---

name: pruning-export-evaluation description: "Export SPLADE vectors for Anserini, prune Anserini-style JSONL, and plan BEIR/PISA evaluation without unsafe external downloads." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SPLADE Pruning, Export, and Evaluation

Use this sub-skill when the task involves Anserini export files, static pruning of SPLADE JSONL vectors, BEIR evaluation planning, PISA evaluation planning, or optional external evaluation dependencies.

Read First

• references/export-evaluation.md: splade.create_anserini, EncodeAnserini formats, BEIR/PISA planning, and external engine boundaries.
• references/pruning-workflows.md: safe static pruning workflows, bundled script usage, and why upstream shell workflows are reference-only.
• references/troubleshooting.md: quantization, empty vectors, BEIR downloads, Java/Pyserini/Anserini, PISA, and pytrec_eval failures.

Route Tasks

• Use this sub-skill for python -m splade.create_anserini, docs_anserini.jsonl, queries_anserini.tsv, pruning, BEIR, PISA, Pyserini/Anserini, and TREC metric dependency questions.
• Route core SPLADE training, indexing, retrieval, Hydra package selection, and toy/full pipeline commands to ../hydra-pipelines/SKILL.md.
• Route model classes, data loaders, raw/qrel schemas, vector semantics, and API smoke checks to ../model-data-api/SKILL.md.
• Route HuggingFace Trainer training or reranking workflows to ../hf-training-reranking/SKILL.md.

Safe Actions

• Generate export command templates with python -m splade.create_anserini; do not assume Anserini or PISA is installed.
• Use scripts/prune_doc_index.py for local top-k or value-threshold pruning of Anserini-style document JSONL.
• Use scripts/prune_quantile.py for local per-token quantile pruning of Anserini-style document JSONL.
• Use --dry-run and --limit before writing large pruned collections.
• Treat BEIR, Anserini, Pyserini, PISA, and metric evaluation as explicit external-prerequisite workflows that may require downloads, Java, compiled binaries, or network access.

Avoid

• Do not run BEIR downloads, Pyserini indexing/search, Anserini regressions, PISA binaries, or large benchmark workflows unless the user explicitly approves external engines, downloads, and runtime cost.
• Do not point runtime instructions at the original repository shell scripts; use the bundled Python pruning scripts and the references in this sub-skill.
• Do not treat pruning output as fully evaluated retrieval quality until an external index/search/evaluation stack has been prepared and run intentionally.