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 mxslr/mlcraft --skill domain-nlp-llmgit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/domain-nlp-llm)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-nlp-llm"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-nlp-llm/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/mxslr/mlcraft/domain-nlp-llm"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-nlp-llm.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 | $0.00122 | $0.00699 |
| Opus 5 | $0.00061 | $0.00349 |
| Sonnet 5 | $0.00024 | $0.00140 |
| Haiku 4.5 | $0.00012 | $0.00070 |
Grade A, and why
domain-nlp-llm 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 9d 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NLP / LLM - Method Selection
First decide the cheapest approach that works: prompt, then RAG, then fine-tune, in that order of effort.
Decision table
| Sub-task | Recommended | Notes |
|---|---|---|
| Text classification / sentiment | Fine-tune DeBERTa-v3 / RoBERTa (or a strong LLM few-shot if data is tiny) | Encoder models are cheap, strong, and fast for fixed label sets. |
| NER / token tagging | DeBERTa/RoBERTa token-classification | Span-level F1; watch tokenizer alignment. |
| Semantic search / dedup / clustering | Sentence-embeddings (e5 / BGE / GTE) + vector index | Cosine similarity; evaluate with retrieval metrics. |
| Knowledge Q&A over your docs | RAG (embed + retrieve + LLM answer) | Prefer RAG over fine-tuning for factual/updatable knowledge. |
| Summarize / rewrite / extract / generate | Instruction-tuned LLM (prompted) | Start with prompting + few-shot; structured output via schema. |
| Domain adaptation of an LLM | LoRA/QLoRA fine-tune | Parameter-efficient; only after prompting/RAG proves insufficient. |
| Chatbot / tool-use agent | LLM + tools/function-calling, RAG for grounding | Define tools crisply; add guardrails. |
Choosing prompt vs RAG vs fine-tune
- Prompt / few-shot: fastest; use when the base model can already do it with instructions.
- RAG: when the model lacks knowledge (private/updatable facts). Fixes hallucination better than fine-tuning.
- Fine-tune (LoRA): when you need a behavior/format/style the base model won't follow, or a cheap specialized classifier. Not for injecting facts.
Rigor
- Split leakage: dedup near-duplicate texts; split by document/author/time, not random sentences. For RAG, keep eval questions out of the indexed corpus if testing generalization.
- Metrics: classification uses F1/AUC (macro for imbalance); generation uses task metrics plus LLM-as-judge with a rubric plus human spot-checks; retrieval uses recall@k / nDCG; QA uses exact-match/F1 plus faithfulness/groundedness.
- Robustness: check prompt sensitivity, class imbalance, and demographic/topic subgroups.
- Improve: use
accuracy-improvement-loop(better retriever, hard-negative mining, better chunking, ensemble/judge, fine-tune last).
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.
- 9d ago First seen · 31 lines · 122 tokens per session scan A fae775ad102d
domain-nlp-llm is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 2mo ago), licensed MIT. It adds 122 tokens to every session and 699 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 2026-08-31.
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