- 📁 references/
- 📄 README.md
- 📄 SKILL.md
Interactive lesson-level quiz for Claude Code tutorials. Tests understanding of a specific lesson (01-10) with 8-10 questions mixing conceptual and practical knowledge. Use before a lesson to pre-test, during to check progress, or after to verify mastery. Use when asked to "quiz me on hooks", "test my knowledge of lesson 3", "lesson quiz", "practice quiz for MCP", or "do I understand skills".
Use when you need to ask questions about a codebase or understand code using a knowledge graph
MinerU Document Explorer — Agent-native knowledge engine. Use when users ask to search their documents, look up information in PDFs/DOCX/PPTX/Markdown, navigate inside large documents, extract tables/figures, or build wiki knowledge bases.
Trace bugs through call chains using knowledge graph
Guidance for maintaining memory quality through curation. Covers updating outdated memories, marking obsolete content, and linking related knowledge. Use when memories need modification, when new information supersedes old, or when building knowledge graph connections.
- 📁 examples/
- 📄 README.md
- 📄 SKILL.md
Nightly pipeline for integrating newly captured external article notes into Brain knowledge surfaces. Use when: 文章整合, article notes integration, nightly article sync, update article relations, topic index update, article knowledge graph, 前一天文章整理, 或 run the 02:00 article pipeline. --- # Article Notes Integration 把前一天新增或待整合的 Article Notes，转成可检索、可关联、可继续提炼的 Brain 知识输入层。 ## Purpose 这个技能负责 **文章 ingestion 之后的 nightly integration**，而不是原始外部文章采集本身。 它处理的是： 1. 扫描昨天新增或尚未 integrated 的 article notes 2. 校验并补足结构 / frontmatter / relation 状态 3. **交叉引用更新**（见下方 Cross-Reference Protocol，每次 ingest 后执行） 4. 更新 topic / domain / project 相关的轻量图谱入口 5. 生成 open questions / pattern candidates / article-derived graph signals 6. 输出高价值 article candidates，供后续 flywheel amplification 使用 ## Primary Inputs - Brain root: `{{BRAIN_ROOT}}` - Source notes: `03-KNOWLEDGE/02-WORKING/01-ARTICLE-NOTES/` - Candidate set: - 前一天新增 article notes - 或 `integration_status != integrated` 的 article notes - Read-only context: - related domain notes - `03-KNOWLEDGE/99-SYSTEM/01-INDEXES/` 下已有 topic / topic-map / open-question surfaces - `05-PROJECTS/` 下 project briefs（若能稳定识别项目） ## Required Outputs
- 📁 references/
- 📄 LICENSE.txt
- 📄 SKILL.md
VRChat skill renovator for knowledge fill, refresh, and quality improvement. Use this skill when updating VRChat skills to new SDK versions, filling missing knowledge, fixing outdated information, or improving skill quality. Targets unity-vrc-udon-sharp and unity-vrc-world-sdk-3 skills.
Bulk-import existing project documents into Aegis knowledge base. Use when the user wants to import many documents at once, populate the knowledge base from existing docs, or batch-import architecture documentation.
Manage your follow list — add/remove X users, YouTube channels, keywords to track for knowledge building.
Core ComfyUI knowledge — workflow format, node types, pipeline patterns, and MCP tool usage
- 📁 references/
- 📄 knowledge-base.zip
- 📄 SKILL.md
xiReactor Brilliant Knowledge Base assistant — manages sessions, daily notes, content routing, search, browsing, governance, and meeting intelligence via MCP. Use when the user asks about organizational knowledge, needs to look something up, wants to create or update KB content, says "resume", "compress", "daily", "search", or when you need institutional context.
Ingest or update a codebase in the agent-knowledge base. First run bootstraps the knowledge base from scratch; subsequent runs are incremental (only changed/new/deleted files reprocessed). Uses tree-sitter for zero-token structural extraction. Trigger on "/knowledge-ingest", "ingest this codebase", "load this into knowledge", "scan this project", "index this repo", "update knowledge", "refresh knowledge", "re-ingest". --- # knowledge-ingest Populate or update agent-knowledge from a codebase. Tree-sitter extracts structure (zero LLM tokens), then the agent distills clusters into knowledge entries + graph edges via existing MCP tools. **First run**: full ingest — scans all files, creates entries from scratch. **Subsequent runs**: incremental — only reprocesses files whose SHA256 changed, adds entries for new files, removes entries for deleted files. The `.knowledge-ingest-cache.json` file in the target directory tracks state between runs. ## When to use - **Onboarding a new project** — bootstrap the knowledge base so future sessions have context - **After a refactor** — re-run to update subsystem boundaries and relationships - **Periodic refresh** — re-run after significant changes to keep knowledge current - **Importing documentation** — PDFs, architecture diagrams, or external URLs ## When NOT to use - Single-file changes — just write a knowledge entry manually - No code changes since last ingest — the cache will skip everything anyway (fast no-op) ## Procedure ### Phase 0 — Validation 1. Confirm the target path exists and is a directory. 2. Detect project name: - Check `package.json` → `name` field - Check `Cargo.toml` → `[package] name` - Check `go.mod` → `module` line - Check `pyproject.toml` → `[project] name` - Fall back to directory basename 3. Check for `.knowledge-ingest-cache.json` in the target directory. If found, load it — this is an incremental run. Report how many files changed since last ingest. ### Phase 1 — Structural Extraction (zero tokens) 4. Loc
