---
name: literature-experiment-extract
description: Extract experimental models, experimental methods, and biomarker information from paper Markdown (typically produced by PDF-to-Markdown tools) when a user provides paper Markdown and needs a structured, evidence-backed summary (1 Markdown + 3 CSVs).
license: MIT
author: AIPOCH
---
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)


## When to Use

- You have a paper converted to Markdown (e.g., via PDF-to-Markdown) and need to extract **cell/animal models** used in experiments.
- You need a structured list of **experimental methods/protocols** described in the paper, with traceable evidence.
- You want to compile **biomarkers / detection indicators** (e.g., genes, proteins, assays, readouts) reported in the study.
- You need standardized outputs for downstream analysis: **one Markdown summary plus three CSV tables**.
- The paper Markdown includes page markers (e.g., `## Page XX`) and you want evidence organized **by page**.

## Key Features

- Extracts three entity groups from paper Markdown:
  - **Experimental models** (cell lines, animal models, strains, genotypes, etc.)
  - **Experimental methods** (assays, protocols, instruments, conditions)
  - **Biomarkers / indicators** (targets, readouts, measured variables)
- Produces **evidence-backed** results (citations/excerpts preserved and traceable to the source).
- Supports **page-aware evidence organization** when the input includes pagination headers like `## Page XX`.
- Outputs are fixed and standardized:
  - **1 Markdown summary**
  - **3 CSV files**: models / methods / biomarkers
- Uses a predefined template and extraction rules:
  - Requirements and consistency rules: `references/guide.md`
  - Output template: `assets/template.md`

## Dependencies

- None (documentation-driven workflow).
- Input assumption: paper content is available as **Markdown**, typically generated by a **PDF-to-Markdown** tool.

## Example Usage

### Input

A paper converted to Markdown, ideally with page headers:

```md
## Page 1
... text describing "C57BL/6 mice" and "Western blot" ...

## Page 2
... text describing "ELISA" and "IL-6 levels" ...
```

### Steps

1. Open the paper Markdown (typically produced by PDF-to-Markdown tools).
2. Extract **models**, **methods**, and **biomarkers** page by page.
3. Follow:
   - Extraction rules and evidence requirements: `references/guide.md`
   - Output template: `assets/template.md`
4. Output **exactly**:
   - `outputs/{Paper Abbreviation}-experiment-summary.md`
   - `outputs/{Paper Abbreviation}-models.csv`
   - `outputs/{Paper Abbreviation}-methods.csv`
   - `outputs/{Paper Abbreviation}-biomarkers.csv`

### Output (required)

- All final outputs must be **UTF-8** encoded.
- Output must be produced **directly** (no confirmation steps or optional branches).
- Evidence excerpts must remain in the **original language** of the source literature.

## Implementation Details

- **Input parsing**
  - Read the paper Markdown as the sole input source.
  - If pagination headers like `## Page XX` exist, prioritize attaching evidence to the corresponding page.

- **Extraction rules**
  - Apply entity definitions, allowed/expected fields, normalization rules, and evidence formatting as specified in `references/guide.md`.

- **Output formatting**
  - Generate outputs using `assets/template.md` as the canonical structure.
  - Add rows as needed while preserving evidence citations/excerpts.
  - The output set is fixed: **1 Markdown summary + 3 CSVs** (models/methods/biomarkers).

- **Paths and naming**
  - Default output directory: `outputs/`
  - Naming:
    - Markdown: `outputs/{Paper Abbreviation}-experiment-summary.md`
    - CSVs:
      - `outputs/{Paper Abbreviation}-models.csv`
      - `outputs/{Paper Abbreviation}-methods.csv`
      - `outputs/{Paper Abbreviation}-biomarkers.csv`

- **Language**
  - Output language should be **Chinese by default** (or the user-requested language if specified).
  - Evidence excerpts must remain in the **original language** of the source text.

## When Not to Use

- Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
- Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
- Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

## Required Inputs

| Field | Required | Format/Source | Example | If Missing |
|---|---|---|---|---|
| User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide |
| Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing |
| Output preference | No | Text | Language, format, target journal, template | Use skill default format |

## Output Contract

- Primary output: Structured result or target file aligned with this skill's objective.
- Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
- Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
- If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

## Failure Handling

- Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
- Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
- Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

## User Checkpoints

- Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
- Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.


## Input Validation

This skill accepts requests that match the documented purpose of `literature-experiment-extract` and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

> `literature-experiment-extract` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

## Quick Validation

- Check that key scripts, templates, or reference file paths this skill depends on exist.
- Check that the final output contains the core fields, sections, or files specified for this task.
- Check that results clearly mark assumptions, limitations, and incomplete items.