literature-experiment-extract
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).
Best use case
literature-experiment-extract is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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).
Teams using literature-experiment-extract should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/literature-experiment-extract/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How literature-experiment-extract Compares
| Feature / Agent | literature-experiment-extract | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **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.Related Skills
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