/figmirror

name: figmirror
description: This FigMirror skill should be used when the user asks to "mirror this figure's style", "copy this figure's style", "make a chart that looks like this paper", "reproduce this figure with my data", "match this paper's aesthetic", "I want a NeurIPS-quality version of this", or any variant where they hand over a cropped or uncropped reference figure AND their own data and want their data rendered in the same visual register. ALSO triggers when the user attaches a paper-figure screenshot plus tabular data and asks for matplotlib output. Does NOT trigger on generic matplotlib chart requests with no reference image — that's a basic matplotlib task, not style transfer.

FigMirror (`figmirror`)

Transfer the visual style of a top-conference paper figure (NeurIPS / ICML / ICLR / Nature / Science) onto user data via an iterative Drawer / Reviewer loop. Output is a self-contained matplotlib script + PNG + type-42 PDF that matches the reference's STYLE — not its data.

When to use

Trigger when the user provides all three:

• A reference paper-figure screenshot.
• Their own data in any parseable form (pasted table, CSV, TSV, markdown

table, or dirty terminal text).

• An expectation that the output should look like the reference — even via

casual phrasing ("make this chart but with my numbers", "redo this in matplotlib"). This includes 3D references when the reference or data is actually 3D.

Do not trigger on plain matplotlib chart requests with no reference image.

Required inputs

• Reference image (PNG / JPG), cropped or uncropped. It may include margins,

captions, neighboring panels, or page text; Stage 0 preprocesses it.

• User data (any parseable form).
• Optional working directory. Default: `<cwd>/figmirror-runs/<run-id>/`.

3D Insert Gate

Enable `references/three-d-prompting.md` only when the user asks for a 3D figure, the reference is visibly 3D, or the parsed data requires a 3D encoding such as `x/y/z`, surfaces, trajectories, layered profiles, closed objects, 3D small multiples, 3D bars, or plane projections. Do not use this insert to turn an ordinary 2D task into 3D.

Architecture

Three bundled subagents drive the loop; the caller orchestrates from the main thread:

• `figure-preprocessor` — **Preprocessor**. Stage 0: preserves the raw upload,

crops away margins/captions/page text/neighboring panels when safe, and writes `inputs/reference_clean.png` plus a crop check/report.

• `figure-illustrator` — **Drawer**. Per iter: reads reference + data + L2

library, produces `figure_iter<N>.py`, `img_iter<N>.png`, `notes_iter<N>.md`, `floor_selfcheck_iter<N>.txt`. Self-checks the layout floor before returning.

• `figure-critic` — **Reviewer**. Per iter: vision-only audit on a

fresh-context view (reference + draft + L2 library + optional 3D insert + prior audit only). Returns ONE strict JSON object per the review schema.

Subagents are stateless across dispatch; iter-to-iter state flows through workdir files.

Prefer `subagent_type: figure-preprocessor` / `figure-illustrator` / `figure-critic`. Fallback path when those names don't resolve: see `references/iter-loop-spec.md` § "Subagent dispatch fallback".

Workflow

For each run:

1. **Stage workdir.** Pre-create every directory the loop will write into (subagents Write into existing dirs only — workspace permission quirk). Stage the uploaded reference image to `inputs/reference_raw.png` and also to `inputs/reference_clean.png` as a temporary first-paint copy; stage parsed data to `inputs/data.txt`, and the L2 library to `inputs/aesthetic-library.md`; stage the 3D router plus `references/three-d/` only when the 3D insert gate is enabled. The router selects exactly one mode file: `three-d/style-transfer.md` for ordinary user-data figures, or `three-d/strict-reproduction.md` for reproduction, comparison, or candidate/control replacement. For strict 3D reproduction runs that need quantitative candidate diagnosis, also stage the optional candidate scorer. The top-level Orchestrator owns final selection and must run the selected mode's rendered-image gates before copying any candidate to the final figure. 2. **Preprocess reference.** Dispatch `figure-preprocessor` before data-gen, Drawer, or Reviewer. It writes the clean L1 anchor to `inputs/reference_clean.png` and records the before/after crop check. 3. **Echo data parse to user (Decision-7).** Show parsed shape (rows × cols, columns, NaN cells, sample row); proceed when confirmed, or skip if the user pre-authorized. Either way, persist the echo to `data_echo.md`. 4. **Iterate** with the caller-provided `max_iters`; default to 6 when the caller gives no explicit limit. If the caller enables auto-until-shipped, ignore `max_iters` and continue until `ship` or a real blocker. Each iter:

• Dispatch the Drawer.
• Stage the Reviewer's audit view (reference + new draft + L2 library +

optional 3D insert + prior audit only — NEVER `data.txt` or drawer notes).

• Dispatch the Reviewer.
• Parse the audit JSON.
• Apply the decision rule:

`floor.passed && verdict == "ship"` → ship and break; else `N == max_iters - 1` and not auto → break (fall through to select-best); else continue. 5. **Select-best fallback** (only if `ship` never fires). Pick the lowest-drift iter among `floor.passed && verdict == "close"` candidates. Document the choice in `selection.md`. 6. **Write canonical artifacts.** Copy the chosen-iter script + PNG to `figure.py` / `figure.png`. Re-render `figure.pdf` with `pdf.fonttype = 42`. 7. **Surface the result to the user.** Render `figure.png` inline, list paths to `figure.py` / `figure.pdf`, give a 1-2 sentence trajectory summary. Do not show audit JSONs or per-iter scripts unless asked.

The full per-step spec (bash commands for staging, dispatch brief templates, audit JSON parsing snippets, drift calcu