Borrowing it
Nothing to install: this file belongs to williamzujkowski/standards. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/williamzujkowski/standards/master/.claude/commands/training/model-update.mdgit clone --depth 1 https://github.com/williamzujkowski/standardsWrote 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/commands/williamzujkowski/standards/model-update)<a href="https://agentmods.dev/commands/williamzujkowski/standards/model-update"><img src="https://agentmods.dev/badge/commands/williamzujkowski/standards/model-update/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/williamzujkowski/standards/model-update"><img src="https://agentmods.dev/badge/commands/williamzujkowski/standards/model-update.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.00125 |
| Opus 5.5 | $0.00000 | $0.00050 |
| Sonnet 5.5 | $0.00000 | $0.00025 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
model-update 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 yesterday.
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.
This is a copy
100% identical to model-update — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
model-update
Update neural models with new data.
Usage
npx claude-flow training model-update [options]
Options
--model <name>- Model to update--incremental- Incremental update--validate- Validate after update
Examples
# Update all models
npx claude-flow training model-update
# Specific model
npx claude-flow training model-update --model agent-selector
# Incremental with validation
npx claude-flow training model-update --incremental --validate
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.
- yesterday First seen · 29 lines · 0 tokens per session scan A d143d8b03192
model-update is a command published in the GitHub repository williamzujkowski/standards (18 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 125 tokens. A static security scan graded it A with 0 findings. It is 100% identical to model-update, differing in 3 lines, and is treated as a copy.
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audit-prompt
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dare-llm-integration
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vlm-ocr-evaluation
Run the vlm-ocr skill in its evaluate phase: compare candidate OCR systems against a stratified human-transcribed ground-truth sample and pick a model on measured CER/WER before committing to a bulk run.