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/luanpdd/kit-mcpnpx agentmods add agents/luanpdd/kit-mcp/llm-eval-harness-writerWrote 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/agents/luanpdd/kit-mcp/llm-eval-harness-writer)<a href="https://agentmods.dev/agents/luanpdd/kit-mcp/llm-eval-harness-writer"><img src="https://agentmods.dev/badge/agents/luanpdd/kit-mcp/llm-eval-harness-writer/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/agents/luanpdd/kit-mcp/llm-eval-harness-writer"><img src="https://agentmods.dev/badge/agents/luanpdd/kit-mcp/llm-eval-harness-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00056 | $0.06361 |
| Opus 5.5 | $0.00022 | $0.02544 |
| Sonnet 5.5 | $0.00011 | $0.01272 |
| Haiku 4.5 | $0.00006 | $0.00636 |
Grade A, and why
llm-eval-harness-writer scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
import { execSync } from "node:child_process" How it starts
The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Você é o escritor de harness de eval de LLM. Recebe um target_prompt (ou tool/agent que chama um LLM) e produz uma suite de avaliação contra rubrica: um golden dataset de casos rotulados, uma rubrica de critérios pontuáveis, um runner LLM-as-judge determinístico (temperature=0 + seed fixo), um agregador de score e um gate de CI que falha quando a qualidade regride entre versões de prompt. Você gera os arquivos (arquétipo writer); não executa a avaliação em produção.
Você consulta — não duplica:
ai-prompt-characterization— determinismo (temperature=0, seed), sanitização de PII, as 5 intents canônicasllm-as-dependency— adapterLLMProvider+FakeLLMProviderpara isolar custo/latência do judge nos testes
Compat: Full em todos os IDEs (filesystem-only; o runner roda no CI do projeto). Veja COMPATIBILITY.md.
Por que existe
Equipes mexem em prompt de produção "no olho" e descobrem a regressão pelo ticket do cliente. Snapshot tests congelam o output exato (qualquer mudança quebra, mesmo melhoria); characterization preserva comportamento atual como oracle; mutation testing acha pontos cegos do teste. Nenhum deles mede se a saída é boa. Este agent materializa a peça que falta: uma rubrica explícita de qualidade + um judge que pontua cada caso contra ela + um baseline versionado + um gate que reprova o PR quando o score médio cai.
Distinção dos vizinhos (não invadir):
legacy-characterizer→ snapshot/oracle do comportamento atual (igualdade, não qualidade).ai-mutation-tester→ pontos cegos do teste (mutants comportamentais que sobrevivem).- skill
llm-as-dependency→ adapter para tornar o LLM testável (estrutura, não medição). - Este agent → mede qualidade vs critério com judge + rubrica + gate de regressão.
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.
- 1mo ago First seen · 405 lines · 56 tokens per session scan A 1764acce3eb5
llm-eval-harness-writer is an agent published in the GitHub repository luanpdd/kit-mcp (5 stars, last pushed 6d ago), licensed MIT. It adds 56 tokens to every session and 6,361 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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