| name | nanodevice_flakedetect_review |
| description | Agent review protocol for validating committed flake polygons in KLayout. Use after the commit step to visually inspect polygons against the microscope image, verify contour quality, and decide pass/fail/retry. |
nanodevice_flakedetect_review — Visual Validation Protocol
Pure agent workflow using existing skills and MCP tools. No custom scripts needed.
When to Use
After the commit step has inserted polygons into KLayout. Review validates the actual committed result, not just overlay images.
Prerequisites
- KLayout running with committed polygons (from the commit step)
- Background microscope image loaded (from the commit step)
- Combine output available:
<out>/combine/overlay_raw.png, overlay_lut.png, combine_report.json
- Alignment report available:
<out>/align/alignment_report.json
Review Protocol
Step 1: Screenshot the committed layout
Use MCP screenshot to capture the current KLayout viewport with polygons on the background image:
mcp__klayoutclaw__screenshot
Read the resulting PNG to see all layers overlaid on the microscope photo.
Step 2: Inspect individual layers
Use the display skill to isolate each material layer for focused inspection:
python skills/display/scripts/show_only.py 10/0
python skills/display/scripts/show_only.py 11/0
python skills/display/scripts/show_only.py 12/0
python skills/display/scripts/show_only.py 13/0
python skills/display/scripts/toggle_layer.py 10/0 11/0 12/0 13/0 --on
Take a screenshot after each show_only to compare each polygon against the flake features.
Step 3: Compare with combine overlays
Read the overlay images from the combine step for cross-reference:
<out>/combine/overlay_raw.png — contours on desaturated raw image<out>/combine/overlay_lut.png — contours on LUT image (if available)<out>/combine/mask_composite.png — color-coded mask overlay
These show the contours before coordinate transform. The committed KLayout polygons should match.
Step 4: Structured visual assessment
Answer these specific questions about the KLayout screenshots:
Q1 — Top hBN boundary:
"Do the top_hBN polygons (layer 10/0) follow the flake boundary visible in the background image?"
Rate: tight fit / acceptable / poor fit
Q2 — Graphene containment:
"Is the graphene polygon (layer 11/0) fully inside the top_hBN polygon (layer 10/0)? Any graphene outside top_hBN indicates a problem."
Rate: fully contained / mostly contained / significant leakage
Q3 — Graphite alignment:
"Does the graphite polygon (layer 13/0) align with the dark strip visible in the image?"
Rate: aligned / offset / missing
Q4 — Bottom hBN coverage:
"Does the bottom_hBN polygon (layer 12/0) cover the correct underlying hBN region?"
Rate: good / partial / wrong region
Q5 — Overall:
"Rate this result: excellent / good / needs work."
Step 5: Check quantitative metrics
Read the reports for numeric cross-checks:
From <out>/align/alignment_report.json:
- Forward Chamfer < 3 um → good, > 5 um → investigate
- IoU > 0.7 → good, < 0.5 → poor
- Outside fraction < 0.1 → good, > 0.2 → poor
From <out>/combine/combine_report.json:
- ECC correlation > 0.9 → raw/LUT registration is reliable
- Transform summary confirms correct warp applied per material
Step 6: Decision
| Visual Assessment | Metrics | Decision |
|---|
| excellent/good | metrics OK | PASS — detection complete |
| good | metrics borderline | PASS with note |
| needs work (alignment) | high chamfer / low IoU | FAIL → re-run align with different rotation or parameters, then redo combine → commit → review |
| needs work (detection) | — | FAIL → re-run specific detect script with adjusted parameters, then redo combine → commit → review |
| needs work (commit) | — | FAIL → re-run commit (coordinate transform or layer error) |
Step 7: Document
Record the decision and reasoning. If FAIL, specify which step to retry and what to change.
Vision Feedback Principles
- Structured prompts, not open-ended. Specific questions get reliable answers.
- Show raw image as context. The background image provides ground truth.
- Budget calls. Max ~10 vision calls per review cycle.
- Vision for coarse judgment, metrics for precision. Vision distinguishes "completely wrong" from "roughly right." Use numeric metrics for fine distinctions.
- Source images as ground truth. Compare against the original microscope photos when debugging.
Retry Guidelines
- Alignment retry: Try a different rotation from the sweep candidates, or widen the scale range.
- Detection retry: Adjust thresholds or use
--cluster-id overrides. Check source image quality.
- Max retries: 2 per stage. If still failing after 2 retries, flag for manual intervention.
