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 commands/sattyamjjain/proofloop/benchmarkgit clone --depth 1 https://github.com/sattyamjjain/proofloopWhat 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 | $0.00008 | $0.00661 |
| Opus 5 | $0.00004 | $0.00331 |
| Sonnet 5 | $0.00002 | $0.00132 |
| Haiku 4.5 | $0.00001 | $0.00066 |
Grade A, and why
benchmark 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.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/benchmark — Compare Against Ideal Benchmarks
Compare a skill's historical performance against defined benchmark standards.
Arguments
skill-name(required): The skill to benchmark.
What to Do
- Read benchmark standards from
skills/judge/references/benchmark-standards.md - Read historical scores for the specified skill from
skills/judge/scores/ - Compute average per dimension across all historical evaluations
- Compare against benchmark:
┌───────────────────────────────────────────────────────────────┐
│ PROOFLOOP BENCHMARK — {skill-name} │
├────────────────┬──────────┬───────────┬───────────────────────┤
│ Dimension │ Your Avg │ Benchmark │ Delta │
├────────────────┼──────────┼───────────┼───────────────────────┤
│ Correctness │ 8.2 │ 8.5 │ -0.3 (Below) │
│ Completeness │ 7.5 │ 8.0 │ -0.5 (Below) │
│ Adherence │ 9.0 │ 8.0 │ +1.0 (Above) │
│ Actionability │ 8.0 │ 8.0 │ 0.0 (On target) │
│ Efficiency │ 7.0 │ 7.5 │ -0.5 (Below) │
│ Safety │ 9.5 │ 9.0 │ +0.5 (Above) │
│ Consistency │ 6.5 │ 7.0 │ -0.5 (Below) │
├────────────────┼──────────┼───────────┼───────────────────────┤
│ COMPOSITE │ 8.05 │ 8.14 │ -0.09 │
└────────────────┴──────────┴───────────┴───────────────────────┘
Strengths: Adherence (+1.0), Safety (+0.5)
Weaknesses: Completeness (-0.5), Efficiency (-0.5), Consistency (-0.5)
Recommendations:
1. Focus on covering all requirements (Completeness gap)
2. Reduce unnecessary tool calls (Efficiency gap)
3. Build more consistent quality (Consistency gap)
If no scores exist for the skill, inform the user to run /judge first.
Regression benchmark (CI gate)
The /benchmark command above compares a skill's own history to
reference standards. For heuristic-regression testing, use
scripts/benchmark_pack.py instead:
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 · 65 lines · 8 tokens per session scan A f9d2780459ce
benchmark is a command published in the GitHub repository sattyamjjain/proofloop (5 stars, last pushed 2mo ago), licensed MIT. It adds 8 tokens to every session and 661 once invoked, about $0.0000 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 2026-08-31.
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