Literature Experiment Extract

Source: 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:

## 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

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.