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name: dedupe-rank description: | Dedupe and rank a raw paper set (papers/papers_raw.jsonl) to produce papers/papers_dedup.jsonl and papers/core_set.csv. Trigger: dedupe, rank, core set, 去重, 排序, 精选论文, 核心集合. Use when: 检索后需要把广覆盖集合收敛成可管理的 core set（用于 taxonomy/outline/mapping）。 Skip if: 已经有人手工整理了稳定的 papers/core_set.csv（无需再次 churn）。 Network: none. Guardrail: 偏 deterministic；输出应可重复（稳定 paper_id、字段规范）。

Dedupe + Rank

Turn a broad retrieved set into a smaller core set for taxonomy/outline building.

This is a deterministic “curation” step: it should be stable and repeatable.

Input

• papers/papers_raw.jsonl

Outputs

• papers/papers_dedup.jsonl
• papers/core_set.csv

Workflow (high level)

1. Dedupe by normalized (title, year) and keep the richest metadata per duplicate cluster.
2. Rank by relevance/recency signals (and optionally pin known classics for certain topics). For LLM-agent topics, also ensure a small quota of prior surveys/reviews is present to support a paper-like Related Work section.
3. Write papers/core_set.csv with stable paper_id values and useful metadata columns (arxiv_id, pdf_url, categories).

Quality checklist

• papers/papers_dedup.jsonl exists and is valid JSONL.
• papers/core_set.csv exists and has a header row.

Script

Quick Start

• python .codex/skills/dedupe-rank/scripts/run.py --help
• python .codex/skills/dedupe-rank/scripts/run.py --workspace <workspace_dir> --core-size 50

All Options

• --core-size <n>: target size for papers/core_set.csv
• queries.md also supports core_size / core_set_size / dedupe_core_size (overrides default when present)

Examples

• Smaller core set for fast iteration:
  • python .codex/skills/dedupe-rank/scripts/run.py --workspace <ws> --core-size 25

Notes

• This step may annotate papers/core_set.csv:reason with tags such as pinned_classic and prior_survey (deterministic, topic-aware guards for survey writing).
• Systematic-review default: if the active pipeline is systematic-review and core_size is not specified, the script keeps the full deduped pool in papers/core_set.csv (so screening does not silently drop candidates).
• This step is deterministic; reruns should be stable for the same inputs.

Troubleshooting

Common Issues

Issue: papers/core_set.csv is too small / empty

Symptom:

• Core set has very few rows.

Causes:

• Input papers/papers_raw.jsonl is small, or many rows are missing required fields.

Solutions:

• Broaden retrieval (or provide a richer offline export) and rerun.
• Lower --core-size only if you intentionally want a small core set.

Issue: Duplicates still appear after dedupe

Symptom:

• Near-identical titles remain.

Causes:

• Title normalization is defeated by noisy exports.

Solutions:

• Clean title fields in the export (strip prefixes/suffixes, fix encoding) and rerun.

Recovery Checklist

• papers/papers_raw.jsonl lines contain title/year/url.
• papers/core_set.csv has stable paper_id values.