Agent Skills › phuc-nt/my-translator › ck:llms

ck:llms

GitHub

生成符合 llms.txt 规范的 LLM 友好型文档索引文件，支持扫描代码库或指定路径，优化 AI 上下文获取与文档可发现性。

.opencode/skills/llms/SKILL.md phuc-nt/my-translator

Trigger Scenarios

用户请求生成 llms.txt 需要为项目创建 LLM 友好的文档索引希望提升文档在 AI 助手中的可发现性

Install

npx skills add phuc-nt/my-translator --skill ck:llms -g -y

More Options

Non-standard path

npx skills add https://github.com/phuc-nt/my-translator/tree/main/.opencode/skills/llms -g -y

Use without installing

npx skills use phuc-nt/my-translator@ck:llms

指定 Agent (Claude Code)

npx skills add phuc-nt/my-translator --skill ck:llms -a claude-code -g -y

安装 repo 全部 skill

npx skills add phuc-nt/my-translator --all -g -y

预览 repo 内 skill

npx skills add phuc-nt/my-translator --list

SKILL.md

Frontmatter

{
    "name": "ck:llms",
    "metadata": {
        "author": "claudekit",
        "version": "1.0.0"
    },
    "description": "Generate llms.txt files from docs or codebase scanning. Follows llmstxt.org spec. Use for LLM-friendly site indexes, documentation summaries, AI context optimization.",
    "argument-hint": "[path|url] [--full] [--output path]"
}

llms.txt Generator

Generate llms.txt files — LLM-friendly markdown indexes of project documentation following the llmstxt.org specification.

Scope

This skill generates llms.txt and llms-full.txt files. Does NOT handle: hosting, deployment, SEO, robots.txt, sitemaps.

When to Use

Project needs LLM-friendly documentation index
Publishing docs site and want AI discoverability
Creating context files for AI assistants
User asks for "llms.txt", "LLM documentation", "AI-friendly docs"

Arguments

No args: Scan current project's ./docs directory
path: Scan specific directory or file
--full: Also generate llms-full.txt (expanded with inline content)
--output path: Custom output location (default: project root)
--url base: Base URL prefix for links (e.g., https://example.com/docs)

Workflow

1. Gather Sources

From docs directory (default):

# Scout docs directory for markdown files

Use /ck:scout to find all .md, .mdx files in target directory.

From URL: Use WebFetch to retrieve existing documentation structure.

2. Analyze & Categorize

For each discovered file:

Extract H1 title (first # heading)
Extract first paragraph as description
Categorize by section (API, Guides, Reference, etc.)
Determine priority: core docs vs optional/supplementary

3. Generate llms.txt

Run generation script:

$HOME/.opencode/skills/.venv/bin/python3 scripts/generate-llms-txt.py \
  --source <path> \
  --output <output-path> \
  --base-url <url> \
  [--full]

Or generate manually following spec in references/llms-txt-specification.md.

4. Structure Output

Follow llmstxt.org specification strictly:

# Project Name

> Brief project description with essential context.

## Section Name

- [Doc Title](url): Brief description of content
- [Another Doc](url): What this covers

## Optional

- [Less Important Doc](url): Supplementary information

5. Validate

H1 heading present (required)
Blockquote summary present (recommended)
All links valid markdown format: [title](url)
Optional section at end for skippable content
Concise descriptions, no jargon

Format Rules (llmstxt.org Spec)

Element	Rule
H1	Required. Project/site name
Blockquote	Recommended. Brief essential context
Sections	H2-delimited groups of related links
Links	`[Title](url): Optional description`
`## Optional`	Special section — skippable for short context windows
Language	Concise, clear, no unexplained jargon