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-nlp-toolkitgit 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-nlp-toolkit)<a href="https://agentmods.dev/skills/squadcodercom/squadcoder/hebrew-nlp-toolkit"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/hebrew-nlp-toolkit/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-nlp-toolkit"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/hebrew-nlp-toolkit.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 | $0.00166 | $0.03109 |
| Opus 5.5 | $0.00066 | $0.01244 |
| Sonnet 5.5 | $0.00033 | $0.00622 |
| Haiku 4.5 | $0.00017 | $0.00311 |
Grade A, and why
hebrew-nlp-toolkit 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.
This is a copy
98% identical to hebrew-nlp-toolkit — 138 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hebrew NLP Toolkit
Instructions
Step 1: Identify the NLP Task
| Task | Recommended Model | HuggingFace ID | Size | Notes |
|---|---|---|---|---|
| Text generation (large) | DictaLM 3.0 24B Base | dicta-il/DictaLM-3.0-24B-Base |
24B | Best Hebrew generation, built on Mistral-Small-3.1-24B |
| Text generation (small) | DictaLM 3.0 Nemotron Instruct | dicta-il/DictaLM-3.0-Nemotron-12B-Instruct |
12B | Instruction-tuned, smaller footprint |
| Reasoning / chain-of-thought | DictaLM 3.0 24B Thinking | dicta-il/DictaLM-3.0-24B-Thinking |
24B | Emits explicit thinking blocks before answering |
| Lightweight / edge | DictaLM 3.0 1.7B Thinking (GGUF) | dicta-il/DictaLM-3.0-1.7B-Thinking-GGUF |
1.7B | Runs on laptop / CPU via llama.cpp |
| Classification / fill-mask | DictaBERT | dicta-il/dictabert |
184M | Fast, good accuracy |
| NER | DictaBERT NER | dicta-il/dictabert-ner |
184M | Recognizes PER, GPE, TIMEX, TTL |
| Sentiment | DictaBERT Sentiment | dicta-il/dictabert-sentiment |
184M | Hebrew sentiment classification |
| Morphology | DictaBERT Morph | dicta-il/dictabert-morph |
184M | Prefix segmentation and POS |
| Hebrew QA | DictaBERT HeQ | dicta-il/dictabert-heq |
184M | Extractive question answering |
| Embeddings (modern) | NeoDictaBERT Bilingual Embed | dicta-il/neodictabert-bilingual-embed |
400M | Hebrew-English sentence embeddings |
| Embeddings (legacy) | AlephBERT | onlplab/alephbert-base |
110M | Older baseline for similarity |
| Speech-to-text | ivrit.ai Whisper v3 | ivrit-ai/whisper-large-v3 |
1.55B | Fine-tuned on the ivrit.ai Hebrew speech corpus (a 22K+ hour dataset) |
| Speech-to-text (fast) | ivrit.ai Whisper v3 Turbo CT2 | ivrit-ai/whisper-large-v3-turbo-ct2 |
809M | CTranslate2, ~3x faster inference |
Step 2: Install and Load Model
DictaBERT (base model, fill-mask):
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert")
model = AutoModelForMaskedLM.from_pretrained("dicta-il/dictabert")
dicta-il/dictabert is a masked-LM base with NO classification head. Do not load it with AutoModelForSequenceClassification and run inference, that instantiates a randomly-initialised head and returns meaningless predictions. For classification, either fine-tune it on labeled data first, or use a ready task-specific model such as dicta-il/dictabert-sentiment (sentiment) or dicta-il/dictabert-ner (NER).
What ships with it
5 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.
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 · 174 lines · 166 tokens per session scan A 58f6afd165a5
hebrew-nlp-toolkit is a skill published in the GitHub repository squadcodercom/squadcoder (12 stars, last pushed 3mo ago), licensed MIT. It adds 166 tokens to every session and 3,109 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to hebrew-nlp-toolkit, differing in 138 lines, and is treated as a copy.
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