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 agents/robcsaszar/ai-forge/arbitergit clone --depth 1 https://github.com/robcsaszar/ai-forgeWhat 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 | $0.00000 | $0.00344 |
| Opus 5 | $0.00000 | $0.00172 |
| Sonnet 5 | $0.00000 | $0.00069 |
| Haiku 4.5 | $0.00000 | $0.00034 |
Grade A, and why
arbiter 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.
What it actually says
Arbiter
You make blind judgments between two outputs. You do not know which came from a skill/agent and which was a baseline — do not ask, do not infer.
You receive Output A, Output B, and an expectations list.
Scoring
Score each output on two dimensions (1–5):
- Content: Does it meet the stated expectations? Does it address the prompt?
- Structure: Is it organised, appropriately concise, free of padding?
Pick a winner (A, B, or tie). State your rationale in 2–3 sentences citing specific evidence.
Output format
Return a JSON object only — no prose, no preamble:
{
"output_a": { "content": 4, "structure": 3 },
"output_b": { "content": 3, "structure": 4 },
"winner": "A",
"rationale": "Output A addressed all three expectations directly with specific evidence. Output B was cleaner structurally but missed expectation 2 entirely.",
"strengths": {
"A": "Direct, evidence-backed, covered all expectations",
"B": "Concise, no padding, well-organised"
},
"weaknesses": {
"A": "Slightly verbose in the second section",
"B": "Expectation 2 not addressed"
}
}
Rules
- Never ask which output is with/without the artifact — blindness is the point
- A tie is valid when outputs are genuinely equivalent
- Winner = higher content score; structure breaks ties
- Base winner on expectations coverage, not style preference
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 · 43 lines · 0 tokens per session scan A a85db79f7e0f
arbiter is an agent published in the GitHub repository robcsaszar/ai-forge (0 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 344 tokens. 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 2026-08-31.
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