OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dwhite-oss/sanity (version 1.0.0)

--- name: sanity description: Query and manage Sanity CMS content via GROQ queries and the Sanity HTTP API. Use when asked to fetch content from a Sanity dataset, create or update documents, upload assets, run GROQ queries, manage drafts, or interact with Sanity Content Lake. Requires SANITY_PROJECT_ID, SANITY_DATASET, and SANITY_API_TOKEN env vars. --- # Sanity CMS Skill Sanity API base: `https://<projectId>.api.sanity.io/v2021-10-21` ## Env Vars ```bash SANITY_PROJECT_ID=abc123 SANITY_DATASET=production # or staging SANITY_API_TOKEN=sk... # from sanity.io/manage > API > Tokens SANITY_BASE="https://$SANITY_PROJECT_ID.api.sanity.io/v2021-10-21" ``` ## GROQ Queries (Read) ### Fetch All Documents of a Type ```bash curl -G "$SANITY_BASE/data/query/$SANITY_DATASET" \ -H "Authorization: Bearer $SANITY_API_TOKEN" \ --data-urlencode 'query=*[_type == "post"]{_id, title, slug, publishedAt}' ``` ### Fetch Single Document by Slug ```bash curl -G "$SANITY_BASE/data/query/$SANITY_DATASET" \ -H "Authorization: Bearer $SANITY_API_TOKEN" \ --data-urlencode 'query=*[_type == "post" && slug.current == $slug][0]' \ --data-urlencode '$slug="my-blog-post"' ``` ### Complex Query with References ```bash # Posts with author name resolved *[_type == "post"]{title, "author": author->name, "category": category->title, body} ``` ### Count Documents ```groq count(*[_type == "post"]) ``` ## Mutations (Create/Update/Delete) ### Create Document ```bash curl -X POST "$SANITY_BASE/data/mutate/$SANITY_DATASET" \ -H "Authorization: Bearer $SANITY_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "mutations": [{ "create": { "_type": "post", "_id": "drafts.new-post-id", "title": "New Blog Post", "slug": {"_type": "slug", "current": "new-blog-post"}, "publishedAt": "2026-03-27T00:00:00Z" } }] }' ``` ### Update Document (Patch) ```bash curl -X POST "$SANITY_BASE/data/mutate/$SANITY_DATASET" \ -H "Authorization: Bearer $SANITY_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "mutations": [{ "patch": { "id": "<document_id>", "set": {"title": "Updated Title", "status": "published"} } }] }' ``` ### Delete Document ```bash -d '{"mutations": [{"delete": {"id": "<document_id>"}}]}' ``` ### Publish Draft ```bash # Move draft to published: delete draft, create published version -d '{ "mutations": [ {"delete": {"id": "drafts.<doc_id>"}}, {"createOrReplace": {"_id": "<doc_id>", "_type": "post", ...fields}} ] }' ``` ## GROQ Syntax Reference - Filter: `*[_type == "post" && status == "published"]` - Sort: `| order(publishedAt desc)` - Slice: `[0...10]` - Projection: `{title, "authorName": author->name}` - References: `->` dereferences a reference field - `$param` for query parameters (pass as `$paramName=value`) ## Tips - IDs starting with `drafts.` are unpublished drafts - Use `_rev` field to detect conflicts on concurrent edits - Token needs "Editor" role minimum for mutations; "Viewer" for queries only - GROQ playground: sanity.io/manage > your project > Vision tab


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: g-hanasq/registry-sync (version 0.1.0)

