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

Parameter	Type	Required	Description
mechanism_description	str	Yes	Text description of the mechanism
diagram_type	str	No	Type: "flowchart", "sequence", "state" (default: "flowchart")
direction	str	No	Flow direction: "TB", "LR", "RL", "BT"
style	str	No	Visual style: "default", "medical", "minimal"

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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of mechanism-flowchart 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:

mechanism-flowchart only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.