LLM Evaluation System is an agent-guided platform for evaluating language models and agents, generating datasets and configuring multiple judges from natural-language requests before producing an analysis report. It is for comparing model responses, testing agents, and creating document-grounded evaluation data.
Borrowing it
Nothing to install: this file belongs to awslabs/llm-evaluation-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/awslabs/llm-evaluation-system/main/.claude/skills/port-a-benchmark/SKILL.mdgit clone --depth 1 https://github.com/awslabs/llm-evaluation-systemWrote 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/awslabs/llm-evaluation-system/port-a-benchmark)<a href="https://agentmods.dev/skills/awslabs/llm-evaluation-system/port-a-benchmark"><img src="https://agentmods.dev/badge/skills/awslabs/llm-evaluation-system/port-a-benchmark/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/skills/awslabs/llm-evaluation-system/port-a-benchmark"><img src="https://agentmods.dev/badge/skills/awslabs/llm-evaluation-system/port-a-benchmark.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00118 | $0.01902 |
| Opus 5 | $0.00059 | $0.00951 |
| Sonnet 5 | $0.00024 | $0.00380 |
| Haiku 4.5 | $0.00012 | $0.00190 |
Grade A, and why
port-a-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 10d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Port a Benchmark
A ported benchmark has exactly one job: produce the same measurement the original produces. A port that runs cleanly but measures something subtly different is worse than no port, because the numbers look authoritative and nobody can tell they're wrong.
The failure mode is specific and quiet: the dataset gets copied faithfully (obviously data), while a judge rubric living in a Python string literal gets "adapted" (looks like code). The suite stays green, because the suite exercises the plumbing. Only a line-by-line comparison against upstream catches it.
The rule
Copy the measuring instrument verbatim. Adapt only the plumbing.
Sort every file in the upstream repo into one of two buckets before writing any code. When unsure which bucket something belongs in, it goes in COPY.
COPY — byte-for-byte, no edits, no reformatting
Anything that influences what a score means:
- Datasets, questions, golden/reference answers, expected outputs
- Prompts of every kind — system prompts, judge/grader rubrics, user-turn scaffolding, few-shot examples, instruction preambles
- Tool/function schemas (names, descriptions, required-ness all change model behaviour)
- Scoring thresholds, weights, metric formulas, aggregation rules
- Stop conditions, retry/recovery policy, turn boundaries
Prompts are data, not code. This is the trap. A prompt in a .py string
literal looks like code, so it feels adaptable. It isn't. The wording is the
instrument. Rewriting it for concision or clarity is not a refactor — it's
recalibrating a scale nobody asked you to recalibrate.
ADAPT — rewrite freely
The mechanics of execution, which can't change what's measured:
- Their runner → an Inspect
@taskwith a solver + scorer - Their HTTP/SDK client →
get_model()/execute_tools() - Their storage/logging → our log dir, viewer, S3 sync
- Their CLI → an MCP tool
- Their concurrency, retries-on-transport-error, progress output
Workflow
1. Read the whole upstream harness before writing anything
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.
- 10d ago First seen · 168 lines · 118 tokens per session scan A 0d50bda43d74
port-a-benchmark is a skill published in the GitHub repository awslabs/llm-evaluation-system (23 stars, last pushed today), licensed Apache-2.0. It adds 118 tokens to every session and 1,902 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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