bears-workflows

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Discover PUDA experiment workflows for bears and choose the right experiment for the task. Use when you need to run, set up, or understand a PUDA experiment such as colour mixing optimization.

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Sourcepudap/bears-skills
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NPX Install

npx skill4agent add pudap/bears-skills bears-workflows

Tags

Translated version includes tags in frontmatter
puda-experimentscolour-mixing-optimizationviscosity-optimizationllm-guided-workflowsbayesian-optimization

SKILL.md Content

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bears workflows

Goal

Provide experiment-selection and workflow guidance for PUDA workflows at bears, then load the correct experiment reference before execution.

Critical Rule

If you are unsure which experiment matches the user's task, ask the user before proceeding.
Do not assume.

Experiment Capabilities and When to Use

Colour Mixing Optimization (
colour-mixing-opt
)

Use for iterative RGB colour mixing to match a target colour via RMSE minimization.
Capabilities:
  • 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)
Use this experiment when:
  • 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
Runner script: 
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
Before running:
  • 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
)

Use for iterative tuning of Opentrons OT-2 liquid handling parameters for viscous fluids using gravimetric feedback.
Capabilities:
  • 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)
Use this experiment when:
  • 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
Before running:
  • 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

When answering experiment-selection questions:
  • 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.