Individual skill
training-data
Use when assembling or curating the corpus a fine-tune trains on — turning raw examples into the JSONL shape a trainer expects (instruction, conversational, preference-pair or binary-label), matching the data to the model's chat template, generating synthetic or distilled examples, deduplicating and decontaminating against eval sets, and quality-filtering. NOT cleaning tabular rows, nulls and dtypes (that is `data-cleaning`), NOT building a retrieval corpus of chunks and embeddings (that is `embeddings-search`), NOT running the trainer or picking hyperparameters (that is `finetuning`).
skills/training-data/SKILL.md
Source commit d9d6bb338f66cc1e23998585e580a0ea45bbcb82
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- Last checked
- Nov 6, 2026
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- 546
- Repository last updated
- Nov 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
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- training-data Declared skill tag
- fine-tuning-dataset Declared skill tag
- jsonl Declared skill tag
- chat-template Declared skill tag
- preference-data Declared skill tag
- dpo Declared skill tag
- kto Declared skill tag
- synthetic-data Declared skill tag
- decontamination Declared skill tag
- dataset-curation Declared skill tag
- data Derived category
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SKILL.mdcore · 14,776 bytesSKILL.mdcore · 14,776 bytesSKILL.mdcore · 14,776 bytesSKILL.mdcore · 14,776 bytesSKILL.mdcore · 14,776 bytesevals/README.mddoc · 1,066 bytesevals/cases.yamlasset · 3,547 bytesreferences/formats.mddoc · 5,250 bytesreferences/synthesis-dedup-quality.mddoc · 6,602 bytes
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# training-data — the corpus a fine-tune actually eats
You own the **training corpus**: the JSONL of chat turns, instruction triples, or preference
pairs that a trainer reads. The deliverable is a validated, deduplicated, decontaminated,
license-clean file in the **exact shape the trainer expects**, rendered through the **target
model's chat template**. You stop the moment that file loads cleanly and round-trips through
`apply_chat_template`. You do not choose LoRA rank or launch the run — that is
[`finetuning`](../finetuning/SKILL.md) / [`unsloth`](../unsloth/SKILL.md).
**Loud boundary.** This is *LLM training corpora* — messages, instruction triples, preference
pairs. It is **not**:
- Tabular row cleaning — nulls, dtypes, dedupe of CSV rows, category normalization → [`data-cleaning`](../data-cleaning/SKILL.md).
- A *retrieval* corpus — chunking documents and embedding them for search → [`embeddings-search`](../embeddings-search/SKILL.md).