Skill Index

InnoClaw/

protocol-generation-from-description

community[skill]

Generate detailed laboratory protocols from natural language descriptions using AI, producing step-by-step experimental procedures ready for lab execution.

$/plugin install InnoClaw

details

Laboratory Protocol Generation Workflow

Usage

1. MCP Server Definition

Use the same DrugSDAClient class pattern with Thoth-Plan server.

2. Protocol Generation from User Description

This workflow generates detailed laboratory protocols from natural language descriptions, useful for experimental planning and automation.

Workflow Steps:

  1. Input User Description - Provide natural language description of desired protocol
  2. Generate Detailed Protocol - AI generates step-by-step experimental procedure
  3. Optional: Convert to Executable Format - Transform protocol to machine-readable JSON for automation

Implementation:

client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/19/Thoth-Plan")
if not await client.connect():
    print("connection failed")
    return

## Step 1: Provide protocol description
user_prompt = """
I need a PCR protocol for amplifying a 500bp DNA fragment.
Use a standard Taq polymerase with the following conditions:
- Annealing temperature: 55°C
- Extension time: 30 seconds
- 30 cycles total
Include primer concentrations and buffer composition.
"""

## Step 2: Generate detailed protocol
result = await client.session.call_tool(
    "protocol_generation",
    arguments={
        "user_prompt": user_prompt
    }
)

protocol_text = client.parse_result(result)["protocol"]

print("Generated Protocol:")
print("=" * 80)
print(protocol_text)
print("=" * 80)

## Step 3 (Optional): Convert to executable JSON for lab automation
result = await client.session.call_tool(
    "generate_executable_json",
    arguments={
        "protocol": protocol_text
    }
)

executable_json = client.parse_result(result)["executable_json"]
print("\nExecutable JSON for lab automation:")
print(executable_json)

## Step 4 (Optional): Execute protocol via lab automation system
result = await client.session.call_tool(
    "execute_json",
    arguments={
        "executable_json": executable_json
    }
)

execution_info = client.parse_result(result)
print("\nExecution Info:")
print(execution_info)

await client.disconnect()

Tool Descriptions

Thoth-Plan Server:

  • protocol_generation: Generate detailed laboratory protocol from description

    • Args: user_prompt (str) - Natural language description of desired protocol
    • Returns: protocol (str) - Detailed step-by-step protocol text
  • generate_executable_json: Convert protocol text to machine-readable format

    • Args: protocol (str) - Protocol text
    • Returns: executable_json (str) - JSON format for Opentrons/lab automation
  • execute_json: Execute protocol via connected lab automation systems

    • Args: executable_json (str) - Executable protocol JSON
    • Returns: Execution status and log

Input/Output

Input:

  • user_prompt: Natural language description of desired experimental protocol
    • Can include: reagents, conditions, equipment, expected outcomes
    • Can reference standard protocols or specific parameters

Output:

  • protocol: Detailed step-by-step protocol including:
    • Materials and reagents list
    • Equipment requirements
    • Detailed procedure steps
    • Safety considerations
    • Expected results
    • Troubleshooting tips

Example Protocol Types

The system can generate protocols for various laboratory procedures:

  • Molecular Biology: PCR, cloning, gel electrophoresis, DNA extraction, transformation
  • Protein Science: Protein purification, Western blot, ELISA, protein crystallization
  • Cell Culture: Cell passage, transfection, differentiation, cryopreservation
  • Biochemistry: Enzyme assays, metabolite extraction, chromatography
  • Analytical: Spectroscopy, mass spectrometry sample prep, HPLC

Protocol Quality Guidelines

Generated protocols include:

  • ✓ Precise volumes and concentrations
  • ✓ Specific temperatures and times
  • ✓ Safety warnings where applicable
  • ✓ Quality control checkpoints
  • ✓ Troubleshooting guidance

Integration with Lab Automation

The generated protocols can be converted to executable JSON format compatible with:

  • Opentrons liquid handling robots
  • Hamilton automated workstations
  • Custom lab automation systems
  • Electronic lab notebooks (ELNs)

Best Practices

For optimal protocol generation:

  1. Be Specific: Include target specifications (e.g., "500bp fragment", "55°C annealing")
  2. Mention Equipment: Specify if using particular instruments or kits
  3. State Goals: Describe the experimental objective
  4. Include Constraints: Note any limitations (time, budget, available reagents)
  5. Reference Standards: Mention if following particular methods or publications

Example Good Prompts:

"Generate a Western blot protocol for detecting GAPDH (37 kDa) in HEK293 cell lysates using a standard semi-dry transfer system"

"I need a DNA extraction protocol from plant tissue (Arabidopsis leaves) optimized for downstream PCR. Yield target is 50 µg from 100mg tissue"

"Create a protein purification protocol for His-tagged recombinant protein from E. coli using IMAC chromatography. Starting culture volume is 500mL"

Limitations

  • Generated protocols should be reviewed by qualified personnel before execution
  • May require adjustment based on specific lab equipment and reagents
  • Safety protocols should be verified against institutional guidelines
  • Novel or untested procedures may need optimization

technical

github
SpectrAI-Initiative/InnoClaw
stars
374
license
Apache-2.0
contributors
16
last commit
2026-04-20T01:27:21Z
file
.claude/skills/protocol-generation/SKILL.md

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