BioMed Skill Creator

A meta-skill for creating and improving skills in the OpenBioMed biomedical toolkit.

Overview

This skill guides you through creating biomedical skills with interactive validation. Biomedical workflows require domain-specific validation with real data before finalization.

Workflow:

1. Capture Intent → 2. Design Workflow → 3. Interactive Validation → 4. Finalize → 5. Evaluate
        ↑                                                              ↓
        └──────────────────── Iterate if needed ←─────────────────────┘

Step 1: Capture Intent

Ask clarifying questions:

1. What biomedical task should this skill perform?
2. What inputs will users provide? (molecule name/SMILES, protein ID, text)
3. What outputs should the skill produce? (reports, files, predictions, visualizations)
4. Are there edge cases or constraints?

Input Types

Step 2: Design Workflow

Identify tools and steps. See references/tools_reference.md for available tools.

Common Workflow Patterns

Basic Workflow Code Pattern

from open_biomed.tools.tool_registry import TOOLS

# Get entity
tool = TOOLS["tool_name"]
result, message = tool.run(parameter=value)
entity = result.get("protein") or result.get("molecule")

# Process with other tools
another_tool = TOOLS["another_tool"]
output, msg = another_tool.run(entity=entity)

Step 3: Interactive Validation (CRITICAL)

Execute ONE step at a time and check with user before proceeding.

After designing the workflow, ask:

"Please provide an example input and I'll run through each step showing results."

For Each Step

1. Execute the step using OpenBioMed tools
2. Display results with standardized format (see references/validation_template.md)
3. Ask for feedback: "Is this result satisfactory? (yes/proceed/modify/skip)"

Handling Errors

When a step fails:

1. Explain the error clearly
2. Propose alternatives (fallback tools, web search, skip)
3. Ask user to decide

After All Steps

Present summary and ask:

"Do you want to:

1. Proceed with this workflow?
2. Modify and re-validate?
3. Try different input?"

Step 4: Finalize the Skill

Once approved, create the skill files:

Directory Structure

skill-name/
├── SKILL.md              # Main skill definition (< 200 lines)
├── examples/             # Runnable example scripts
│   └── basic_example.py
└── references/           # Detailed documentation
    ├── advanced.md
    └── troubleshooting.md

SKILL.md Template

See references/skill_template.md for the full structure. Key sections:

---
name: skill-name
description: >
  [One-line summary of what the skill does].
  Use this skill when:
  (1) [Use case 1],
  (2) [Use case 2],
  (3) [Use case 3].
license: [MIT|Apache-2.0|BSD-3-Clause|GPL-3.0]
category: [category from list below]
tags: [tag1, tag2, tag3]
---

# Skill Title

## When to Use
## Workflow (keep code snippets < 20 lines)
## Expected Outputs
## Error Handling

License Selection

Before finalizing SKILL.md, ask the user to choose a license:

"What license should this skill use?

1. MIT (Recommended) - Permissive, allows commercial use
2. Apache-2.0 - Permissive with patent grant
3. BSD-3-Clause - Permissive, no endorsement clause
4. GPL-3.0 - Copyleft, derivatives must be open source"

Default to MIT if user doesn't specify.

Category Options

Writing Guidelines

1. Keep SKILL.md under 200 lines - Move long code to examples/ or references/
2. Code snippets < 20 lines - Link to full examples
3. Include interpretation - What do scores/outputs mean?
4. Handle errors - What if tools/APIs fail?

Step 5: Evaluate the Skill

Run evaluation to ensure quality. See references/evaluation_reference.md for details.

1. Create 2-3 test cases with realistic prompts
2. Run grader - Compare with-skill vs baseline agents
3. Analyze results - Identify patterns and issues
4. Iterate if needed

Quick Reference

See references/quick_reference.md for:

• Workflow patterns summary
• Input type reference
• Score interpretation tables
• Evaluation checklist

Communication Style

Adapt to user's familiarity:

• Expert: Use technical terms (ADMET, TPSA, RMSD)
• Intermediate: Brief explanations
• Beginner: Analogies, explain why metrics matter

Checklist

Before finalizing:

• Workflow validated with real input
• User approved the workflow
• SKILL.md under 200 lines
• Long code in examples/
• Error handling documented
• Test cases created and graded