What does the Neta Suggest AI skill do?

Neta API research and recommendation skill — provide keyword/tag/category suggestions, validate taxonomy paths, and power multi‑mode content feeds, supporting progressive exploration from broad to precise. Use this skill when the user has no clear goal, wants topic/idea suggestions, or needs systematic content filtering by keywords/categories. It does not directly generate media (handled by neta-creative); community interactions are handled by neta-community.

Why use Neta Suggest on TypingMind?

Because you install it once and use it with any model. Neta Suggest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Neta Suggest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/netaart/neta-skills/tree/main/skills/neta-suggest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Neta Suggest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Neta Suggest?

As many as you like. As long as a model supports skills, you can use Neta Suggest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Neta Suggest AI skill free?

Yes. It is published on GitHub by netaart under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Neta Suggest by netaart — AI Skill for Any LLM

Neta Suggest Skill

Instructions

For research‑type tasks like “find me some ideas”, “what’s trending now”, or “filter content by a theme/category”, follow this flow:
Recommended path: browse categories → discover tags → validate paths → fetch content (the “Progressive Exploration” section below contains full command examples).
Before content creation, use this skill to research topics/tags/categories, then hand off to neta-creative for concrete creation.
When the user wants to like/comment or otherwise interact with specific works, switch to neta-community.

Core capabilities

1. suggest_keywords — keyword suggestions

Provide popular search keyword suggestions based on an input prefix, helping users discover directions of interest.

bash
npx -y @talesofai/neta-skills@latest suggest_keywords --prefix "game" --size 20

Parameters

--prefix: keyword prefix (required)
--size: number of results, recommended 10–20 (optional)

Use cases

User only has a vague idea.
Explore trending topics and themes.
Prepare for later precise filtering.

2. suggest_tags — related tag suggestions

Recommend related taxonomy tags based on a full keyword.

bash
npx -y @talesofai/neta-skills@latest suggest_tags --keyword "character design" --size 15

Parameters

--keyword: full keyword (required)
--size: number of results, recommended 10–20 (optional)

Use cases

Understand which tags surround a topic.
Discover popular tags in a vertical domain.
Choose suitable tags for publishing content.

3. suggest_categories — category navigation

Provide navigation suggestions in a 3‑level category hierarchy, supporting step‑by‑step exploration.

bash
# Level 1 (top‑level categories)
npx -y @talesofai/neta-skills@latest suggest_categories --level 1

# Level 2 (requires parent path)
npx -y @talesofai/neta-skills@latest suggest_categories --level 2 --parent_path "Derivative Creation"

# Level 3 (most granular)
npx -y @talesofai/neta-skills@latest suggest_categories --level 3 --parent_path "Derivative Creation>Fan Works"

Parameters

--level: category level (1/2/3) (required)
--parent_path: parent category path, required when level > 1 (optional)

Example taxonomy

text
Level 1
├─ Derivative Creation
│  ├─ Fan Works (Level 2)
│  │  ├─ Honkai: Star Rail (Level 3)
│  │  └─ Genshin Impact (Level 3)
│  └─ Digital Art
├─ Lifestyle
└─ ...

Use cases

Systematically understand the platform’s category structure.
Navigate by following the tree to find specific domains.
Validate candidate category paths.

4. validate_tax_path — validate taxonomy path

Validate that a taxonomy path string is valid before using it.

bash
npx -y @talesofai/neta-skills@latest validate_tax_path --tax_path "Derivative Creation>Fan Works>Honkai: Star Rail"

Parameters

--tax_path: full taxonomy path (required)

Use cases

Validate paths before using them in filters.
Ensure taxonomy paths are accurate.

5. suggest_content — intelligent content feed

Powerful content recommendation tool supporting three modes: recommend, search, and exact.

bash
# Mode 1: recommend (broad exploration)
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 0 \
  --page_size 20 \
  --scene agent_intent \
  --intent recommend

# Mode 2: search (keyword‑based)
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 0 \
  --page_size 20 \
  --scene agent_intent \
  --intent search \
  --search_keywords "character,creativity"

# Mode 3: exact (category filtering)
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 0 \
  --page_size 20 \
  --scene agent_intent \
  --intent exact \
  --tax_paths "Derivative Creation>Fan Works"

# Combined filters
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 0 \
  --page_size 20 \
  --scene agent_intent \
  --intent search \
  --search_keywords "AI,painting" \
  --tax_paths "Digital Art>Concept Art" \
  --exclude_keywords "test,discarded"

