| name | annotate-figure |
| description | Produce a labeled figure with PERFECT text — render a label-free image locally (or take an external image) then overlay leader lines and typeset labels programmatically from vision-derived anchor coordinates. Text is correct by construction; only pointer placement is reviewed. |
| allowed-tools | Read, Bash(python3 *), Bash(ls *), Task |
Annotate Figure
Split the work between what image models do well (drawing scenes and objects) and what code does perfectly (text and lines). Never ask a diffusion model to render the labels.
Do not Read PNG/JPG/image files directly — all image inspection goes through the jack-tar-deckhand:image-reviewer or general-purpose subagent (issue #76 discipline).
Inputs
- Either
--image <path> — an external image supplied by the operator (photo, screenshot, existing diagram; no generation, no spend), or a scene description to render locally.
- A label list: the exact strings to place (e.g.,
Mast, Sail, Hull, Rudder, Bow, Stern) and, per label, what it should point at.
Procedure
1. Source the base image
- External image: use the given path as-is. Record
source: external.
- Generate: transform the prompt to be LABEL-FREE — remove every quoted label directive and any "labelled/labeled X" phrasing, describe the parts that must be visible instead ("rudder visible at the rear below the waterline"), and append:
No text, no labels, no leader lines, no annotations of any kind. Render via the standard local draft ladder (local-config.json model preferences; klein/z-image per the catalog local_draft role) at 1024×576 unless told otherwise. $0; the F10 gate is untouched (no paid tier is involved unless the operator later escalates the BASE image).
2. Anchor pass (vision → structured JSON)
Dispatch jack-tar-deckhand:image-reviewer (or general-purpose for complex scenes) with the image path and this contract — labels filled in from the request:
Return NORMALIZED coordinates (x, y as fractions of width/height, 0–1, origin top-left) for the exact point a leader line should TOUCH for each of: : , … Also give a one-line description of the depicted subject and any orientation facts needed to sanity-check (e.g. which side is the front). Output ONLY JSON: {"description": "...", "anchors": {"<Label>": [x, y], ...}}
Validate the response with annotate_figure.validate_anchors (module below); on validation failure, re-dispatch once with the error message included.
3. Overlay (perfect text, deterministic)
python3 - <<'PY'
import sys; sys.path.insert(0, '<PLUGIN_ROOT>/src')
from annotate_figure import annotate
annotate('<base image>', {<label>: [x, y], ...}, '<out path>')
PY
Automatic occlusion-avoiding placement puts each label in the nearest margin band; pass explicit {"anchor": [...], "label_pos": [...]} per label only when the reviewer flags a placement problem.
4. Anchor-verification review (pointers only — text is axiomatic)
Dispatch the reviewer on the ANNOTATED image: "For each labeled leader line, does it touch the anatomically/semantically correct part? Leader lines are dark with a white casing halo — over dark image regions look for the casing, and zoom before declaring a leader absent. Do not review spelling (programmatic). Flag any label box occluding important content and any stray text baked into the base image." One refine loop allowed: re-query coordinates for mispointed labels (include the reviewer's correction hints), re-run the overlay. More than one failed loop → surface to the operator with the best attempt (F12 stance: reviewer verdicts advisory; the operator certifies).
5. Deliver
Report: output path, source (external | generated + model + seed), anchor JSON used, review verdict. In deck context, hand the annotated PNG to the assembler like any figure asset.
Why this exists (evidence)
The 2026-07-17 benchmark (docs/spikes/2026-07-17-mlx-model-benchmark/) showed exact labels + pointer placement is the universal weakness — best local 5.8/10, cloud ceiling 8.7/10 on technical figures. This flow's PoC scored a blind 10/10 on the same rubric at $0: spelling cannot fail, and pointer accuracy became a vision-coordinate problem with a verification loop. External-image support extends the pipeline to imagery it did not generate.