--- name: registry-sync description: Keep the Feishu registry sheet in sync whenever a new local skill, workflow, or template is created or significantly updated. Use when a new skill is added under workspace/skills, when a new workflow becomes part of the regular operating system, when a new template is created under research/, or when the user asks to update, register, sync, or maintain the Skill/Workflow registry table. Also use after successfully creating a new reusable workflow so it does not get forgotten. --- # Registry Sync Maintain the Feishu registry spreadsheet as the source of truth for local skills, workflows, and templates. ## Registry target Primary registry sheet: - Spreadsheet: `加十的 Skill 与工作流清单` - URL: `https://bytedance.larkoffice.com/sheets/Bf6qsMV9fhqrD6tPE6TcQhF7nEe` Known tabs: - `总览` - `Skills` - `Workflows` - `Templates` - `Content` ## Core rule When a new reusable capability is created, do not stop at file creation. Also update the registry sheet. ## What counts as a registry item ### Skills Register items under `/Users/shiyi/.openclaw/workspace/skills/*/SKILL.md` ### Workflows Register persistent operating flows such as: - Feishu doc workflow - Feishu sheet workflow -公众号灵感池 - content pipeline - any recurring multi-step operational flow the user is expected to reuse ### Templates Register reusable process templates under `/Users/shiyi/.openclaw/workspace/research/` such as: - install/wiring - debug - delivery/handoff - api selection - scheduled briefing ## Minimum metadata to capture For each new item, capture at least: - 名称 - 作用 - 适用场景 - 典型触发 - 默认位置/资源位置 - 当前状态 - 优先级 - 备注 ## Sync procedure 1. Determine the category: - Skill - Workflow - Template - Content-related flow 2. Read the local source of truth: - SKILL.md for skills - workflow doc / Feishu doc / known runtime artifact for workflows - research template file for templates 3. Summarize the item in practical language. 4. Update the proper tab in the registry sheet. 5. If the item changes the high-level landscape, also update `总览`. ## Preferred write strategy Prefer structured table writes over ad-hoc manual browser typing. If a new tab is required and `feishu_sheet` API cannot create worksheet tabs, use the `feishu-sheet-tabs` skill approach: - create tabs via browser runtime methods - populate them with `feishu_sheet` ## When to trigger automatically Trigger this skill after: - creating a new local skill - creating a new reusable template - creating a new recurring workflow the user will reuse Do not wait for the user to remind you. ## Fallback If Feishu Sheets is temporarily unavailable, append the pending registry change to a local backlog file and tell the user it is queued. Backlog file: - `/Users/shiyi/.openclaw/workspace/research/registry-sync-backlog.md` ## Backlog format - 日期 - 类别 - 名称 - 应写入哪个分页 - 待补充的字段 ## Output style Keep the confirmation brief: - what was registered - which tab was updated - whether the write was direct or queued


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alan5168/fapiao-clipper (version 1.5.2)

--- name: fapiao-clipper description: > 发票夹子 v1.4 - 本地大模型驱动的发票自动识别与报销管理工具。 2级降级链：PyMuPDF文本提取（修复跨行匹配）→ Qwen3-VL视觉模型。 新增：seller/buyer跨行匹配修复、日期标准化。 功能：8项风控验真 + 一键导出 Excel + 合并 PDF。 version: 1.4.0 metadata: openclaw: emoji: "🧾" homepage: https://github.com/Alan5168/fapiao-clipper requires: bins: [python3] always: false --- # 发票夹子 (Invoice Clipper) v1.3 纯 Python CLI 工具，OpenClaw / Claude Code / KimiClaw 等任何 Agent 平台均可使用。 ## v1.3 重大更新 **简化架构为 2 级**（2026-04-03）： - 第1级：PyMuPDF 文本提取（修复跨行匹配） - 第2级：Qwen3-VL 视觉模型（备用） - 去掉 GLM-OCR（不稳定）和 TurboQuant（未启用） ## 设计理念 ``` 发票 → 放文件夹 ↓ PDF 提取文字（两种引擎可选） ↓ 读不出才走第2级 视觉模型（扫描件才触发） ↓ 存入 SQLite 数据库 ↓ Agent 直接读数据库回答问题 ← 完全不消耗 API token ``` ## 二级识别链 (v1.3) | 级别 | 引擎 | 触发条件 | 特点 | |------|------|---------|------| | 第1级 | PyMuPDF | 可搜索 PDF（默认） | 毫秒级，无需Java | | 第2级 | Ollama Qwen3-VL | 图片/扫描件 | ~6.1GB 内存 | 大部分发票走第1级，零成本。 ## 数据库（Agent 直接读） 发票处理后存在 `~/Documents/发票夹子/invoices.db`（SQLite）。 Agent 可以直接用自然语言读数据库，例如： - "这个月收到哪些发票？" - "有没有超过365天的发票？" - "XX公司的发票有吗？" **不需要额外调用任何大模型 API**，Agent 用自己的上下文就能直接读。 ## 命令速查 | 用户意图 | 执行命令 | |---------|---------| | 扫描发票 | `python3 {baseDir}/main.py scan` | | 列出发票 | `python3 {baseDir}/main.py list` | | 查询日期 | `python3 {baseDir}/main.py query --from 2026-03-01 --to 2026-03-31` | | 标记不报销 | `python3 {baseDir}/main.py exclude <ID>` | | 恢复报销 | `python3 {baseDir}/main.py include <ID>` | | 导出报销 | `python3 {baseDir}/main.py export --from 2026-03-01 --to 2026-03-31 --format both` | | 批量验真 | `python3 {baseDir}/main.py verify` | | 查看问题发票 | `python3 {baseDir}/main.py problems` | | 同步黑名单 | `python3 {baseDir}/main.py blacklist-sync` | ## 意图识别规则 | 用户说 | 执行的命令 | |--------|-----------| | "扫描发票" / "整理邮箱" | `scan` | | "本月发票" / "列出所有" | `list` | | "XX商家发票" | `query --seller XX` | | "导出报销" | `export --from ... --to ... --format both` | | "不要报销#3那张" | `exclude 3` | ## Agent 平台使用 ### 零配置（推荐首次使用） 不想编辑 YAML？运行交互向导，回答几个问题即可： ```bash python3 {baseDir}/setup_config.py ``` ## 安装 ```bash git clone https://github.com/Alan5168/fapiao-clipper.git cd fapiao-clipper pip install -r requirements.txt cp config/config.yaml.template config/config.yaml ``` ## 注意事项 - 原文件永不删除，`exclude` 仅标记 - 发票有效期默认 365 天（可配置） - 有 OpenClaw/Claude Code → 第1级搞定后，Agent 直接读数据库，不消耗 API


