littlepeachs/naturepanelforge/codex-panel-reproduce

Codex Panel Reproduce

Trigger

Use this for code-only reproduction or refinement of one scientific figure panel in NaturePanelForge. The target is an editable Python script plus rendered PNG/PDF, not image editing or raster tracing.

For local user-supplied images, do not run Qwen scoring and do not require Qwen outputs. Qwen context is only optional metadata when the input already comes from an existing SciFigureHub/NaturePanelForge pipeline directory.

Inputs

• Single-image workflow: a target panel image path, optional caption, optional source PDF.
• Existing-panel workflow: a panel directory containing target.png; optional metadata.json, qwen_score.json, qwen_prompt.md, and raw_response.txt. Missing Qwen files are acceptable for user-supplied images.
• Refine workflow: an existing panel directory containing target.png, reproduce_panel.py, reproduce_panel.png, and reproduce_panel.pdf.

Reproduce One Panel

Run from the NaturePanelForge repo root:

python3 forge.py single-panel-image \
  --image path/to/target_panel.png \
  --out-root UserRuns/single_panel \
  --panel-id my_panel \
  --caption "brief visual/caption context" \
  --chart-type user_supplied \
  --review-rounds 4 \
  --skip-existing

This command prepares the local single-image bundle itself. It writes placeholder user-image metadata as needed; it does not classify the image with Qwen and does not need a local Qwen model.

For an existing panel directory:

python3 examples/prompt_codex_reproduce_fig02_g.py \
  --panel-dir path/to/panel_dir \
  --panel-root path/to/panel_root \
  --reviews-dir path/to/reviews_root \
  --specs-dir path/to/specs_root \
  --jobs 1 \
  --review-rounds 4 \
  --skip-existing

Expected panel outputs:

• reproduce_panel.py
• reproduce_panel.png
• reproduce_panel.pdf

Expected mirrored review/spec outputs:

• reproduce_panel_run_log.md
• reproduce_panel_review_notes.md
• reproduce_panel_review_summary.json
• reproduce_panel_prompt.md
• reproduce_panel_raw_response.txt

Dry Run

Use --dry-run before a live run to create/check task context and print the nested Codex prompt without running it:

python3 -m nature_panel_forge.reproduce_image --image path/to/target_panel.png --out-root UserRuns/dry_run --dry-run --print-command

For existing panel directories, add --dry-run to the batch reproduce or refine command.

Refine Existing Output

Use refinement only after a baseline reproduction exists:

python3 examples/prompt_codex_refine_reproduce.py \
  --panel-dir path/to/panel_dir \
  --panel-root path/to/panel_root \
  --reviews-dir path/to/refine_reviews_root \
  --specs-dir path/to/refine_specs_root \
  --jobs 1 \
  --review-rounds 4 \
  --skip-existing

Refine in place by editing reproduce_panel.py; do not create a competing script. Also write refine_complexity_assessment.json.

Review Loop

Each live run should alternate code-writing and review passes until close enough or --review-rounds is reached. The review must compare the saved PNG against target.png and record concrete findings about chart type, data pattern, axes, ticks, labels, legends/colorbars, annotations, colors, font/readability, edge visibility, and layout collisions.

If a review finds a fixable issue, edit the Python code and rerender. Do not modify target.png, target.pdf, metadata, score files, prompts, or raw source context.

Validation

Before reporting success, verify:

• python path/to/reproduce_panel.py exits successfully.
• The PNG and PDF outputs exist and are regenerated by the script.
• Review summary JSON has review_passed: true.
• Refine runs also have font_audit_passed, layout_audit_passed, and edge_visibility_passed set to true.
• The final response lists created files, final PNG size, iteration count, and the regeneration command.

Natural-Language Use

When the user asks in natural language, infer the CLI call and run the workflow. A good request looks like:

Use the codex-panel-reproduce skill to reproduce this scientific panel as editable Python/matplotlib code.
Target image: /path/to/target_panel.png
Optional PDF: /path/to/target_panel.pdf
Output root: UserRuns/my_panel
Panel id: my_panel
Chart type: bubble_plot
Caption: A short description of the visual structure, axes, legend, and data pattern.
Do not use Qwen scoring. Generate reproduce_panel.py, reproduce_panel.png, reproduce_panel.pdf, review notes, review summary, and a run log. Then report the output directory, review_passed, contract_passed, final PNG size, and rerender command.