cp-prepare-segmentation-inputs

cp-prepare-segmentation-inputs resolves which image fields will enter segmentation.

It is the planning step before any CellProfiler segmentation run begins.

Purpose

Use this skill when you want:

  • confirm which fields will be sent into segmentation

  • inspect the segmentation input table before a longer run

  • hand a clean field list to cp-extract-segmentation-artifacts

Main Outcome

After this skill finishes, you know exactly which image fields will go into segmentation.

This is a planning step. It does not produce masks, outlines, or single-cell crops yet.

Inputs

This skill reads:

  • a project config such as configs/project_config.demo.json

  • the configured raw-image and illumination roots

  • optional plate, well, or site filters

  • an optional output directory

Outputs

  • load_data_for_segmentation.csv The field-level segmentation input table.

  • pipeline_skill_manifest.json The run record for this skill invocation.

Agent Demo

This page is based on a real local OpenClaw turn against the repository demo config:

  • session id: segprepare-rerun-v2

  • recorded on 2026-05-06

  • model: vibe/gpt-5-mini

Request

Before I segment the demo Cell Painting images, I want to confirm exactly which wells and image fields will be included in this run. Please use the demo project config, prepare the segmentation input table, write it under demo/workspace/outputs/agent_demo_segmentation/01_prepare_inputs_rerun_v2, and summarize the wells and fields that will be processed.

Structured Trace

user_input:
Before I segment the demo Cell Painting images, I want to confirm exactly which wells and image fields will be included in this run. Please use the demo project config, prepare the segmentation input table, write it under demo/workspace/outputs/agent_demo_segmentation/01_prepare_inputs_rerun_v2, and summarize the wells and fields that will be processed.

recorded_turn:
- the saved OpenClaw turn answered this request directly
- the reply identified the segmentation-input step used
- the reply reported the output path of the load-data table
- the reply summarized the number of wells and fields included
- the reply listed the exact well/site combinations

final_answer_snapshot:
The demo segmentation run includes two image fields: BR00000001 / A01 / site 1 and BR00000001 / A02 / site 1.

Observed Result

The recorded run wrote:

  • load_data_for_segmentation.csv

  • pipeline_skill_manifest.json

The resolved segmentation scope covered:

  • 1 plate

  • 2 wells

  • 2 image fields

The included fields were:

  • BR00000001 / A01 / site 1

  • BR00000001 / A02 / site 1

Recorded Reply

Done — I used the stable step **`cp-prepare-segmentation-inputs`** with the demo project config and wrote the segmentation input table under:

- `demo/workspace/outputs/agent_demo_segmentation/01_prepare_inputs_rerun_v2`

### Output file
- `demo/workspace/outputs/agent_demo_segmentation/01_prepare_inputs_rerun_v2/load_data_for_segmentation.csv`

### What will be processed
This prepared segmentation run includes:

- **1 plate**
- **2 wells**
- **2 image fields (rows in the load-data table)**

Next Skills