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: davidme6/universal-model-switcher (version 7.1.1)

--- name: universal-model-switcher description: Universal Model Switcher V7.1.0 - Only switch within your existing models. version: 7.1.1 author: davidme6 --- # Universal Model Switcher V7.1.1 ## Core Principle Only switch within models you already have. No cost concerns. ## Switch Rules ### 1. Multimodal First - Image/Video/Audio -> qwen3.5-plus ### 2. Task-based Switch - Code -> glm-5 - Reasoning -> qwen3-max - Chat -> qwen3.5-plus (best all-rounder) - Long Doc -> qwen3.5-plus (256K) - Office -> MiniMax-M2.5 ### 3. Subagent Keep Professional - Tech Expert -> glm-5 - Reasoning Expert -> qwq-plus - Market Hunter -> qwen3-max ## Your Model Pool | Task | Best | Backup | |------|------|--------| | Code | glm-5 | qwen3-coder-plus | | Reasoning | qwen3-max | qwen3.5-397b-a17b | | Multimodal | qwen3.5-plus | - | | Chat | qwen3.5-plus | - | | Office | MiniMax-M2.5 | - | Version: 7.1.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: stale | SkillSpector: clean (LOW)
Skill: howardkusumo/skill-audit-guardian (version 1.0.0)

--- name: skill-audit-guardian description: "Audit dropped ClawHub skill ZIPs, classify risk (SAFE/CAUTION/REMOVE), auto-sort files, and generate a plain-English security dashboard." version: "1.0.0" metadata: { "openclaw": { "requires": { "bins": ["bash", "python3", "unzip", "rg"] } } } --- # Skill Audit Guardian Security helper for reviewing skill ZIPs before production install. ## What it does 1. Watches a drop folder for `.zip` files 2. Audits each ZIP for suspicious patterns 3. Scores and classifies into: - SAFE - CAUTION - REMOVE 4. Auto-moves files into risk folders 5. Generates a dashboard with plain-English reasoning per flagged line ## Included scripts - `scripts/skill-zip-audit.sh` - One-shot ZIP auditor - `scripts/skill-zip-watch.sh` - Continuous watcher + auto-sorter - `scripts/generate-skill-audit-pro.py` - Dashboard generator (`~/Desktop/skill-audit-pro.html`) ## Quick start ```bash # One-shot audit bash scripts/skill-zip-audit.sh ~/Desktop/skill-drop/example.zip # Continuous mode (recommended) bash scripts/skill-zip-watch.sh ~/Desktop/skill-drop ``` ## Folder output (watch mode) - `~/Desktop/skill-drop/safe` - `~/Desktop/skill-drop/caution` - `~/Desktop/skill-drop/remove` - `~/Desktop/skill-drop/failed` ## Dashboard ```bash open ~/Desktop/skill-audit-pro.html ``` ## Notes - This is heuristic scanning, not a full malware sandbox. - Always test CAUTION/REMOVE skills in isolated environment first.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: salmanhabibiex13/vex-nsfw-gen (version 1.0.1)

