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Search Config

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alsk1992
search-config

Search indexing configuration and full-text search management

Overview

Publisheralsk1992
RepositoryCloddsBot
Skill namesearch-config
Stars
3K
Forks
353
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by alsk1992 on GitHub. Read the source before you install it.

Installation

Install the Search Config AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alsk1992/CloddsBot/tree/main/src/skills/bundled/search-config
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alsk1992/CloddsBot.git /tmp/CloddsBot
mkdir -p .claude/skills
cp -r /tmp/CloddsBot/src/skills/bundled/search-config .claude/skills/search-config
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Search Config in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Search Config on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Search Config is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Search Config - Complete API Reference

Configure search indexing, manage search backends, and optimize full-text search.


Chat Commands

View Status

/search-config                              Show search config
/search-config status                       Index status
/search-config stats                        Search statistics

Index Management

/search-config rebuild                      Rebuild all indexes
/search-config rebuild memories             Rebuild specific index
/search-config optimize                     Optimize indexes
/search-config clear <index>                Clear index

Configuration

/search-config backend sqlite               Set backend
/search-config backend elasticsearch        Use Elasticsearch
/search-config mode hybrid                  Set search mode
/search-config boost semantic 0.7           Set semantic weight

TypeScript API Reference

Create Search Service

typescript
import { createSearchService } from 'clodds/search';

const search = createSearchService({
  // Backend
  backend: 'sqlite',  // 'sqlite' | 'elasticsearch' | 'typesense' | 'meilisearch'

  // Search mode
  mode: 'hybrid',  // 'fulltext' | 'semantic' | 'hybrid'

  // Hybrid weights
  semanticWeight: 0.6,
  fulltextWeight: 0.4,

  // Embedding provider (for semantic)
  embeddings: {
    provider: 'openai',
    model: 'text-embedding-3-small',
  },

  // Storage
  dbPath: './search.db',
});

Index Documents

typescript
// Index single document
await search.index({
  collection: 'memories',
  id: 'mem-1',
  content: 'User prefers conservative trading',
  metadata: {
    type: 'preference',
    userId: 'user-123',
  },
});

// Index batch
await search.indexBatch({
  collection: 'documents',
  documents: [
    { id: 'doc-1', content: 'First document', metadata: {} },
    { id: 'doc-2', content: 'Second document', metadata: {} },
  ],
});

Search

typescript
// Full-text search
const results = await search.search({
  query: 'trading strategies',
  collection: 'documents',
  limit: 10,
});

for (const result of results) {
  console.log(`${result.id}: ${result.score}`);
  console.log(`  ${result.snippet}`);
}

// With filters
const results = await search.search({
  query: 'bitcoin',
  collection: 'news',
  filters: {
    date: { gte: '2024-01-01' },
    source: 'reuters',
  },
  limit: 20,
});

Hybrid Search

typescript
// Combine full-text and semantic
const results = await search.hybridSearch({
  query: 'how to manage risk in trading',
  collection: 'documents',
  semanticWeight: 0.7,
  fulltextWeight: 0.3,
  limit: 10,
});

Get Index Stats

typescript
const stats = await search.getStats();

console.log('Index Statistics:');
for (const [collection, info] of Object.entries(stats.collections)) {
  console.log(`${collection}:`);
  console.log(`  Documents: ${info.documentCount}`);
  console.log(`  Size: ${info.sizeMB} MB`);
  console.log(`  Last indexed: ${info.lastIndexed}`);
}

console.log(`\nSearch Stats:`);
console.log(`  Queries today: ${stats.queriesToday}`);
console.log(`  Avg latency: ${stats.avgLatencyMs}ms`);
console.log(`  Cache hit rate: ${stats.cacheHitRate}%`);

Rebuild Index

typescript
// Rebuild all indexes
await search.rebuildAll();

// Rebuild specific collection
await search.rebuild('memories');

// With progress callback
await search.rebuild('documents', {
  onProgress: (progress) => {
    console.log(`${progress.current}/${progress.total} (${progress.percent}%)`);
  },
});

Optimize Index

typescript
// Optimize for better performance
await search.optimize();

// Optimize specific collection
await search.optimize('documents');

Clear Index

typescript
// Clear specific collection
await search.clear('memories');

// Clear all
await search.clearAll();

Configure Backend

typescript
// Switch to Elasticsearch
await search.setBackend('elasticsearch', {
  url: process.env.ELASTICSEARCH_URL,
  index: 'clodds',
});

// Switch to Typesense
await search.setBackend('typesense', {
  url: process.env.TYPESENSE_URL,
  apiKey: process.env.TYPESENSE_API_KEY,
});

Search Backends

BackendBest ForFeatures
SQLiteDevelopment, small dataSimple, embedded
ElasticsearchProduction, large dataScalable, powerful
TypesenseFast searchTypo tolerance
MeilisearchInstant searchEasy setup

Search Modes

ModeDescription
fulltextTraditional keyword matching
semanticVector similarity search
hybridCombined (best of both)

Hybrid Search Weights

typescript
// More emphasis on meaning
const results = await search.hybridSearch({
  query: 'risk management',
  semanticWeight: 0.8,  // 80% semantic
  fulltextWeight: 0.2,  // 20% keyword
});

// More emphasis on exact matches
const results = await search.hybridSearch({
  query: 'BTCUSDT',
  semanticWeight: 0.2,  // 20% semantic
  fulltextWeight: 0.8,  // 80% keyword
});

Best Practices

  1. Use hybrid search — Best results for most queries
  2. Rebuild periodically — Keep indexes fresh
  3. Optimize after bulk inserts — Improve performance
  4. Monitor latency — Scale if too slow
  5. Tune weights — Adjust semantic/fulltext balance

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Search Config AI skill do?

Search indexing configuration and full-text search management

Why use Search Config on TypingMind?

Because you install it once and use it with any model. Search Config 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 Search Config in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alsk1992/CloddsBot/tree/main/src/skills/bundled/search-config. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Search Config?

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 Search Config?

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

Is the Search Config AI skill free?

Yes. It is published on GitHub by alsk1992 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

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.

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