A comprehensive research and recommendation engine for discovering keywords, tags, and intelligent content feeds via the Neta API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install neta-suggest
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install neta-suggest using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is neta-suggest: Neta API Research and Content Discovery Skill?
The neta-suggest skill is a specialized tool within the Openclaw Skills ecosystem designed for deep research and discovery. It enables developers and AI agents to systematically explore content by suggesting keywords, mapping taxonomy tags, and navigating complex category hierarchies. By bridging the gap between vague user intent and precise content filtering, this skill provides the necessary data layer for creating highly relevant content strategies or powering discovery-based user experiences.
Built to facilitate progressive exploration, the skill allows for a flow from broad interest to granular content selection. Whether you are building an automated curation agent or a research assistant, this Openclaw Skills component ensures that your taxonomy is valid and your recommendations are data-driven.
neta-suggest: Neta API Research and Content Discovery Skill Use Cases
- Researching trending topics and themes when user goals are loosely defined.
- Validating complex taxonomy paths to ensure accurate content categorization.
- Powering intelligent content feeds using multi-mode search and recommendation intents.
- Conducting pre-creation research to understand popular tags and vertical domain structures.
- Filtering out noise by excluding specific keywords and taxonomy paths from search results.
How neta-suggest: Neta API Research and Content Discovery Skill Works
- The user initiates a broad inquiry or explores the category system to understand the platform structure.
- Related tags and popular keywords are discovered through fuzzy matching and relevance-based suggestions.
- Taxonomy paths are validated to ensure they conform to the three-level hierarchy requirements.
- Content is fetched using one of three intents: recommend for exploration, search for keywords, or exact for strict filtering.
- Session continuity is maintained using a trace ID to provide stable pagination across exploration sessions.
neta-suggest: Neta API Research and Content Discovery Skill Setup
To begin using this skill, ensure the NETA_TOKEN environment variable is set in your environment. Install the latest version of the package using your preferred package manager:
npm i @talesofai/neta-skills@latest -g
# OR
pnpm add -g @talesofai/neta-skills@latest
Verify the installation by checking the neta-cli version:
neta-cli --version
neta-suggest: Neta API Research and Content Discovery Skill Data Schema & Taxonomy
The neta-suggest skill organizes data into several distinct structures depending on the requested research action:
| Feature | Output Format | Description |
|---|---|---|
| Suggestions | {"suggestions": ["..."]} |
A list of keyword, tag, or category strings. |
| Validation | {"valid": boolean, "message": "..."} |
Confirmation of taxonomy path accuracy. |
| Content Feed | {"module_list": [...], "page_data": {...}} |
A paginated list containing data IDs and module templates. |
| Metadata | biz_trace_id |
A unique string used to maintain session state across paginated requests. |
neta-suggest: Neta API Research and Content Discovery Skill Advanced Features
- Multi-mode intent support including recommend, search, and exact matching for flexible discovery.
- Advanced exclusion filtering using keywords and taxonomy paths to remove unwanted content.
- Intelligent three-level category navigation for systematic domain exploration.
- Session-based pagination continuity through the persistent use of trace IDs.
- High-performance exploration via batch-validation and background preloading of categories.
SKILL.md
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