bears workflows

Goal

Critical Rule

Experiment Capabilities and When to Use

Colour Mixing Optimization (

colour-mixing-opt

)

• Automated liquid handling on Opentrons OT-2 to mix R, G, B dye volumes
• Camera capture of mixed colour after each dispensing step
• VLM-based image processing and ROI extraction for per-well RGB measurement
• RMSE calculation between mixed and target colour
• Bayesian Optimization (BO) or LLM-driven suggestion of next volume ratios
• Iterative protocol generation and execution until stop condition is reached
• Per-iteration report generation (volumes, RGB, RMSE, next suggestion)

• The user wants to mix colours to match a target RGB
• The task involves optimizing volume ratios of dyes to minimize colour error
• The user mentions colour mixing, RMSE, BO, or LLM-guided liquid handling

scripts/run_colour_mixing.py

• End-to-end experiment runner; edit the config block at the top to change parameters
• Set

  ROBOT_IP

  to the OT-2 IP address in

  .env

  for fully automated protocol execution via HTTP API
• Set

  OPENROUTER_API_KEY

  environment variable before running
• Outputs: generated protocols in

  protocols/

  , corrected images in

  images/

  , live report in

  reports/report.md

• Refer to: colour-mixing-opt
• See optimization details: optimization.md
• See image processing details: image-processing.md
• Optimizer classes: scripts/optimizers.py
• RMSE utility: scripts/rmse.py
• Image processing pipeline: scripts/image_processing.py

Viscosity Optimization (

viscosity-optimization

)

• Automated protocol generation and execution on Opentrons OT-2
• Concurrent gravimetric data collection from a mass balance (4 Hz) during each run
• Automatic data processing: outlier removal, phase slicing, normalisation
• Transfer error calculation (signed and absolute, in µL)
• Bayesian Optimization (LCB or EI) or LLM-driven suggestion of next protocol parameters
• Optimizable parameters: flow rates, delays, aspirate/dispense offsets
• Per-iteration report generation (params, signed error, absolute error)

• The user wants to improve pipetting accuracy for viscous or non-water liquids
• The task involves tuning flow rate, delay, or offset parameters to minimize transfer error
• The user mentions gravimetric calibration, balance feedback, or viscosity optimization
• The user mentions BO, LCB, EI, or LLM-guided pipetting parameter optimization

• Refer to: viscosity-optimization

Selection Workflow

1. Parse user intent and identify the experiment type.
2. Match intent to the experiment capabilities above.
3. If experiment selection is unclear or ambiguous, ask the user and wait for confirmation.
4. Load the corresponding reference file.
5. Proceed with the experiment workflow only after the experiment is confirmed.

Output Guidance

• State the recommended experiment and a one-line reason tied to its capability.
• If uncertain, ask a direct clarification question instead of guessing.

Critical Rules

1. Always ask for all required inputs (target colour, thresholds, limits, deck layout) before starting any experiment.
2. Ask the user for the OT-2 robot IP address before running, and set it as

   ROBOT_IP

   in

   .env

   .
3. Ask the user for the OpenRouter API key if not already set in the environment.
4. Invoke puda-memory after every protocol creation and run to keep

   experiment.md

   current.
5. Opentrons protocols must always end with no tip attached to any pipette.
6. Ask user if unsure — do not assume.