- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Search and retrieve knowledge from agentic_kb knowledge base. Use when the user requests to search the KB, asks "How do I..." questions that should consult the KB, wants to document new knowledge, or at session start to update the KB submodule. Also use when User wants to udpate the knowledge base with new knowledge. Knowledge Capture when you learn new, reusable knowledge during tasks. Supports Typesense (fast full-text search), FAISS (semantic vector search), and ripgrep (exact pattern matching). All KB is Obsidian formatted and can be browsed easily and visually with network maps in Obsidian.
- 📁 agents/
- 📄 SKILL.md
- 📄 split-planner-prompt.md
- 📄 split-wordcount-planner-prompt.md
Use when extracted rulebook markdown needs to be split into semantic documentation files and navigation. Trigger this skill from `init-doc`, future append/add-document flows, or whenever regenerated `_pages.md` source invalidates the existing chapter map. Do not use this skill for temporary translation chunking; that belongs to a separate draft-only translation workflow.
Use NotebookLM for grounded research — documentation queries, stack comparisons, session log search.
Collect DOC: notes from session, generalize, and propose repo documentation (.claude/rules/, docs/).
- 📁 data/
- 📄 fluxui-docs
- 📄 install
- 📄 SETUP.md
Livewire Flux UI component documentation lookup. Provides offline access to fluxui.dev documentation via CLI.
Configuration templates for research MCP servers used by the knowledge vault. Internal skill used by /knowledge-vault:setup-sources and /knowledge-vault:collect.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Create and validate ASCII circuit diagrams with automatic rule checking and iterative refinement. Use when the user requests circuit diagrams in ASCII/text format, or when creating technical documentation with embedded circuit schematics. Automatically ensures diagrams follow golden rules (no line crossings without junctions, no lines crossing labels, proper component connections, correct polarity). Includes preview validation using monospace rendering.
Convert a markdown spending report to a styled, self-contained HTML document suitable for email delivery or browser viewing. Use when generating HTML versions of spending reports.
- 📁 assets/
- 📁 system/
- 📄 .gitignore
- 📄 .npmignore
- 📄 CLAUDE.md
AI-driven PDCA project management with Feishu/Lark integration. Use for: project setup (new), active project tracking (ongoing), experience retrieval (achieve), PDCA cycles, SMART goal validation, quality improvement (OEE, defects), manufacturing optimization, or structured problem-solving with Feishu Bitable + docs. Also use for project transitions, proactive AI alerts, or template-based experience reuse. --- # PDCA 项目管理系统 基于 PDCA 循环的结构化问题解决系统,由 AI 驱动实现主动巡检、SMART 目标校验和飞书工具链集成。 ## 何时使用 | 触发症状 | 不适用场景 | |---------|-----------| | 问题需要结构化分析(5W1H、鱼骨图、5Why) | 简单单次任务(直接用任务管理工具) | | 需要量化目标和可衡量指标 | 纯技术研究(无需流程闭环) | | 需要主动进度监控和预警 | 紧急故障处理(先修复,后复盘) | **触发示例**: - "启动一个 PDCA 项目来降低产品缺陷率" - "用飞书 Bitable 管理我们的质量改善项目" - "我需要 SMART 目标校验,目标是将 OEE 提升到 85%" ## 系统依赖 **必需平台**: - **OpenClaw**:AI CLI 框架(https://github.com/open-claw/open-claw) - **飞书插件**:提供 `feishu-bitable`、`feishu-create-doc` 等 API 本 skill 通过这些 API 创建 Bitable 应用、Wiki 文档、任务和日程。 **项目存储位置**:所有项目统一存储在 Wiki 知识空间「PDCA」下。 ## 核心工作流 1. **评估与启动 (new)**:评估问题是否适合立项,在 Wiki 知识空间创建项目文档 + Bitable 应用 + 项目甘特图 2. **计划与校验 (Plan)**:执行 SMART 校验与因果逻辑审查 3. **执行与巡检 (Do)**:AI 通过 Bitable 数据记录主动巡检并汇总进展 4. **检查与评估 (Check)**:分析数据偏差 5. **决策与沉淀 (Act)**:生成标准化 SOP 并归档经验 ## 全局交互规范:AskUserQuestion 选项设计 **适用范围**:PDCA **每个阶段** 中 AI 主动发起的 AskUserQuestion 对话,不限于项目启动阶段。 设计选项时遵循三大原则: ### 1. MECE 原则 — 基于框架设计选项 选项必须"相互独立、完全穷尽"。根据当前对话的问题类型,选择对应的 MECE 框架,用其维度作为选项基础: | 问题类型 → 框架 | |------| | 生产/制造 → 4M1E | | 个人健康 → TREND | | 软件/技术 → PPTD | | 销售/营销 → 5P | | 学习/教育 → COMET | | 财务/投资 → 3RL-TD | | 团队协作 → GRCT | | 客户服务 → 5S | | 个人效率 → TIME | | 流程/服务 → SIPOC | | 其他管理/组织 → 5P2E | 每个框架的逐维度详细说明见 [mece-frameworks.md](assets/references/mece-frameworks.md)。 ### 2. 多选优先 — 原因/因素往往不止一个 问题涉及"哪些方面"、"什么原因"、"什么因素"时,使用 `multiSelect: true`。 ### 3. 允许自定义 — 必须有 Other 选项 你无法预先覆盖所有情况,所有问题必须包含 "Other" 选项。 ### AskUserQuestion 模板 ```yaml
Processes Firefox bookmark exports (JSON) to organize links by category, generate summaries, and produce a visual HTML feed. Activate when the user mentions "bookmarks", "bookmark curator", "organizar bookmarks", "exportei os bookmarks", or "bookmark feed". --- # Bookmark Curator Process Firefox bookmark JSON exports into organized, categorized outputs: a structured markdown file for the training-mentor Skill and a visual HTML feed for browsing. ## Input Firefox bookmark JSON export. Default location: `~/Downloads/bookmarks-YYYY-MM-DD.json` (or ask the user for the filename). If the file is not found, ask the user to export: > Firefox > Bookmarks > Manage Bookmarks > Import and Backup > Backup > Save as JSON ## Processing Pipeline ### Step 0: Check Progress Read `references/progress.md` (in this Skill's folder). This file tracks which URLs have already been processed. If it doesn't exist, create it. Compare all bookmark URLs from the JSON against the processed list. Only process new URLs not yet in the list.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
对任意代码仓库进行合规审计并生成可取证报告(Markdown + JSON findings),覆盖“是否遵循 AGENTS.md/仓库规则/用户指令”“文档索引/规格/工作记录/任务总结”“TDD 与离线回归证据”“可复现性(.env.example 等)”“潜在密钥泄露与仓库卫生”等;并支持在**人类勾选 finding.id** 后执行选择性低风险整改(默认不改业务逻辑)。触发场景:仓库交付前自检、接手陌生仓库、需要合规审计报告、需要把整改条目做成可选择的执行清单。
文件驱动的结构化脑暴。只在用户直接触发时使用。适用场景:(1) 用户手动调用本 skill 并给出脑暴主题,(2) 用户要求对某个问题进行发散讨论和方案收敛。作为有立场的思维伙伴,通过逐个问题与用户碰撞观点,结论实时回写到脑暴文档。