zhaixin244-wq
zhaixin244-wq/fnw
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/zhaixin244-wq/fnw --skill rag-project-remember命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Autonomous iterative research loop. Takes a topic, runs web searches, fetches sources, synthesizes findings, and files everything into the wiki as structured pages. Based on Karpathy's autoresearch pattern: program.md configures objectives and constraints, the loop runs until depth is reached, output goes directly into the knowledge base. Triggers on: "/autoresearch", "autoresearch", "research [topic]", "deep dive into [topic]", "investigate [topic]", "find everything about [topic]", "research and file", "go research", "build a wiki on".
Visual layer of the wiki. Add images, text cards, PDFs, and wiki pages to Obsidian canvas files with auto-positioning inside zones. Integrates with /banana for image capture. Triggers on: /canvas, canvas new, canvas add image, canvas add text, canvas add pdf, canvas add note, canvas zone, canvas list, canvas from banana, add to canvas, put this on the canvas, open canvas, create canvas.
Use when running RTL lint checks. Triggers on 'lint', 'verilator', 'verible', 'spyglass', '语法检查', '可综合性', '组合环路', 'latch检查', 'rtl lint', 'lint检查'. Supports multiple EDA tools with auto-detection.
基于 SOC 职业分类
正在显示 SKILL.md
| name | rag-project-remember |
| description | Store project-specific knowledge into the project-level LightRAG knowledge graph |
| version | 1.0.0 |
| model | sonnet |
| triggers | ["/rag-project-remember","remember this for the project","save project context"] |
Store project-specific knowledge — architecture decisions, conventions, dependencies, and requirements — into the project-level LightRAG graph.
Format each entry as:
[TYPE] Title — YYYY-MM-DD
What: Brief description
Why: The reasoning or constraint behind it
Files: Relevant file paths (if applicable)
| Type | Use for |
|---|---|
ARCHITECTURE | System design choices, component structure, data flow |
CONVENTION | Coding standards, naming patterns, file organization rules |
DECISION | Technology choices, library selections, approach decisions |
DEPENDENCY | External services, APIs, packages, version constraints |
CONFIG | Environment setup, build config, deployment settings |
REQUIREMENT | Business rules, constraints, acceptance criteria |
INSIGHT | Performance findings, gotchas, non-obvious behaviors |
node -e "
const BASE = 'http://YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORT';
const API_KEY = process.env.LIGHTRAG_API_KEY || 'YOUR_API_KEY';
(async () => {
const res = await fetch(BASE + '/documents/text', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': API_KEY
},
body: JSON.stringify({
file_source: '[TYPE] Title — YYYY-MM-DD',
text: 'Full content here with What/Why/Files structure'
})
});
console.log('Status:', res.status);
})();
"
Use /rag-remember (personal graph) instead for:
This skill is for things specific to the current project that wouldn't be relevant elsewhere.
YOUR_LIGHTRAG_HOST:YOUR_PROJECT_PORTLIGHTRAG_API_KEY