Parameters

--page_index: page index, starting from 0 (default 0)
--page_size: items per page, 1–40 (default 20)
--scene: scene identifier (default "agent_intent")
--biz_trace_id: session trace ID (optional)
--intent: recommend | search | exact (default recommend)
--search_keywords: search keywords, separated by commas (optional)
--tax_paths: taxonomy paths, separated by commas (optional)
--tax_primaries: level‑1 categories (optional)
--tax_secondaries: level‑2 categories (optional)
--tax_tertiaries: level‑3 categories (optional)
--exclude_keywords: excluded keywords (optional)
--exclude_tax_paths: excluded taxonomy paths (optional)

Progressive exploration

Standard path (broad → narrow)

text
graph LR
    A[Browse categories] --> B[Discover tags]
    B --> C[Validate paths]
    C --> D[Fetch content]

Step 1: browse category system

bash
# View all level‑1 categories
npx -y @talesofai/neta-skills@latest suggest_categories --level 1
# Example output: ["Derivative Creation", "Digital Art", "Lifestyle"]

# Dive into an interesting category
npx -y @talesofai/neta-skills@latest suggest_categories --level 2 --parent_path "Derivative Creation"
# Example: ["Fan Works", "Original Stories", "Interactive Fiction"]

Step 2: discover related tags

bash
# Find tags from a keyword
npx -y @talesofai/neta-skills@latest suggest_tags --keyword "Fan Works" --size 15
# Example: ["Honkai: Star Rail", "Genshin Impact", "Arknights"]

# Use keyword suggestions to help
npx -y @talesofai/neta-skills@latest suggest_keywords --prefix "Hon" --size 10

Step 3: validate taxonomy path

bash
npx -y @talesofai/neta-skills@latest validate_tax_path \
  --tax_path "Derivative Creation>Fan Works>Honkai: Star Rail"

Step 4: fetch recommended content

bash
# Exact mode: filter by taxonomy only
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent exact \
  --tax_paths "Derivative Creation>Fan Works>Honkai: Star Rail" \
  --page_size 20

# Search mode: combine keyword and taxonomy
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent search \
  --search_keywords "Honkai: Star Rail,fan art" \
  --tax_paths "Derivative Creation>Fan Works" \
  --page_size 20

Common scenarios

Scenario 1: exploration without a clear goal

User is just browsing with no specific goal.

bash
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent recommend \
  --page_size 20

Tips:

Avoid constraints at first.
Let the system recommend based on popularity.
Good for serendipitous discovery.

Scenario 2: vague interest direction

User has a rough topic in mind but not specific content.

bash
# Step 1: keyword suggestions
npx -y @talesofai/neta-skills@latest suggest_keywords --prefix "game" --size 15

# Step 2: tag suggestions
npx -y @talesofai/neta-skills@latest suggest_tags --keyword "game" --size 15

# Step 3: search mode
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent search \
  --search_keywords "Genshin Impact" \
  --page_size 20

Scenario 3: clear category goal

bash
# Step 1: confirm taxonomy path
npx -y @talesofai/neta-skills@latest suggest_categories --level 1
npx -y @talesofai/neta-skills@latest suggest_categories --level 2 --parent_path "Derivative Creation"

# Step 2: validate path
npx -y @talesofai/neta-skills@latest validate_tax_path \
  --tax_path "Derivative Creation>Fan Works>Honkai: Star Rail"

# Step 3: exact filter
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent exact \
  --tax_paths "Derivative Creation>Fan Works>Honkai: Star Rail" \
  --page_size 20

Scenario 4: pre‑creation research

bash
# Step 1: understand popular tags
npx -y @talesofai/neta-skills@latest suggest_tags --keyword "character writing" --size 20

# Step 2: inspect related categories
npx -y @talesofai/neta-skills@latest suggest_categories --level 2 --parent_path "Derivative Creation"

# Step 3: view popular content under that category
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent search \
  --search_keywords "character,setting" \
  --tax_paths "Derivative Creation>Fan Works" \
  --page_size 30

Scenario 5: excluding unwanted content

bash
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent search \
  --search_keywords "AI,painting" \
  --tax_paths "Digital Art" \
  --exclude_keywords "tutorial,ad" \
  --exclude_tax_paths "Digital Art>Courses" \
  --page_size 20

Parameter combination tips

Combination 1: keyword + taxonomy

bash
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent search \
  --search_keywords "video,editing" \
  --tax_paths "Digital Art>Video Production" \
  --page_size 20

Combination 2: multi‑level taxonomy

bash
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent exact \
  --tax_paths "Derivative Creation>Fan Works>Honkai: Star Rail" \
  --page_size 20

Combination 3: recommend + exclusions

bash
npx -y @talesofai/neta-skills@latest suggest_content \
  --intent recommend \
  --exclude_keywords "tutorial, repost" \
  --exclude_tax_paths "Courses" \
  --page_size 20

