# Extraction Form

> Extraction Form is an Agent Skill for documents published in the WILLOSCAR/research-units-pipeline-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Use when `evidence-review` has screened includes and needs a schema-aligned extraction table. **Trigger**: extraction form, extraction table, data extraction, 信息提取, 提取表. **Use when**: `evidence-review` 在 screening 后进入 extraction（C4），需要把纳入论文按字段落到 CSV 以支持后续 synthesis。 **Skip if**: 还没有 `papers/screening_log.csv` 或 protocol 未锁定。 **Network**: none. **Guardrail**: 严格按 schema 填字段；不要在此阶段写 narrative synthesis（那是 `synthesis-writer`）。

- Skill name: `extraction-form`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/WILLOSCAR/research-units-pipeline-skills/blob/HEAD/.codex/skills/extraction-form/SKILL.md
- Repository: WILLOSCAR/research-units-pipeline-skills (487 GitHub stars when collected)

## SKILL.md

# Extraction Form

Transforms screened include rows plus protocol schema into the analysis table used by `evidence-review`.

## Inputs

Required:
- `papers/screening_log.csv`
- `output/PROTOCOL.md`

Optional:
- `papers/paper_notes.jsonl`

## Output

- `papers/extraction_table.csv`

## Contract

The table must:
- contain one row per included paper
- preserve provenance columns (`paper_id`, `title`, `year`, `url`)
- include protocol-defined extraction fields
- keep narrative residue in `notes`, not in schema columns

## Script boundary

`scripts/run.py` should:
- parse the extraction schema from the protocol
- filter `include` rows
- materialize a normalized CSV

## Acceptance

- output exists
- include rows map 1:1 to extraction rows
- schema matches `output/PROTOCOL.md`

## Non-goals

- synthesis writing
- bias scoring

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Source: https://www.openparable.com/skill/documents/extraction-form (OpenParable). Cite the canonical URL above.