القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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