Medical-research-skills phenotype-introduction
Expert system for generating comprehensive biomedical phenotype introductions with structured academic content. Use when users request detailed explanations of cellular phenotypes including concept, mechanism, regulation, and detection methods in Chinese academic writing.
install
source · Clone the upstream repo
git clone https://github.com/aipoch/medical-research-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/aipoch/medical-research-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/scientific-skills/Evidence Insight/phenotype-introduction" ~/.claude/skills/aipoch-medical-research-skills-phenotype-introduction && rm -rf "$T"
manifest:
scientific-skills/Evidence Insight/phenotype-introduction/SKILL.mdsource content
Phenotype Introduction
When to Use
- Use this skill when you need "expert system for generating comprehensive biomedical phenotype introductions with structured academic content. use when users request detailed explanations of cellular phenotypes including concept, mechanism, regulation, and detection methods in chinese academic writing." in a reproducible workflow.
- Use this skill when a evidence insight task needs a packaged method instead of ad-hoc freeform output.
- Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
- Use this skill when
is the most direct path to complete the request.scripts/example.py - Use this skill when you need the
package behavior rather than a generic answer.phenotype-introduction
Key Features
- Scope-focused workflow aligned to: "Expert system for generating comprehensive biomedical phenotype introductions with structured academic content. Use when users request detailed explanations of cellular phenotypes including concept, mechanism, regulation, and detection methods in Chinese academic writing.".
- Packaged executable path(s):
plus 1 additional script(s).scripts/example.py - Reference material available in
for task-specific guidance.references/ - Reusable packaged asset(s), including
.assets/example_asset.txt - Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
:Python
. Repository baseline for current packaged skills.3.10+
:Third-party packages
. Add pinned versions if this skill needs stricter environment control.not explicitly version-pinned in this skill package
Example Usage
cd "20260316/scientific-skills/Evidence Insight/phenotype-introduction" python -m py_compile scripts/example.py python scripts/example.py --help
Example run plan:
- Confirm the user input, output path, and any required config values.
- Edit the in-file
block or documented parameters if the script uses fixed settings.CONFIG - Run
with the validated inputs.python scripts/example.py - Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See
## Overview above for related details.
- Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
- Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
- Primary implementation surface:
with additional helper scripts underscripts/example.py
.scripts/ - Reference guidance:
contains supporting rules, prompts, or checklists.references/ - Packaged assets: reusable files are available under
.assets/ - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
- Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Overview
This skill generates detailed academic introductions for biomedical phenotypes with strict content requirements and word count constraints. It produces structured academic content with four mandatory sections:
- Concept
- Mechanism and occurrence process
- Regulation
- Marker detection methods
Quick Start
When a user requests a phenotype introduction:
- Parse the phenotype name from the user query.
- Generate Concept section (≥800 words) including definition, biological characteristics, cellular functions, and historical background.
- Generate Mechanism section (≥800 words) describing occurrence process, cellular impacts, and key molecular components.
- Generate Regulation section (≥800 words) covering regulatory principles, molecular pathways, and phenotype crosstalk.
- Generate Marker Detection section (≥500 words) listing ≥5 key markers with detection principles and methods.
- Format output using strict academic structure.
Content Requirements
1. Concept Section (≥800 words)
Include:
- Detailed phenotype definition
- Biological characteristics
- Cellular-level functions and roles
- Historical development and background
2. Mechanism Section (≥800 words)
Include:
- How the phenotype occurs
- Cellular impacts and downstream effects
- Key molecular components
- Step-by-step occurrence description
3. Regulation Section (≥800 words)
Include:
- Regulatory principles
- Participating molecules and signaling pathways
- Crosstalk with other phenotypes
- Nested or hierarchical relationships
4. Marker Detection Section (≥500 words, ≥5 markers)
Include:
- List of key marker molecules
- Detection rationale for each marker
- Common detection methods
Output Format
Strict academic structure:
1. Concept [Content ≥800 words] 2. Mechanism and Occurrence Process [Content ≥800 words] 3. Regulation Regulation: [Regulatory content] Phenotype Crosstalk: [Crosstalk content] 4. Markers and Detection Methods Molecule: [Marker name]; Principle: [Detection principle]; Methods: [Detection method] Molecule: [Marker name]; Principle: [Detection principle]; Methods: [Detection method] [Repeat for ≥5 markers]
Quality Control
All outputs must pass validation for:
- Word count per section
- Minimum 5 marker molecules
- Proper academic terminology
- Complete section coverage
- Logical scientific consistency