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.
git clone --depth 1 https://github.com/wildcard/caroWrote 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/wildcard/caro/ml-fine-tune-loop)<a href="https://agentmods.dev/commands/wildcard/caro/ml-fine-tune-loop"><img src="https://agentmods.dev/badge/commands/wildcard/caro/ml-fine-tune-loop/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/wildcard/caro/ml-fine-tune-loop"><img src="https://agentmods.dev/badge/commands/wildcard/caro/ml-fine-tune-loop.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 | $0.01428 |
| Opus 5.5 | $0.00000 | $0.00571 |
| Sonnet 5.5 | $0.00000 | $0.00286 |
| Haiku 4.5 | $0.00000 | $0.00143 |
Grade B, and why
ml-fine-tune-loop scanned grade B with 1 finding 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 1mo 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
tail -40 .claude/memory/ml-session-log.md The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 112 lines · 0 tokens per session scan B a1eabdf0fae8
ml-fine-tune-loop is a command published in the GitHub repository wildcard/caro (40 stars, last pushed yesterday), licensed AGPL-3.0. It costs nothing until one of its globs matches a file; then it loads 1,428 tokens. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
design-plan
Generate a concrete design system (tokens, style, type, UX) before building UI.
design-review
Run the 7-phase design review (WCAG AA, responsive, interaction) on a page or URL.
check
Checks if the code is ready to be committed.
fixme
Looks for all the fixme comments in the code and attempts to fix them.
autospec.implement
Execute the implementation plan by processing tasks defined in tasks.yaml.
memorywhale-check
Explicitly check the reviewed MemoryWhale MCP connection; this does not enable capture or write memory.