Conciseness

The body is dense and operational throughout — input tables, a failure-modes table, preflight checks — with zero padding on concepts Claude already knows, fitting anchor 4 rather than anchor 5. The public-URL-for-refs rule, proxy-routing/billing note, and credential warning are each repeated across Inputs, Workflow, and Failure Modes, which could be consolidated into one authoritative statement to trim tokens.

Actionability

Guidance is fully executable: a copy-paste preflight bash snippet, three complete `python3 .../generate.py` invocations covering the common cases (default text-to-image, edit-from-reference, custom-size gpt-image-2), a "What the script does" breakdown, and a symptom→cause→fix table. It matches anchor 5 — the commands cover the common cases; the only placeholders are the deliberately abbreviated `.../generate.py` path and `"..."` prompt text, which are explicit substitutions, not missing detail.

Workflow Clarity

The sequence is explicit and validated: Preflight (credential and dependency checks with a failure message and remediation "run: gooseworks login"), then the Workflow commands, then a Quality Checks checklist (file exists and > 1 KB, meta.json field verification, dimension match for custom sizes), with the Failure Modes table supplying error-recovery feedback loops. This matches anchor 5; no destructive or batch operation applies, so no cap is triggered.

Progressive Disclosure

Structure is clear and navigable with well-labeled sections, and every referenced bundle path is real (scripts/generate.py, scripts/media_proxy.py, scripts/fal_helpers.py — the legacy-helper status it describes matches the actual imports in generate.py). It fits anchor 4 rather than 5 because everything lives inline in one ~125-line file: dated pricing detail and the extensive failure-modes/parity sections could be split into a reference file, leaving a leaner overview, though references that do exist are one level deep and clearly signaled.

Specificity

Concrete actions are explicit: "Generate a single photoreal or designed image with OpenAI gpt-image via fal.ai", "Routes to text-to-image or the edit variant depending on whether a reference image is provided", and model-specific size behavior ("custom output sizes up to 3840px"). It stays below anchor 5 because all of these are facets of one generation capability rather than multiple distinct actions like extract/fill/merge/convert, and it sits above anchor 3 because several specific behaviors (two models, routing rule, size support) are named, not just 1-2 generic actions.

Completeness

Both questions are answered explicitly and in third person: the what is "Generate a single photoreal or designed image with OpenAI gpt-image via fal.ai" (with model variants and routing detail), and the when is a concrete "Use for photoreal character anchors, scene keyframes, and designed sheets (e.g. storyboards) where precise layout and legible text matter" clause. This matches anchor 5 exactly; nothing is left merely implied.

Trigger Term Quality

Natural terms users would say are present: "photoreal", "image", "character anchors", "scene keyframes", "storyboards", "designed sheets", "gpt-image", plus model ids. It fits anchor 4 (good coverage, a few natural terms missing) rather than anchor 5 because common synonyms like "picture", "render", "illustration", or generic "image generation" phrasing are absent.

Distinctiveness Conflict Risk

The niche is tightly scoped — OpenAI gpt-image specifically via fal.ai, with explicit model ids ("gpt-image-1", "gpt-image-2") and an explicit boundary against the sibling path ("the FAL fallback for Higgsfield's `gpt_image_2`"). Triggers like "photoreal character anchors" and "designed sheets" are distinctive of this exact capability, matching anchor 5's clear niche with minimal conflict risk.