moorcheh

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Use this skill to interact with Moorcheh, the Universal Memory Layer for Agentic AI. Provides semantic search with ITS (Information-Theoretic Scoring), namespace management, text and vector data operations, and AI-powered answer generation (RAG). Use when building applications that need semantic search, knowledge bases, document Q&A, AI memory systems, or retrieval-augmented generation.

3installs

Sourcemoorcheh-ai/agent-skills

Added on2026-03-15

NPX Install

npx skill4agent add moorcheh-ai/agent-skills moorcheh

SKILL.md Content

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Moorcheh — Universal Memory Layer Operations

This skill provides comprehensive access to the Moorcheh platform including namespace management, data operations, semantic search with ITS scoring, and AI-powered answer generation.

Moorcheh Account

If the user does not have an account yet, direct them to the console to register and create a free account.

Create a Moorcheh account at console.moorcheh.ai.

Environment Variables

bash

export MOORCHEH_API_KEY="your-api-key-here"

For full environment setup, see Environment Requirements.

Script Index

Namespace Management

Create Namespace: Use to create a new text or vector namespace for organizing data. Text namespaces handle automatic embedding; vector namespaces require pre-computed embeddings.
List Namespaces: Use to discover what namespaces exist in the account. This should be the first step before any operation.
Delete Namespace: Use to permanently remove a namespace and all its data. This action is irreversible.

Data Operations

Upload Text Data: Use to upload text documents with metadata to a text namespace. Documents are automatically embedded and indexed for semantic search.
Upload Vectors: Use to upload pre-computed vector embeddings to a vector namespace. Best when you have your own embedding pipeline.
Delete Data: Use to remove specific documents or vectors from a namespace.
Create Example Data: Use to create sample data for demos and testing when no data is available.

Search & AI

Semantic Search: Primary search operation. Performs semantic search across one or more namespaces using ITS scoring. Supports text queries, metadata filters, keyword filters, and relevance thresholds.
Generate AI Answer: Use to generate AI-powered answers from your data (RAG). Searches relevant context and synthesizes a natural-language answer. Supports chat history, custom prompts, and structured output.

Recommendations

Always run List Namespaces first to discover available data before searching or uploading.
For text data, prefer text namespaces — Moorcheh handles embedding automatically.
Use ITS scoring thresholds (0.0–1.0) to control result quality. Higher = stricter matching.
The Generate Answer endpoint is the primary RAG capability — use it for Q&A over documents.

Output Formats

Search results include
```
id
```
,
```
score
```
,
```
label
```
(relevance category),
```
text
```
, and
```
metadata
```
.
AI answers include
```
answer
```
,
```
model
```
,
```
contextCount
```
, and optional
```
structuredData
```
.

Error Handling

```
401 Unauthorized
```
: Verify
```
MOORCHEH_API_KEY
```
is set and valid
```
404 Namespace not found
```
: Create the namespace first or check spelling (case-sensitive)
```
400 Vector dimension mismatch
```
: Ensure vectors match the namespace's configured dimension
```
429 Too Many Requests
```
: Implement exponential backoff