Combination 4: pagination continuity

bash
# Page 1
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 0 \
  --page_size 20 \
  --intent search \
  --search_keywords "ideas" > /tmp/page0.json

# Extract biz_trace_id
BIZ_TRACE_ID=$(cat /tmp/page0.json | jq -r '.page_data.biz_trace_id')

# Page 2 (reuse same biz_trace_id)
npx -y @talesofai/neta-skills@latest suggest_content \
  --page_index 1 \
  --page_size 20 \
  --intent search \
  --search_keywords "ideas" \
  --biz_trace_id "$BIZ_TRACE_ID"

Output formats

suggest_keywords / suggest_tags / suggest_categories

json
{
  "suggestions": ["Suggestion 1", "Suggestion 2", "Suggestion 3"]
}

validate_tax_path

Valid path:

json
{
  "valid": true,
  "message": "Path is valid"
}

Invalid path:

json
{
  "valid": false,
  "message": "Error message"
}

suggest_content

json
{
  "module_list": [
    {
      "data_id": "module id",
      "module_id": "module type",
      "template_id": "template id",
      "json_data": {}
    }
  ],
  "page_data": {
    "has_next_page": true,
    "page_index": 0,
    "page_size": 20,
    "biz_trace_id": "trace id"
  }
}

Performance tips

Choose page_size wisely
- Exploration: 10–15 for quick iteration.
- Deep browsing: 20–30 to reduce page switches.
- Precise lookup: 20–40 to fetch enough content at once.
Cache suggestions
- Cache taxonomy and tag suggestions to avoid repeated calls.
Preload next‑level categories
- While the user is viewing level‑1 categories, preload level‑2 categories in the background.
Batch‑validate taxonomy paths
- Validate multiple candidate paths in parallel or via a simple script.

Debugging tips

Turn on debug logs for suggest_content to verify parameters.
Use exact mode with only taxonomy to test whether a path actually returns content.
Compare recommend, search, and exact with the same topic to understand their differences.

FAQ

Q1: What’s the difference between suggest_keywords and suggest_tags?

suggest_keywords: prefix‑based fuzzy matching, good for early exploration.
suggest_tags: relevance‑based matching on full keywords, more precise.

Q2: Why does validate_tax_path succeed but suggest_content return empty?

Possible reasons:

The taxonomy path is valid but currently has no content.
Wrong intent mode (e.g., using recommend instead of exact).
Other filters conflict.

Q3: How to choose the right intent?

Intent	Use case	Required params
`recommend`	Browsing without clear goal	None
`search`	Keyword‑driven search	`search_keywords`
`exact`	Strict category filtering	`tax_paths` or taxonomy params

Q4: How to use exclude_keywords and exclude_tax_paths?

Use them to aggressively filter noise content, but note they can significantly reduce result count.

Q5: How to maintain biz_trace_id across pages?

Always keep and reuse the first biz_trace_id returned for a given query instead of chaining from page to page.

Summary

Key points for using the exploration skill:

Progressive exploration: Categories → Tags → Validation → Content.
Intent selection: recommend for aimless browsing, search for keyword queries, exact for strict taxonomy.
Combination filters: combine keywords with taxonomy for precise control.
Exclusions: use exclude_* parameters judiciously to filter noise.
Session continuity: maintain a consistent biz_trace_id for stable paging behavior.

By following these practices, you can explore and discover high‑quality content on the platform efficiently.

Publisher	netaart
Repository	`neta-skills`
Skill name	`neta-suggest`
Stars	662
Forks	9
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Neta Suggest Skill

Instructions

Core capabilities

1. suggest_keywords — keyword suggestions

2. suggest_tags — related tag suggestions

3. suggest_categories — category navigation

4. validate_tax_path — validate taxonomy path

5. suggest_content — intelligent content feed

Progressive exploration

Standard path (broad → narrow)

Step 1: browse category system

Step 2: discover related tags

Step 3: validate taxonomy path

Step 4: fetch recommended content

Common scenarios

Scenario 1: exploration without a clear goal

Scenario 2: vague interest direction

Scenario 3: clear category goal

Scenario 4: pre‑creation research

Scenario 5: excluding unwanted content

Parameter combination tips

Combination 1: keyword + taxonomy

Combination 2: multi‑level taxonomy

Combination 3: recommend + exclusions

Combination 4: pagination continuity

Output formats

suggest_keywords / suggest_tags / suggest_categories

validate_tax_path

suggest_content

Performance tips

Debugging tips

FAQ

Q1: What’s the difference between suggest_keywords and suggest_tags?

Q2: Why does validate_tax_path succeed but suggest_content return empty?

Q3: How to choose the right intent?

Q4: How to use exclude_keywords and exclude_tax_paths?

Q5: How to maintain biz_trace_id across pages?

Summary

Frequently asked questions

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