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 instructions/jfrog/agent-belt/agents-mdgit clone --depth 1 https://github.com/jfrog/agent-beltWhat 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 | $0.02594 | $0.02594 |
| Opus 5 | $0.01297 | $0.01297 |
| Sonnet 5 | $0.00519 | $0.00519 |
| Haiku 4.5 | $0.00259 | $0.00259 |
Grade A, and why
agent-belt AGENTS.md 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 2d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-belt - Guide for AI Coding Agents
This file is the single entrypoint. Read it top-to-bottom and you have everything you need to make a non-trivial change to this repo.
1. What this is
A universal evaluation harness for headless CLI agents. It runs multi-turn scenarios against any agent that has a CLI, scores the results with a combination of rule-based checks and LLM judges, and aggregates them into reports.
The only public surface is the belt console script. Everything
under src/belt/{commands,runner,scorer,aggregator}/ is internal -
callers that aren't the CLI must go through documented agent/scorer
extension points.
2. Setup & first verification
uv sync # creates .venv, installs locked dev deps
uv run belt doctor # checks Python, agents, providers, env
make check # lint + test - same as CI
belt doctor is the fastest way to sanity-check the install: it
verifies entry points resolve, agents are reachable, judge providers are
configured, and the active clone is the one your belt command
points at (a real foot-gun if you have multiple checkouts).
3. Where to start (by task)
| Task | Open this first |
|---|---|
| Add a new agent | docs/glossary/PLUGGABILITY.md (Authoring an agent), then src/belt/agent/base.py |
| Modify an existing agent's behaviour | src/belt/agent/<agent>.py (subclass BaseAgentAdapter) |
| Add a new scorer plugin | docs/glossary/PLUGGABILITY.md (Authoring a scorer), then src/belt/scorer/base.py |
| Add a new rule-based check | src/belt/scorer/rules/scorer.py (read neighbours first) |
| Change LLM judging or prompts | src/belt/scorer/llm/scorer.py |
| Add an LLM provider | src/belt/scorer/llm/backend.py (subclass BaseJudgeBackend) |
| Change scenario JSON shape | src/belt/entities.py + docs/glossary/SCENARIOS.md (reference appendix) |
| Add a CLI flag | src/belt/commands/<cmd>.py (argparse only) |
| Add a CLI subcommand | New commands/<name>.py (thin) + new module under the right phase library |
| Change run-phase pipeline | src/belt/runner/phases/ |
| Change threshold/aggregation logic | src/belt/aggregator/ |
| Add a new exporter (CSV / JUnit / vendor plugin) | docs/glossary/PLUGGABILITY.md (Authoring an exporter), then src/belt/exporter/base.py |
Tune the export phase or chain on eval / aggregate |
src/belt/commands/export.py (registry + dispatch) |
| Author a scenario | docs/glossary/SCENARIOS.md + examples/scenarios/ |
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.
- 2d ago First seen · 186 lines · 2,594 tokens per session scan A 18de22e8b291
agent-belt AGENTS.md is an instructions file published in the GitHub repository jfrog/agent-belt (18 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 2,594 tokens to every session, about $0.0130 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.
Other instructions, from other repositories
mobilegym AGENTS.md
Instructions for Purewhiter/mobilegym, covering agents.md, project overview, type-checking strategy, eslint and with data graph generation.
nagi-bench AGENTS.md
AGENTS.md instructions for nagi-studio/nagi-bench, covering agents.md, what this repo is, a contribution is exactly two files, models/ .json schema and naming rules.
agenteval copilot-instructions.md
Instructions for lukasmetzler/agenteval, covering copilot instructions, language, rules and suggestions.
agenteval backend.instructions.md
Instructions for lukasmetzler/agenteval, covering backend instructions, api design, database and performance.
mobilegym CLAUDE.md
Instructions for Purewhiter/mobilegym, a project described as:
agenteval CLAUDE.md
Instructions for lukasmetzler/agenteval, covering agenteval, commands, install (end users), development and architecture.