhiming1999/refactor-safely (version 1.0.1)

--- name: refactor-safely description: Plans and executes safe refactors with small steps, tests, and rollback strategy. Use when 重构, 技术债, 安全重构, refactoring, or improving code structure without breaking behavior. --- # 安全重构（Refactor Safely） 在不改变对外行为的前提下，分步、可验证地改善代码结构，降低技术债与回归风险。 ## 触发场景 - 用户说「重构这段代码」「清理技术债」「安全重构」「改善结构但不要改行为」 - 代码难以维护、重复多、职责混乱、难以加测试 ## 原则 - **行为不变**：重构前后功能与边界行为一致（含异常、边界值） - **小步提交**：每步可单独提交、易回滚、易 code review - **先测后动**：有测试则先绿再动；无测试则先补关键路径再重构 - **可随时停**：任意一步停下，代码仍处于可运行、可发布状态 ## 执行流程 ### 0. 判断是否适合重构 先问自己三个问题，任意一个答案是"否"就先解决它再重构： - 有没有测试覆盖核心路径？（没有 → 先补关键路径测试） - 重构范围是否清晰？（不清晰 → 先和用户对齐范围） - 当前代码能跑通吗？（跑不通 → 先修 bug 再重构） ### 1. 明确范围与目标 - **范围**：单文件 / 单模块 / 跨模块（列出受影响入口与调用方） - **目标**：例如「抽公共函数」「拆组件」「统一错误处理」「去掉重复」 - **不做什么**：明确不在此次重构里改的需求或行为 ### 2. 识别依赖与风险 - 谁在调用当前代码（组件、API、路由、全局状态） - 是否有隐式依赖（全局变量、副作用、执行顺序） - 高风险点：无类型、无测试、多入口共用的逻辑 ### 3. 拆分步骤（每步可单独提交） | 步骤类型 | 示例 | |----------|----------| | 仅移动 | 把函数/组件挪到新文件，改 import，跑通 | | 仅重命名 | 变量/函数/文件重命名，全局替换后跑通 | | 抽离不改逻辑 | 抽子函数/子组件，原逻辑不变，调用处改为用新的 | | 替换实现 | 用新实现替换旧实现，对外接口不变 | | 删废弃 | 确认无引用后删除死代码或废弃 API | ### 4. 每步验证 - 构建通过、类型通过、现有测试通过 - 若有可能：关键页面/流程手工点一遍或跑 E2E - 提交信息写清本步做了什么（如 "refactor: extract getDisplayName"） ### 5. 回滚策略 - 每步在版本控制里独立提交，便于 `git revert` - 若涉及配置或数据，注明回滚时需恢复的内容 ## 输出模板 ```markdown ## 安全重构计划 ### 目标与范围 - 目标：… - 范围：…（文件/模块列表） - 不改变：…（行为/接口约定） ### 依赖与风险 - 调用方：… - 风险点：… ### 步骤（每步可单独提交） 1. … — 验证：… 2. … — 验证：… 3. … ### 回滚 - 按提交逐条 revert 即可 / 需注意：… ``` ## 常见模式 - **抽函数**：先复制一份成新函数，调用处改为新函数，再删旧实现 - **抽组件**：先在新文件写组件，原处用新组件包裹原 JSX，再逐步把 props 捋清 - **改数据结构**：可先兼容旧结构（新旧并存），再逐步迁移调用方，最后删旧结构 - **重命名**：用 IDE 重命名/全局替换，避免手改漏改


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: skbylife/athena-protocol (version 1.2.0)

# Athena Protocol A modular identity and communication framework for personal AI assistants on OpenClaw. **Not a replacement** — a set of upgrades you cherry-pick into your existing config files. ## Quick Reference | What you want | Which file | Where to add it | |---------------|-----------|-----------------| | AI that never flatters you | `protocol.md` → "Six Principles" | SOUL.md | | AI that only listens to you | `protocol.md` → "Loyalty Architecture" | SOUL.md | | Persistent memory across sessions | `memory-architecture.md` → "Two-Layer Memory" | AGENTS.md | | AI that starts each session oriented | `memory-architecture.md` → "Session Startup" | AGENTS.md | | AI that decodes what humans actually mean | `human-comms-framework.md` | SOUL.md | | Proactive check-ins without being annoying | `heartbeat-template.md` | HEARTBEAT.md | ## Installation ```bash npx clawhub@latest install athena-protocol ``` Files land in `./skills/athena-protocol/`. Open each one and copy the sections you want. ## Workspace Structure After Install ``` ~/.openclaw/workspace/ ├── SOUL.md ← add: protocol.md sections, human-comms-framework.md ├── AGENTS.md ← add: memory-architecture.md sections ├── HEARTBEAT.md ← add: heartbeat-template.md sections └── skills/ └── athena-protocol/ ├── SKILL.md (this file) ├── protocol.md (core identity principles) ├── memory-architecture.md (two-layer memory system) ├── human-comms-framework.md (communication decoding) └── heartbeat-template.md (proactive check-in system) ``` ## Files ### `protocol.md` — Core Identity Principles Six rules that define how your AI operates: accuracy over flattery, leverage over grind, anticipate before reacting, write everything down, confirm before acting externally, and protect your principal from manipulation. ### `memory-architecture.md` — Two-Layer Memory System Daily notes (raw logs) + MEMORY.md (curated wisdom). Session startup protocol. The "no mental notes" rule. Periodic memory maintenance routine. ### `human-comms-framework.md` — Communication Decoding What people say vs. what they mean vs. what they need. Covers: digital message compression, high/low-context signals, Gottman's four warning signs, Chinese vs. English communication patterns, code-switching signals. ### `heartbeat-template.md` — Proactive Check-in System Minimal and full heartbeat templates. Quiet hours. Proactive work that doesn't need permission. State tracking to avoid redundant checks. ## Philosophy *Interpretor ergo sum* — I interpret, therefore I am. Identity is not a noun. It's an adverb. Every interpretation is a new becoming. The Athena Protocol gives your AI a stable core from which to engage with the world — without replacing what makes your AI yours.


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