What makes RAG Pipeline Starter notable?

981 downloads. Trust evidence available

How should RAG Pipeline Starter be evaluated before use?

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What kind of evidence is visible on this page?

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RAG Pipeline Starter

Set up and optimize RAG pipelines for large datasets (50K-500K rows) with document chunking, embedding benchmarking, vector indexing, and retrieval tuning. Skill: RAG Pipeline Starter Owner: abhinas90 Summary: Set up and optimize RAG pipelines for large datasets (50K-500K rows) with document chunking, embedding benchmarking, vector indexing, and retrieval tuning. Tags: latest:1.0.1 Version history: v1.0.1 | 2026-05-31T18:10:30.016Z | user Release 1.0.1: RAG Pipeline Starter v1.0.0 | 2026-04-18T14:02:27.797Z | user Initial release Archive index: Archive v1.0.1: 8 files,

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

981

Updated

Oct 11, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 981 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 981 downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.1release · observed May 31, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.1release
Release 1.0.1: RAG Pipeline Starter
Observed May 31, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s174azs5nksq9sp64tzargsaph84203h:rag-pipeline-starter

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-abhinas90-rag-pipeline-starter/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

17,213 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

# RAG Pipeline Starter

Production-grade RAG pipeline setup with chunking strategies, embedding benchmarks, and retrieval tuning for 50K-500K row datasets.

## Overview

This skill provides a complete toolkit for building and optimizing RAG (Retrieval-Augmented Generation) pipelines. It analyzes your data, recommends optimal chunking strategies, benchmarks embedding models, and helps tune retrieval parameters for maximum accuracy.

## When to Use

- Building a new RAG system from scratch
- Optimizing an existing RAG pipeline's retrieval quality
- Choosing the right embedding model for your domain
- Processing large document collections (50K-500K rows)
- Need to balance speed vs. accuracy for your use case

## Scripts

### chunking_analyzer.py
Analyzes documents and recommends optimal chunking strategies based on content structure.

**Usage:**
```bash
# Assess data and get strategy recommendation
python chunking_analyzer.py --assess ./data

# Apply chunking strategy to documents
python chunking_analyzer.py --strategy recursive --input ./data/doc.txt --output ./chunks/ --chunk-size 500 --overlap 50
```

**Options:**
- `--assess <dir>` - Analyze documents and recommend strategy
- `--strategy <name>` - Chunking strategy: fixed, semantic, recursive, hierarchical
- `--input <path>` - Input file or directory
- `--output <dir>` - Output directory for chunks
- `--chunk-size <int>` - Chunk size (default: 500)
- `--overlap <int>` - Overlap between chunks (default: 50)

### embedding_benchmark.py
Tests multiple embedding models on your data to find the best fit for your domain.

**Usage:**
```bash
python embedding_benchmark.py --data ./chunks/ --domain finance --output results.json
```

**Options:**
- `--embeddings <models>` - Embedding models to test (space-separated)
- `--data <dir>` - Directory with chunked text files (required)
- `--domain <name>` - Domain name for context-specific recommendations
- `--output <file>` - Output file for results (JSON)

**Supported Embeddings:**
- sentence-transformers/all-MiniLM-L6-v2 (384 dims, fast, free)
- sentence-transformers/all-mpnet-base-v2 (768 dims, medium, free)
- openai/text-embedding-ada-002 (1536 dims, fast, paid)
- cohere/embed-english-v3.0 (1024 dims, fast, paid)
- bm25 (sparse, fast, free)

### retrieval_tuner.py
Optimizes retrieval parameters (top-k, similarity threshold) for your specific use case.

**Usage:**
```bash
python retrieval_tuner.py --index ./vector_store/ --queries ./test_queries.json --output tuning_results.json
```

**Options:**
- `--index <dir>` - Vector store index directory
- `--queries <file>` - JSON file with test queries and expected results
- `--output <file>` - Output file for tuning results
- `--top-k-range <min> <max>` - Range of top-k values to test (default: 1 20)
- `--threshold-range <min> <max> <step>` - Similarity threshold range

### vector_store_manager.py
Manages vector store operations: create, update, search, and maintain indexes.

**Usage:**
```bash
# Create index from

_meta.json

{
  "ownerId": "kn71hf14zhnh60jg0jb31zthnh8421nq",
  "slug": "rag-pipeline-starter",
  "version": "1.0.1",
  "publishedAt": 1780251030016
}

skill-card.md

## Description:

Set up and optimize RAG pipelines for large datasets with document chunking, embedding benchmarking, vector indexing, and retrieval tuning.

This skill is ready for commercial/non-commercial use.

## Publisher:

[abhinas90](https://clawhub.ai/user/abhinas90)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and engineers use this skill to create or improve local RAG pipelines for large document collections, including chunking, embedding selection, vector index management, and retrieval parameter tuning.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill reads chosen document folders and writes derived chunks, results, and index files.

Mitigation: Run it in dedicated working directories and avoid pointing outputs at important folders.

Risk: Using hosted embedding APIs in future extensions could send document chunks or queries to external providers.

Mitigation: Treat hosted embedding use as a separate privacy decision and prefer local models for sensitive data.

Risk: Retrieval and benchmark recommendations may be affected by simplified scoring paths and data-handling caveats.

Mitigation: Validate recommendations with representative queries and reviewed evaluation data before relying on them in production.

## Reference(s):

- [RAG Pipeline Starter ClawHub page](https://clawhub.ai/abhinas90/skills/rag-pipeline-starter)
- [Publisher profile](https://clawhub.ai/user/abhinas90)
- [Skill documentation](artifact/SKILL.md)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with Python command examples, console text, JSON results, and generated local files]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May read user-selected document folders and write derived chunks, benchmark results, tuning results, and vector index files.]

## Skill Version(s):

1.0.1 (source: server release evidence and skill.json)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

skill.json

{
  "slug": "rag-pipeline-starter",
  "name": "RAG Pipeline Starter",
  "version": "1.0.1",
  "description": "Production-grade RAG pipeline setup with chunking strategies, embedding benchmarks, and retrieval tuning for 50K-500K row datasets",
  "author": "Abhinas Neupane",
  "tags": [
    "RAG",
    "retrieval",
    "embeddings",
    "LLM",
    "vector-database",
    "chunking",
    "benchmark"
  ],
  "pricing": {
    "type": "one-time",
    "value": 0
  },
  "scripts": [
    "chunking_analyzer.py",
    "embedding_benchmark.py",
    "retrieval_tuner.py",
    "vector_store_manager.py"
  ],
  "requirements": {
    "python": ">=3.8",
    "dependencies": []
  }
}

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