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 skills add squadcodercom/squadcoder --skill hebrew-llm-eval-suitegit clone --depth 1 https://github.com/squadcodercom/squadcoderWrote 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/squadcodercom/squadcoder/hebrew-llm-eval-suite)<a href="https://agentmods.dev/skills/squadcodercom/squadcoder/hebrew-llm-eval-suite"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/hebrew-llm-eval-suite/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/squadcodercom/squadcoder/hebrew-llm-eval-suite"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/hebrew-llm-eval-suite.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 | $0.00261 | $0.05432 |
| Opus 5.5 | $0.00104 | $0.02173 |
| Sonnet 5.5 | $0.00052 | $0.01086 |
| Haiku 4.5 | $0.00026 | $0.00543 |
Grade A, and why
hebrew-llm-eval-suite 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 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.
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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hebrew LLM Eval Suite
Problem
Israeli product teams pick LLMs blind. There is no standardized Hebrew benchmark that a PM can run in an afternoon to compare Claude against GPT against DictaLM against AI21 Jamba on their actual use case. The HuggingFace Open Hebrew LLM Leaderboard exists but is built for base models and few-shot prompts, not for API-hosted chat models. DictaLM publishes benchmark results but only for its own suite. Teams end up guessing, testing informally, or trusting marketing claims. The result is costly model switches after launch, or shipping Hebrew products on models that silently fail on native speakers.
Instructions
Step 1: Pick the right benchmark set for your task
Different benchmarks test different things. Choose the smallest set that covers your actual use case.
| Benchmark | HuggingFace ID | What it tests | When to use |
|---|---|---|---|
| HeQ (Hebrew Question Answering) | Etelis/HeQ_v1 (HF mirror); canonical at github.com/NNLP-IL/Hebrew-Question-Answering-Dataset |
Reading comprehension, extractive QA on Hebrew Wikipedia and Geektime articles. 30,147 questions | Any product that answers questions over Hebrew text: search, RAG, support, research assistants |
| HebrewSentiment | HebArabNlpProject/HebrewSentiment |
Sentiment classification (positive, negative, neutral). 41,305 samples. License CC-BY-4.0 | Social media analysis, review classification, product feedback |
| Hebrew Winograd | Community port of Winograd Schema Challenge (cs.ubc.ca/~vshwartz/resources/winograd_he.jsonl) |
Pronoun resolution requiring world knowledge. Reasoning-heavy | Any product that needs nuanced Hebrew understanding |
| NeuLabs-TedTalks (translation) | OPUS NeuLab-TedTalks en-he subset | English to Hebrew and Hebrew to English translation quality | Translation products, multilingual apps |
| HebNLI | HebArabNlpProject/HebNLI |
Natural Language Inference in Hebrew | Classification, content moderation, logical reasoning |
| HEBREW-MMLU (general knowledge) | Hebrew-translated MMLU subset, used by the Open Hebrew LLM Leaderboard ecosystem (verify the active HF mirror before use; openai/MMMLU covers 14 languages but Hebrew is not in the official set) |
14-subject general-knowledge accuracy; Hebrew adaptation of Massive Multitask Language Understanding | General-purpose chat/RAG products that need broad world knowledge in Hebrew |
| DictaLM 3.0 Summarization | Dicta benchmark suite (see DictaLM 3.0 technical report) | Abstractive summarization of Hebrew news | Summarization tools, executive briefings |
| DictaLM 3.0 Nikud | Dicta benchmark suite | Adding vowel diacritics to unvocalized Hebrew | Educational tools, TTS preprocessing, religious text tools |
| DictaLM 3.0 Israeli Trivia | Dicta benchmark suite | Knowledge of Israeli culture, geography, history, politics | Consumer products where cultural grounding matters |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evidence.json 7.2 KB
- metadata.json 2.4 KB
- references/.gitkeep 0 B
- references/benchmark-catalog.md 5.2 KB
- references/prompt-templates.md 5.6 KB
- scripts/.gitkeep 0 B
- scripts/make_scorecard.py 5.4 KB runs code
- scripts/run_eval.py 8.7 KB runs code
- scripts/score_results.py 5.8 KB runs code
- SKILL_HE.md 22 KB
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 · 257 lines · 261 tokens per session scan A 30dd1f4ba823
hebrew-llm-eval-suite is a skill published in the GitHub repository squadcodercom/squadcoder (12 stars, last pushed 3mo ago), licensed MIT. It adds 261 tokens to every session and 5,432 once invoked, about $0.0010 per session on Opus 5.5. 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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