菜单
Use when you need Schema.org JSON-LD structured data. Pure Python — no LLM call required. Supports FAQPage, Article, Breadcrumb. Part of brand-loom; hosted brand-memory version at neoxra.com.
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。
基于 SOC 职业分类
Use when you need to create a LinkedIn post from a milestone, achievement, or topic. Produces authentic, story-driven posts with proper formatting (line breaks, hooks, CTAs).
Use when you need to translate marketing copy between EN and zh-TW while preserving brand voice, tone, and impact. Not word-level translation — a voice-preserving rewrite in the target locale.
Use when you need a social media caption for any platform. Supports brand_context for voice/tone. Model-agnostic. Part of brand-loom; hosted brand-memory version at neoxra.com.
Use when you need call-to-action copy for buttons, banners, or closing lines. Generates multiple CTA variants. Model-agnostic. Part of brand-loom; hosted brand-memory version at neoxra.com.
Use when you need FAQ items from a body of text. Generates 3-6 Q&A pairs. Model-agnostic. Part of brand-loom; hosted brand-memory version at neoxra.com.
Use when you need hashtags for social media posts. Generates deduped, count-bounded sets. Model-agnostic. Part of brand-loom; hosted brand-memory version at neoxra.com.
| name | schema-org |
| slug | schema-org |
| displayName | Schema.org Markup Generator |
| summary | FAQPage / Article / Breadcrumb JSON-LD (no LLM needed) |
| description | Use when you need Schema.org JSON-LD structured data. Pure Python — no LLM call required. Supports FAQPage, Article, Breadcrumb. Part of brand-loom; hosted brand-memory version at neoxra.com. |
| version | 0.1.0 |
| license | Apache-2.0 |
| compatibility | Claude Code, Cursor, OpenClaw, or standalone via pip install brand-loom |
| homepage | https://github.com/hogan-tech/brand-loom |
| when_to_use | schema.org markup, JSON-LD, structured data, SEO rich snippets |
| argument-hint | <faqs_json> [--schema-type faq] |
| metadata | {"author":"hogan-tech","discipline":"marketing","hosted":"https://neoxra.com"} |
Use when you need Schema.org JSON-LD structured data. Pure Python — no LLM call required. Supports FAQPage, Article, Breadcrumb.
brand-loom run schema_org --text '[{"q":"What is X?","a":"X is..."}]'brand-loom chain faq,schema_org --text "Our product helps teams..."from brand_loom.agent import run_skill
from brand_loom.providers import use_provider
use_provider("openai") # or "anthropic", "gemini", "ollama", "fake"
result = run_skill("schema_org", "your topic here")
print(result.text)
pip install brand-loom # standalone (no coding agent needed)
npx skills add hogan-tech/brand-loom # via skills.sh
Want hooks auto-matched to your brand voice, across every platform, no setup? → neoxra.com