Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jfrog/agent-beltnpx agentmods add skills/jfrog/agent-belt/beltWrote 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/jfrog/agent-belt/belt)<a href="https://agentmods.dev/skills/jfrog/agent-belt/belt"><img src="https://agentmods.dev/badge/skills/jfrog/agent-belt/belt.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00116 | $0.04086 |
| Opus 5 | $0.00058 | $0.02043 |
| Sonnet 5 | $0.00023 | $0.00817 |
| Haiku 4.5 | $0.00012 | $0.00409 |
Grade A, and why
belt 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-belt
belt is a CLI that evaluates headless coding-agent CLIs by running multi-turn scenarios against
them and scoring the results with rule-based checks plus optional LLM judges. The belt console
script is the only public surface - never import internals.
1. Verify install before doing anything
belt doctor
Checks Python, registered agents (auth + reachability), LLM scoring providers (cloud keys + Ollama),
and which belt clone the command resolves to. If doctor is unhappy, fix what it reports
before attempting anything else - most user-reported problems are solved by reading its output.
belt agent list # registered agents (entry-point discovered)
belt agent info <name> # capabilities of one agent (cli_options, env vars, fields it supports)
2. Run an evaluation
belt eval chains run → score → aggregate in one command. Start here.
belt quickstart # auto-detect, single rules-only
belt eval examples/scenarios/showcase --modes rules --tags real-runnable # whole runnable showcase
belt eval my-scenarios/ --modes rules,llm --workers 3 # rules + LLM judge, parallel
belt eval my-scenarios/ --dry-run # list matched scenarios, no run
belt eval my-scenarios/ --modes rules --export junit:report.xml # JUnit report for CI test reporters
--modes rules runs without any judge or API key - use it for fast feedback and CI smoke tests.
--modes llm (or rules,llm) requires an LLM judge configured (see §5).
Scenario filtering is path-relative to the directory passed as <path>; if the path itself is a group
(contains _config.json), --scenarios takes the bare scenario name. Tag filters use AND semantics
across the listed tags.
Subcommand index, common workflows, and progress modes:
docs/glossary/CLI.md.
The canonical per-flag reference is belt <subcommand> --help.
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.
- 7d ago First seen · 327 lines · 116 tokens per session scan A 2046876b4744
belt is a skill published in the GitHub repository jfrog/agent-belt (18 stars, last pushed 5d ago), licensed Apache-2.0. It adds 116 tokens to every session and 4,086 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 2026-08-30.
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