---

name: data-and-config description: "Work with FlashRAG Config, dataset JSONL, corpus JSONL, Item/Dataset concepts, safe input validation, and reproducible path/save behavior before retrieval or pipeline execution." disable-model-invocation: true metadata: disco-role: operating license: MIT

FlashRAG Data and Config

Use this sub-skill when preparing or reviewing FlashRAG inputs: YAML/dict configuration, dataset rows, corpus rows, split handling, save/output behavior, and safe validation before building indexes or running pipelines.

Route by task

• Config merge and defaults: Start with configuration.md for priority, required keys, basic_config sections, path derivation, and disable_save.
• Dataset and corpus files: Use data-formats.md to distinguish evaluation JSONL from retrieval corpus JSONL and to create tiny fixtures.
• APIs and utilities: Use api-reference.md for Config, Item, Dataset, and dataset utility behavior.
• Validation without runtime imports: Run validate_flashrag_inputs.py before expensive pipelines or index builds.
• Failure diagnosis: Use troubleshooting.md for dependency symptoms, invalid JSONL, split normalization, required config keys, and save-directory surprises.

Boundaries

This sub-skill covers configuration and input data readiness only. For corpus indexing, retriever model paths, cache behavior, and FAISS/BM25 details, switch to retrieval-and-indexing. For actually running methods, generators, refiner/pipeline behavior, and method-specific configs, switch to pipelines-and-methods.

Quick safe check

Validate a config plus evaluation and corpus JSONL without importing FlashRAG:

python skills/flashrag/sub-skills/data-and-config/scripts/validate_flashrag_inputs.py \
  --config my_config.yaml \
  --eval-jsonl dataset/nq/test.jsonl \
  --corpus-jsonl indexes/general_knowledge.jsonl

Use --show-effective-summary to print the high-impact keys that should be reviewed before routing to retrieval or pipelines.