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 ericrisco/rsc-harness --skill nlpgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/nlp)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/nlp"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/nlp.svg" alt="Measured on agentmods" 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 | $0.00142 | $0.03515 |
| Opus 5 | $0.00071 | $0.01758 |
| Sonnet 5 | $0.00028 | $0.00703 |
| Haiku 4.5 | $0.00014 | $0.00351 |
Grade A, and why
nlp 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 3d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nlp — tokenize the text, pick the model type, pick the metric
You own the language-modeling discipline: how raw text becomes tokens, which transformer architecture fits a task, and which metric actually tells you whether it worked. When the question is "which tokenizer," "BERT or GPT or T5 for this," "why does my Catalan text cost 3× the tokens," or "is this BLEU score meaningful," this is the skill. You stop at retrieval, the RAG loop, prompt wording, and the training step itself — those route out (below).
Route out first (loud — do not duplicate these)
- Retrieval embeddings + vector search (which embedding model, chunking, recall@k, rerank)
→
../embeddings-search/SKILL.md. Sentence embeddings live here as a task; using them to retrieve is theirs. - The retrieve → prompt → generate → answer loop and groundedness →
../rag/SKILL.md. - Prompt wording / few-shot / system prompts →
../prompt-engineering/SKILL.md. - Training the network (LoRA/SFT, trainer loop, PyTorch) →
../finetuning/SKILL.md - The training corpus itself (JSONL messages, label sets) →
../training-data/SKILL.md.
Decision: model type per task (get this right before anything else)
Pick the architecture from the task's shape, not from what is trendy. A decoder LLM can technically classify, but a fine-tuned encoder is smaller, faster, cheaper, and usually more accurate on a fixed-label task.
| Task shape | Architecture | Why | Example families* |
|---|---|---|---|
| Understand / label a whole input (classification, NER, extractive QA, similarity) | Encoder (bidirectional) | Attends to the full sentence both directions; cheap to fine-tune and to serve | BERT, RoBERTa, DistilBERT, ModernBERT |
| Free-form generation, chat, few-shot | Decoder (autoregressive) | Attends only to prior tokens; predicts the next token | GPT-style, Llama, Gemma, Qwen |
| Transform input → new text (summarize, translate, generative QA) | Encoder-decoder / seq2seq | Encoder reads all of the source, decoder writes conditioned on it | T5 / FLAN-T5, BART, mT5 |
What ships with it
4 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.
- 3d ago First seen · 226 lines · 142 tokens per session scan A 13c2f1cd66be
nlp is a skill published in the GitHub repository ericrisco/rsc-harness (65 stars, last pushed yesterday), licensed MIT. It adds 142 tokens to every session and 3,515 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
huggingface-transformers
Operational skill for Hugging Face Transformers: pipelines, tokenizers, fine-tuning, and inference with AutoModel APIs.
research-huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alig...
digital-health-clinical-asr-eval
Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipasource diagnostic). Not for ASR auth (/riva-asr).