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figure-composer

Compose one publication-grade multi-panel figure. Entry from a one-line claim + data files, OR from an existing figure via `derive_outline_prompt` (you read the PNG). Runs a per-figure loop: outline (12-col grid, per-panel ask + label_budget) → render each panel with `panel_task` (loading `figure-style`), one at a time or parallelized → tile + stamp letters with `compose_figure` → adversarial composite self-review with two-tier feedback (Tier-1 outline_revisions / Tier-2 per-panel violations) → regen affected panels, ≤3 rounds. Helpers: panel_task / compose_figure / compose_crops / composite_review_task / derive_outline_prompt. For one standalone plot use `figure-style`; for whole-paper figure ordering use `paper-narrative`.

configs/microservice/bff-service/configs/agent-skills/claude-science/figure-composer/SKILL.md

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Nov 6, 2026
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Apache-2.0

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# Figure Composer — narrative → panels → compose → adversarial loop

Compose ONE publication-grade multi-panel figure: turn a one-sentence claim plus
data files into an outline, render each panel, tile them into a composite, and
harden it through an adversarial self-review loop.

## Setup (any agent, no API key)
This is a **pure skill** — `kernel.py` is deterministic Python (PIL geometry plus
schema/prompt builders) and *you* (the base model) do all the reasoning:
reverse-engineering an outline from a figure, rendering panels, and the
adversarial composite review. There is no `host` runtime and no LLM API. Load
the helpers once per session in a Python cell:
```python