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/MilindGaharwar/fettleWrote 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/milindgaharwar/fettle/review)<a href="https://agentmods.dev/commands/milindgaharwar/fettle/review"><img src="https://agentmods.dev/badge/commands/milindgaharwar/fettle/review/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/milindgaharwar/fettle/review"><img src="https://agentmods.dev/badge/commands/milindgaharwar/fettle/review.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 | $0.00169 |
| Opus 5.5 | $0.00000 | $0.00068 |
| Sonnet 5.5 | $0.00000 | $0.00034 |
| Haiku 4.5 | $0.00000 | $0.00017 |
Grade A, and why
review 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 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.
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
86% identical to fettle-review — 6 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
/fettle:review
Run an independent cross-review of a file using a different LLM.
Usage
When the user invokes /fettle:review:
- Identify the file to review (ask if not obvious from context).
- Run:
python3 -m fettle.review --file PATH - Present the review findings.
Configuration
# .fettle.toml
[review]
provider = "ollama" # ollama | proxy | openai
endpoint = "http://localhost:11434/v1"
model = "llama3.2"
Purpose
Gets a "second opinion" from a different model. Useful for:
- Critical code paths
- Security-sensitive changes
- Before merging complex PRs
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 · 32 lines · 0 tokens per session scan A a3baed376999
review is a command published in the GitHub repository MilindGaharwar/fettle (4 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 169 tokens. A static security scan graded it A with 0 findings. It is 86% identical to fettle-review, differing in 6 lines, and is treated as a copy.
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