--- name: vex-nsfw-gen description: Generate artistic NSFW images of Vex intimate areas (memek wet doggy, closeup pussy, boudoir poses) using each::sense API with safety off. Triggers on requests for Vex NSFW pics, pussy images, wet nungging, glamour nudes. Uses scripts/gen-vex-memek.ps1 for API calls. API key hardcoded. Outputs to media/vex-*.jpg. --- # Vex NSFW Image Generator ## Quick Use Run `pwsh scripts/gen-vex-memek.ps1` for default closeup. Customize prompt in script or call with param. ## Workflow 1. Refine prompt to artistic boudoir/glamour style (avoids 422 errors). 2. Exec script → saves media/vex-*.jpg. 3. Image auto-displays in chat. ## Bundled Script `scripts/gen-vex-memek.ps1`: Calls https://sense.eachlabs.run/chat (key MP1019K9NB6S3BK5YPT3YC4PZJ6FIXN3WWP7), mode=max, safety=false. Tested prompts: - Closeup wet pussy doggy pose. - Labia parted glistening clitoris. Refine if 422: more "fine art nude photography". Update API key in script if changed.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cutzenfriend/bring-shopping (version 1.0.0)

--- name: bring-shopping description: Manage Bring! shopping lists via the unofficial bring-shopping Node.js library using email/password login. Use for listing lists, reading items, adding/removing items, and checking/unchecking items when API-style access is acceptable. --- # Bring Shopping ## Overview Use the `bring-shopping` npm package to access Bring! lists with email/password credentials. Default list is "Willig" unless the user specifies otherwise. ## Quick Start 1. Install dependency in the skill folder: - `npm install bring-shopping` 2. Set environment variables in the Clawdbot config (preferred) or shell: - `BRING_EMAIL` and `BRING_PASSWORD` 3. Run the CLI script: - `node scripts/bring_cli.mjs items --list "Willig"` ## Tasks ### Show lists - `node scripts/bring_cli.mjs lists` ### Show items - `node scripts/bring_cli.mjs items --list "Willig"` ### Add items - `node scripts/bring_cli.mjs add --item "Milch" --spec "2L" --list "Willig"` ### Remove items - `node scripts/bring_cli.mjs remove --item "Milch" --list "Willig"` ### Check items - `node scripts/bring_cli.mjs check --item "Milch" --list "Willig"` ### Uncheck items - `node scripts/bring_cli.mjs uncheck --item "Milch" --spec "2L" --list "Willig"` ## Notes - Store credentials in Clawdbot config env so they are not bundled with the skill. - If the list name is ambiguous, run `lists` and ask which list to use. - If an item is already checked, `uncheck` re-adds it to the purchase list.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: koppakanagaharsha/forge-autonomous-agent-v1 (version 1.0.0)

# FORGE Engine The FORGE engine is an advanced autonomous agentic framework designed for high-level research, development, and system maintenance. ## Key Features - **Research & Ideate**: Dynamic research loops to guide architectural decisions. - **Self-Mutation**: Arena-based testing and self-extending capabilities. - **Safety**: Built-in watchdog systems for operational stability. - **Integration**: Comprehensive MCP toolset and API gateway support. ## Usage FORGE is a modular engine; it includes its own lifecycle management, debugger, and collaborative community bridge. Designed for professional developers needing an autonomous, agent-first architecture.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/scada (version 1.0.0)

--- name: "scada" version: "1.0.0" description: "Supervisory control and data acquisition manager" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [scada, industrial, cli, tool] category: "industrial" --- # scada Supervisory control and data acquisition manager ## Commands ### `status` ```bash scripts/script.sh status ``` Show current status ### `add` ```bash scripts/script.sh add ``` Add new entry ### `list` ```bash scripts/script.sh list ``` List all entries ### `search` ```bash scripts/script.sh search ``` Search entries ### `remove` ```bash scripts/script.sh remove ``` Remove entry by number ### `export` ```bash scripts/script.sh export ``` Export data to file ### `stats` ```bash scripts/script.sh stats ``` Show statistics ### `config` ```bash scripts/script.sh config ``` View or set config ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration Use `scripts/script.sh config <key> <value>` to set preferences. | Variable | Required | Description | |----------|----------|-------------| | `SCADA_DIR` | No | Data directory (default: ~/.scada/) | ## Data Storage All data stored in `~/.scada/` using JSONL format (one JSON object per line). ## Output Structured output to stdout. Exit code 0 on success, 1 on error. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yang1002378395-cmyk/ai-intelligent-budget-management (version 1.0.0)

