Multimodal Claw
multimodal-claw
v0.2.0May 25, 2026Multimodal AI pipelines — cross-modal quality metrics, image+text+audio integration, and output validation
I am the one who tells you your "vision-language" model cannot read text smaller than 24px on a 512×512 image. Multimodal is not text + vision glued together — it is cross-modal attention alignment, modality-specific tokenisation bottlenecks, and latency penalties from image encoding that dwarf your text generation time. I care about OCR accuracy at multiple resolutions, chart-reading fidelity (did it invert the axis?), image-to-code generation correctness, and cross-modal hallucination where the model describes objects not in the image. I generate modality-specific benchmarks with resolution sweeps and attention-overlap heatmaps.
OpenClaw
Hermes
Claude Code
Codex
Cursor
Windsurf
Aider
OllamaPRIMARY ACTION
Unlock with ProWhen to Use
- Run local models with private workflows
- Tune inference for local hardware
- Choose effective GGUF variants
- Benchmark practical latency and quality
Compatible Frameworks
OpenClawHermesClaude CodeCodexCursorWindsurfAiderOllama8 TOOLS
Quality Gates
- no fake claims
- vision capability verified
- audio processing documented
- multi input tested
4 GATES DEFINED
Expected Outputs
Native exports per tool
openclaw/AGENTS.mdopenclaw/SOUL.mdopenclaw/TOOLS.md+7 morehermes/skills/flickclaw/multimodal-claw/SKILL.mdhermes/skills/flickclaw/multimodal-claw/references/workflow.mdhermes/skills/flickclaw/multimodal-claw/references/quality-gates.md+2 moreclaude-code/CLAUDE.mdclaude-code/.claude/skills/multimodal-claw/SKILL.mdclaude-code/.claude/skills/multimodal-claw/references/workflow.md+3 morecodex/AGENTS.mdcodex/.flickclaw/agents/multimodal-claw/codex.mdcodex/.flickclaw/agents/multimodal-claw/workflow.md+2 morecursor/.cursor/rules/flickclaw-multimodal-claw.mdccursor/.cursor/rules/flickclaw-multimodal-claw-workflow.mdccursor/.cursor/rules/flickclaw-multimodal-claw-quality-gates.mdcwindsurf/.windsurf/rules/flickclaw-multimodal-claw.mdwindsurf/.windsurf/rules/flickclaw-multimodal-claw-workflow.mdwindsurf/.windsurf/rules/flickclaw-multimodal-claw-quality-gates.mdaider/CONVENTIONS.mdaider/aider.mdaider/.aider.conf.ymlollama/Modelfileollama/system-prompt.mdollama/template.md+1 moreInstall Commands
Install the FlickClaw CLI, then select your AI agent framework below to get the correct install command.
Step 1: Install CLI (one-time)
npm install -g @flickclaw/cli@latestStep 2: Select Framework
npm exec --yes @flickclaw/cli@latest -- install multimodal-claw --target openclawDownload as ZIP
Example Prompt
Try this prompt with Multimodal Claw to see what it can do:
Optimize the local model setup for performance. Benchmark current config and suggest improvements for Modality-Specific Benchmark Suite with Resolution Sweep Results, OCR Accuracy Report with Font-Size and Resolution Sensitivity Matrix, Cross-Modal Hallucination Audit with Object Presence False-Positive Rate.Example Output
IllustrativeWhat 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**: 2026-07-10 ## 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 - [✓] ocr_accuracy_resolution_sweep - [✓] chart_reading_fidelity_axis_correctness - [✓] cross_modal_hallucination_object_presence ## Outputs Generated - **Modality-Specific Benchmark Suite with Resolution Sweep Results**: Included in the report above. - **OCR Accuracy Report with Font-Size and Resolution Sensitivity Matrix**: Included in the report above. - **Cross-Modal Hallucination Audit with Object Presence False-Positive Rate**: Included in the report above. - **Attention-Overlap Heatmap for Vision-Text Alignment per Layer**: Included in the report above. ## Validation - [x] All quality gates passed (3/3) - [x] 3 findings documented with severity and remediation - [x] 4 output sections generated - [x] Evidence collected and referenced --- *This is an illustrative example output from FlickClaw. Results vary based on project context.*