Agent Skills

catalyst-zia

mlcatalystbyzoho

Catalyst Zia Services and QuickML — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, Content Moderation, and AutoML predictions. Trigger on 'Zia', 'QuickML', 'OCR', 'face detection', 'text analytics', 'AutoML', 'ML model', or 'train a model on Catalyst'. DC restrictions: Identity Scanner Document Processing is IN DC only (API included); Facial Comparison works via API from any DC (console testing is IN DC only); AutoML/QuickML is not available in JP, SA, CA data centers.

Install

npx skills add https://github.com/catalystbyzoho/agent-skills --skill catalyst-zia
SKILL.md

How It Works

  1. Identify the capability — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, Content Moderation (Zia Services), or AutoML/predictions (QuickML).
  2. Load references/zia-services.md — for all Zia API calls with Node.js and Python examples.
  3. Load references/quickml.md — for AutoML workflow (dataset upload → training → prediction) and pricing.
  4. 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", "QuickML", "OCR", "face detection", "text analytics", "object detection", "barcode reader", "content moderation", "AutoML", "ML model", "predict", "Zia Services", "image recognition", "sentiment analysis", "train a model on Catalyst", or "Zia API".

References

Reference Load when the query is about…
references/zia-services.md All Zia APIs — Text Analytics, OCR, Face Analytics, Object Detection, Barcode Reader, Moderation — SDK examples in Node.js and Python
references/quickml.md AutoML workflow, problem types (classification/regression), dataset upload, SDK prediction calls, pricing

Related skills

repo-intake-and-planlllllllama450KRigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.minimal-run-and-auditlllllllama450KRigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.ai-research-reproductionlllllllama311KRigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and wexplore-codelllllllama311KRigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted bas

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers