lookbook

Use when the user wants to curate a folder of images into a smaller training set — for character LoRAs, product LoRAs, style LoRAs, or any "pick K best from N candidates" image-selection task. Triggers on "curate these images", "pick K for LoRA training", "choose images for character LoRA", "filter my photo dump", "select diverse training images", "build a training set". Also use to drive lookbook over MCP/HTTP from an agent.

Use when developing or modifying the lookbook package — extending the curation pipeline, adding scorers/selectors/embedders, working with the manifest, swapping storage backends. Triggers on lookbook architecture questions, "how do I add a scorer", "where does the manifest live", "what's the registry pattern", or any work inside /Users/thorwhalen/Dropbox/py/proj/t/lookbook/.

Use after a lookbook curation run to interpret the result, explain why specific images were dropped or kept, query the manifest for provenance, or read cluster-coverage diagnoses. Triggers on "why was this image rejected", "what's missing from my training set", "explain the curation report", "this image was dropped — why", "what coverage gaps does my set have".

Use when the user wants to customize a lookbook curation pipeline beyond what the built-in recipes / profiles provide — overriding filter thresholds, swapping selectors, adjusting quotas, building a one-off recipe inline. Triggers on "different defaults", "custom scorer weights", "skip aesthetic scoring", "build my own pipeline", "tweak the funnel", "override the threshold", "what-if reweighting".

Use when working on lookbook's HTTP surface — adding a new endpoint, debugging a route, configuring the server (host/port/data-root), or wiring a frontend/agent client. Triggers on lookbook.http questions, "add an endpoint", "new HTTP route", "lookbook serve", "FastAPI app", or work in lookbook/http.py.

Use when adding a new subject profile to lookbook (product, scene, style, brand, font, …). A profile is a YAML file that bundles scorers, embedders, filters, a selector, and constraints into a named recipe. Triggers on "add a profile for X", "support product LoRAs", "scene profile", "style LoRA recipe", "new subject type", or work in lookbook/profiles/.

Use when adding a new selector to lookbook — wrapping an apricot submodular function, building a quota/constraint selector, integrating DPPy, or implementing a custom set-selection algorithm. Triggers on "add a selector", "wrap a submodular function", "quota-aware selection", "DPP selector", "custom set selection".

Use when adding a new per-image metric (scorer) to lookbook — wrapping a model from pyiqa/insightface/CLIP, adding technical metrics like noise or compression detection, or any "score one image" annotation producer. Triggers on "add a scorer", "wrap pyiqa model X", "add a metric for Y", "score images by Z".

Use when working on lookbook persistence — swapping storage backends (filesystem, SQLite, S3, Mongo, Redis), debugging the manifest, designing a new store codec, or wiring stores for tests. Triggers on questions about lookbook.store, get_stores(), the repository pattern, manifest persistence, or "where does lookbook save its data".