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.
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 scripts/example.py is the most direct path to complete the request.
Use this skill when you need the phenotype-introduction package behavior rather than a generic answer.
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).
相关技能
scripts/example.py
Reference material available in references/ for task-specific guidance.
Reusable packaged asset(s), including assets/example_asset.txt.
Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
Python: 3.10+. Repository baseline for current packaged skills.
Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.
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 CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/example.py with the validated inputs.
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: scripts/example.py with additional helper scripts under scripts/.
Reference guidance: references/ contains supporting rules, prompts, or checklists.
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.