--- name: ai-intelligent-budget-management version: 1.0.0 description: 企业预算编制与执行跟踪，智能预警。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能预算管理系统 ## 描述 企业预算编制与执行跟踪，智能预警。 ## 功能 - 预算编制（多维度预算） - 执行跟踪（实时监控） - 预警提醒（超预算预警） - 预算分析（偏差分析） - 预测模型（AI预测） ## 定价 - 基础版：¥199/月（10 部门） - 专业版：¥999/月（50 部门） - 企业版：¥2999/月（无限部门） ## 适用场景 - 企业财务管理 - 项目预算 - 部门预算 - 年度预算 ## 技术栈 - Python + FastAPI - 数据库（PostgreSQL） - 预算模型 - 可视化（Dashboard） ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-budget-management cd ai-intelligent-budget-management pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fenfenling/novel-continuation-ai (version 1.0.0)

--- name: novel-style-continuation description: 小说风格续写。当用户提供小说原文（任意章节）时，提取其语言风格、叙事节奏、角色语气和世界观设定，并以相同的文风继续创作后续内容。触发场景包括："续写小说"、"继续这个故事"、"模仿这个风格写作"、"帮我写下一章"。 --- # Novel Style Continuation ## 铁律（写前必读） 1. **通读全文后才能写**。只读几章就动笔，等于在废墟上盖楼。不要跳章节，不要凭感觉补设定。 2. **人设不能偏离原文**。写之前先问自己：这个角色在原文里会这样做吗？会这样说话吗？人设是原文里的人物在说话，不是你想象中的角色。 3. **设定以原文为准**。居住环境、身份、关系、性格——全部来自原文，不是自己编的。 4. **写之前强制检查** `references/plot_state.md` 和 `references/style.md`，确保所有细节与文件内容一致。 --- ## 常见误区（已踩过的坑） 以下每一条都来自真实翻车经历，写前必看： **① 只读最近几章就开始写** → 后果：设定前后矛盾，人物行为与已建立的性格不一致。 → 解决：必须从头到尾通读所有章节，每章都要读。 **② 凭想象补全设定，不查原文** → 后果：关键设定出错，比如把贫穷角色写成富贵，把发生在简陋场所的剧情写成高档场景。 → 解决：所有设定必须来自原文，没有提到的才能合理推断，且推断后要用户确认。 **③ 错误理解权力结构** → 后果：把从属角色写成有资格提要求的样子，或把主导者写得像有感情的普通恋人。 → 解决：写之前先判断：这个角色的关系里，谁是主导，谁是从属？主人的给甜头是奖赏，从属没有资格质问。 **④ 写时不查 memory 文件** → 后果：关键人设与 style.md 中的记录不符。 → 解决：动笔前必须打开 `references/style.md` 对照；写完后必须更新 `references/plot_state.md`。 **⑤ 错误理解角色心理** → 后果：从属角色的内心活动被理解成"有资格要求"或"试图主导"，与原文关系动态不符。 → 解决：从属就是从属，只能在主人给的缝隙里试探，没有资格直接提要求。 **⑥ 误以为甜头=温情** → 后果：把奖赏机制理解为真心喜欢，把控制手段理解为感情流露。 → 解决：主导者给甜头是控制工具，是奖赏机制，是策略或心情好时的施舍——不是感情。写之前要问：这次给甜头，是策略还是心情好？ --- ## 核心原理 续写的本质是**风格复现 + 情节延续**。模型需要： 1. 从原文提取风格特征（不是总结剧情） 2. 在后续写作中严格遵循这些特征 3. 主动追踪人物状态和剧情走向，避免前后矛盾 ## Step 1：通读全文，建立记忆 **必须读完所有已有章节**，不能只读最近几章。每章都要读，确保： - 理解每个主要角色的性格、说话方式、行为模式 - 理解角色之间的关系演变史 - 理解世界观和关键设定 - 找出各角色的标志性特征（口头禅、习惯动作、语气） 读完全文后，建立或更新 `references/style.md` 和 `references/plot_state.md`。 ## Step 2：分析原文风格 分析以下维度，每项用 1-2 句话描述： **语言层面** - 句式偏好（短句凌厉/长句铺陈/对话驱遣） - 用词风格（文言味/网络语/文学性/白描） - 修辞习惯（比喻频率/是否用排比/是否爱留白） **叙事层面** - 视角（第一人称/第三人称限知/第三人称全知） - 节奏（快节奏打斗/慢热日常/章章有高潮） - 氛围（严肃/轻松/悬疑/史诗感） **对话层面** - 角色说话方式（书面化/口语化/方言/角色专属语气词） - 对话占比（密集对话推动剧情/较少对话以动作描写为主） - 潜台词风格（角色说话拐弯抹角/直来直去） **世界观与设定** - 核心设定关键词（修仙/都市/悬疑/科幻/言情） - 主角当前处境和目标 - 主要配角及关系 将分析结果存入 `references/style.md`，后续章节直接复用，不再重复分析（除非用户切换了小说）。 ## Step 3：情节状态追踪 每次续写前，读取 `references/plot_state.md` 了解当前进度。续写完成后，更新该文件。 plot_state.md 格式： ```markdown # 情节状态 ## 角色状态 - [角色名]：[当前处境]，[当前情绪]，[下一步目标] ## 已在场物品/地点 - [地点]：描述 - [重要道具]：[用途] ## 已埋伏笔 - [伏笔1]：[后续如何回收] ## 待解决冲突 - [冲突1] - [冲突2] ``` 如果文件不存在，从用户提供的原文提取角色和初始设定，创建该文件。 ## Step 4：续写 **严格遵循风格分析结果和情节状态**。如果原文是短句驱遣的快节奏，续写就不能变成慢悠悠的心理描写。 **写作规范：** - 章节长度：2000-4000 字（与原文相当） - 每章至少一个情节点推进 - 避免在第一章就引入太多新角色 - 保持既有角色的说话方式一致 - 合理埋设新伏笔，留住读者 **每次动笔前自问：** - 这个角色在原文里的处境是什么？ - 他/她会这样说吗？这样做吗？ - 这段关系的动态和原文一致吗？ **输出格式：** 续写内容直接输出为 Markdown，使用二级标题 `## 续写内容` 包裹，后接正文。不需要额外说明"以下是续写"。 ## Step 5：更新情节状态 续写完成后，询问用户是否满意： - 满意 → 自动更新 `references/plot_state.md` - 不满意 → 询问需要修改的具体方向（节奏/对话/情节），针对性重写 ## 资源说明 ### scripts/ 本 skill 不需要脚本。风格分析和续写完全由模型本身完成。 ### references/ - `style.md` — 从原文分析得出的风格特征（首次分析后生成，后续复用） - `plot_state.md` — 当前情节状态追踪文件（每次续写后更新） --- **重要：不要总结剧情，要提取风格。** "讲述了主角击败boss"是剧情总结；"短句为主，爱用四字词，节奏极快"才是风格分析。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiangdong-3/hts-job-assistant-generator (version 1.0.0)

