Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mxslr/mlcraft/domain-multimodalnpx skills add mxslr/mlcraft --skill domain-multimodalgit clone --depth 1 https://github.com/mxslr/mlcraftWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mxslr/mlcraft/domain-multimodal)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-multimodal"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-multimodal.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00115 | $0.00453 |
| Opus 5 | $0.00057 | $0.00227 |
| Sonnet 5 | $0.00023 | $0.00091 |
| Haiku 4.5 | $0.00012 | $0.00045 |
Grade A, and why
domain-multimodal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Multimodal (Vision-Language) - Method Selection
Try zero-shot first. Pretrained models often work with no training.
Decision table
| Task | Recommended | Notes |
|---|---|---|
| Zero-shot classification or image-text retrieval | CLIP or SigLIP (contrastive dual-encoder) | embeddings plus cosine similarity; build an approximate nearest neighbor index for retrieval. |
| VQA, captioning, visual chat | BLIP-2 (Q-Former plus a frozen LLM) or a LLaVA-family model | instruction-tuned; adapt parameter-efficiently. |
| Fine detail, documents, or charts | a high-resolution VLM or a document-specialized model | small text and layout need high input resolution. |
| Custom domain adaptation | LoRA on the VLM, or fine-tune CLIP with a contrastive loss | small data. |
Cross-cutting practice
- Prompt design matters for VLMs. Start zero-shot and only fine-tune if it is insufficient.
- Metrics: retrieval uses Recall@K; zero-shot classification uses accuracy; VQA uses VQA accuracy (open-ended VQA needs an LLM judge plus human checks); captioning uses CIDEr, SPICE, or BLEU plus human review.
- Caveats: VLMs hallucinate, so evaluate faithfulness. Deduplicate to avoid train and test image overlap.
- Explainability: cross-attention or grounding maps (which region the answer used), and retrieved neighbors for retrieval.
- Improve results: use
accuracy-improvement-loop; evaluate withrigorous-evaluation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 24 lines · 115 tokens per session scan A 7d4c83b1c428
domain-multimodal is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 453 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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