Mechanism Flowchart

Source: https://github.com/aipoch/medical-research-skills

Mechanism Flowchart

Generates Mermaid flowchart code and visual representations of medical mechanisms, pathophysiology, and drug action pathways.

When to Use

• Use this skill when the task needs Generates Mermaid flowchart code and visual diagrams for pathophysiological.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Generates Mermaid flowchart code and visual diagrams for pathophysiological.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• enum: unspecified. Declared in requirements.txt.

Example Usage

from mechanism_flowchart import MechanismDiagram

diagram = MechanismDiagram()
result = diagram.generate(
    "Type 2 Diabetes: Insulin resistance leads to hyperglycemia, "
    "causing beta cell dysfunction and further glucose elevation"
)
print(result['mermaid_code'])

Implementation Details

See ## Workflow 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/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• 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.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Automatic flowchart generation from text descriptions
• Multiple diagram types (flowchart, sequence, state)
• Customizable styling for publication
• Support for complex branching logic
• Export to multiple formats

Use Cases

• Creating educational diagrams for presentations
• Visualizing drug mechanism of action
• Illustrating disease pathways
• Thesis and publication figure preparation

Input Parameters

Output Format

{
  "mermaid_code": "string",
  "diagram_type": "string",
  "nodes": ["string"],
  "edges": ["string"],
  "rendered_svg": "string (optional)"
}

Sample Output

flowchart TB
    A[Insulin Resistance] --> B[Hyperglycemia]
    B --> C[Beta Cell Dysfunction]
    C --> D[Worsening Glucose Control]
    B --> D

Limitations

• Requires Mermaid renderer for visualization
• Complex mechanisms may need manual refinement
• Limited to Mermaid-supported diagram types

Risk Assessment