Multimodal Claw

Try this prompt with Multimodal Claw to see what it can do:

What a typical Multimodal Claw report looks like:

# Multimodal Claw — Assessment Report

**Project**: ollama-deployment
**Context**: a local LLM deployment running Llama 3.1 8B on consumer GPU hardware
**Generated**: 2026-05-26

## Executive Summary

The Multimodal Claw completed its review of ollama-deployment (a local LLM deployment running Llama 3.1 8B on consumer GPU hardware).
3 findings were identified with concrete remediation steps.
All quality gates were verified before delivery.

## Findings

| # | Severity | Area | Finding | Recommended Action |
|---|----------|------|---------|-------------------|
| 1 | **P1** | Performance | Inference latency spikes to 8s under concurrency | Enable continuous batching and set max_batch=4 |
| 2 | **P2** | Memory | Model consumes 18GB VRAM, headroom insufficient | Switch to Q4_K_M quantization, target <12GB |
| 3 | **P2** | Setup | No Modelfile with system prompt defined | Create Modelfile with role, constraints, and templates |

## Quality Gates

- [✓] no_fake_claims
- [✓] vision_capability_verified
- [✓] audio_processing_documented

## Outputs Generated


## Validation

- [x] All quality gates passed (3/3)
- [x] 3 findings documented with severity and remediation
- [x] 0 output sections generated
- [x] Evidence collected and referenced

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*This is an illustrative example output from FlickClaw. Results vary based on project context.*