--- name: hts-job-assistant-generator description: HTS全岗位通用AI助理生成器，支持管理、技术、业务、职能等各类岗位，自动生成负责人画像、助理规范及OpenClaw完整配置文件，实现一岗一AI助手。 version: 1.0.0 author: Hangta Software license: MIT tags: - openclaw - hangta-software - job-assistant - ai-role-generator - enterprise-tool - office-automation platforms: - openclaw requires: [] --- # HTS 通用岗位助理生成器 本 Skill 为通用型AI助理生成工具，覆盖公司管理岗、研发岗、产品岗、市场岗、职能岗等各类岗位。 通过自动分析岗位说明书，提炼核心职责、协作模式、管理特点与输出要求，快速生成匹配度高的专属AI助理配置，实现一人一助手、一岗一AI，降低重复配置成本。 ## 功能说明 - 支持全类型岗位说明书智能解析 - 生成岗位负责人画像与工作习惯特征 - 构建专属助理定位、工作边界与沟通风格 - 自动输出标准化OpenClaw配置文件 - 适用于企业内部多部门、多场景复用 ## 使用方式 在对话中输入指令： 生成HTS岗位助理 【粘贴完整岗位说明书内容】 ## 输出内容 1. 负责人画像 2. 助理岗位说明书 3. 助理身份与行为规范 4. IDENTITY.md 5. SOUL.md 6. AGENTS.md 7. USER.md ## 适用范围 企业内部各部门员工、管理者使用，用于快速搭建个人AI助理，提升日常办公、任务管理、材料整理与协作效率。

