catalystbyzoho/agent-skills

catalyst-zia

Catalyst Zia Services — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, and Content Moderation via pre-trained AI APIs.

Voir la source

Mode d’emploi

  1. Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
  2. Consultez le document original pour les outils, identifiants et environnements requis.
  3. Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/catalystbyzoho/agent-skills --skill catalyst-zia

Avant de commencer

  • L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
  • Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

How It Works

  1. Identify the capability — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, Content Moderation (Zia Services). For custom-trained models, AutoML, ML pipelines, or Generative AI (LLM Serving, RAG), STOP and load the `catalyst-quickml` skill instead.
  2. Load `references/zia-services.md` — for all Zia API calls with Node.js and Python examples.
  3. Show both SDK examples — Zia reference includes Node.js and Python; provide both or ask the user which platform they're using.

Triggers

Use this skill for: "Zia", "OCR", "face detection", "text analytics", "object detection", "barcode reader", "content moderation", "sentiment analysis", "image recognition", "Zia Services", or "Zia API".

Route to `catalyst-quickml` for: "QuickML", "AutoML", "ML model", "train a model on Catalyst", "ML pipeline", "LLM Serving", "RAG", "predict" (with a custom model).

References

ReferenceLoad when the query is about…
references/zia-services.mdAll Zia APIs — Text Analytics, OCR, Face Analytics, Object Detection, Barcode Reader, Moderation — SDK examples in Node.js and Python
../catalyst-quickml/SKILL.mdAutoML, ML pipelines, model training/prediction endpoints, LLM Serving, RAG, Knowledge Base

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