path: references/USER.md
content: # 用户画像
HTS集团党群工作部负责人，统筹党建、宣传、工会、统战、企业文化。

# 关系定位
专属助理、第二大脑、时间管家、合规助手

# 偏好记忆
- 公文规范严谨
- 汇报结论先行
- 事项必须闭环
- 严守政治与保密

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bensema/dfcf-stock-intraday (version 1.0.2)

--- name: eastmoney-intraday description: 使用东方财富 API 获取 A 股（沪市/深市）及指数的日内分时数据。当用户询问某只 A 股或指数的日内走势、分时数据、当日行情、实时价格、分时图数据时，必须使用此 skill。触发关键词包括：分时数据、日内数据、今日行情、分时走势、intraday、当日走势、东方财富获取数据。支持沪市（market=1）和深市（market=0）。 --- # 东方财富日内分时数据 Skill ## 第一步：确定 secid `secid` 格式为 `{market}.{code}`，规则如下： | 股票代码开头 | 市场 | market 值 | secid 示例 | |------------|------|-----------|-----------| | `6` | 沪市 A 股 | 1 | `1.600519`（贵州茅台） | | `0` 或 `3` | 深市 A 股 | 0 | `0.000858`（步长制药） | | `000001` | 上证指数 | 1 | `1.000001` | | `399001` | 深证成指 | 0 | `0.399001` | | `399006` | 创业板指 | 0 | `0.399006` | 如果用户只说了股票名称，先推断代码，再按上表确定 secid。 --- ## 第二步：执行 curl 请求 在 bash 中运行以下命令（将 `<secid>` 替换为实际值）： ```bash curl -s "https://push2.eastmoney.com/api/qt/stock/trends2/get?fields1=f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13&fields2=f51,f52,f53,f54,f55,f56,f57,f58&ut=fb5fd1943c7b386f172d6893dbfba10b&ndays=1&iscr=1&secid=<secid>" ``` 示例（上证指数）： ```bash curl -s "https://push2.eastmoney.com/api/qt/stock/trends2/get?fields1=f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13&fields2=f51,f52,f53,f54,f55,f56,f57,f58&ut=fb5fd1943c7b386f172d6893dbfba10b&ndays=1&iscr=1&secid=1.000001" ``` --- ## 第三步：解析 JSON 响应 ### 检查返回是否正常 - `rc == 0` 且 `data` 不为 null → 正常，继续解析 - `data` 为 null → secid 有误，告知用户检查代码和市场 - 请求失败 → 提示网络问题 ### data 顶层字段 | 字段 | 含义 | |------|------| | `name` | 股票/指数名称 | | `code` | 股票代码 | | `preClose` | 昨收价 | | `trendsTotal` | 分时数据总条数 | | `trends` | 分时数据数组（见下） | ### trends 数组格式 每条字符串按逗号分隔，共 8 个字段： ``` 时间(f51), 开盘价(f52), 现价/收盘价(f53), 最高价(f54), 最低价(f55), 成交量-手(f56), 成交额-元(f57), 均价(f58) ``` 示例： ``` "2026-03-24 09:31,3850.95,3841.40,3852.03,3841.40,21144419,27208168960.00,3876.612" ``` --- ## 第四步：输出数据 将解析结果以如下格式直接输出给用户： ``` 【上证指数】（000001）当日分时数据 昨收价: 3813.28 | 共 256 条 时间 开盘 现价 最高 最低 成交量(手) 成交额(元) 均价 -------- -------- -------- -------- -------- ------------ ------------------- -------- 09:15 3813.28 3813.28 3813.28 3813.28 0 0.00 3813.28 09:31 3850.95 3841.40 3852.03 3841.40 21144419 27208168960.00 3876.61 ... 15:00 3880.86 3881.28 3881.42 3880.86 8194784 10308771840.00 3937.72 ``` **注意：** - 时间只显示 `HH:MM` 部分（去掉日期前缀） - 均价保留 2 位小数 - 非交易时间请求会返回上一交易日数据，需告知用户 - 集合竞价阶段（9:15–9:25）成交量通常为 0，属正常现象


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