name	latent-scope
description	Use Latent Scope to turn a user's dataset (text or images) into an explorable latent-space map — embed, project to 2D, cluster, label, and serve an interactive scatter. Invoke when a user wants to explore, cluster, visualize, or search the structure of a corpus/table/folder of images, or asks to "run latent scope" / "map my data" / "show me the clusters".

Latent Scope

The full runbook is AGENTS.md at the repo root — read it. It's the cross-provider source of truth (shared with Codex and other agents); this file just makes the skill auto-discoverable in Claude Code and gives the fast path.

When to use

A user has a table/corpus of text, or a folder of images, and wants to see its structure — clusters, themes, outliers, similarity — not just a single number.

Fast path — text (drive it for the user)

export LATENT_SCOPE_DATA=~/latent-scope-data HF_HOME=~/hf-cache
uv run ls-ingest  mydata --path data.csv --text_column text
uv run ls-embed   mydata text huggingface-jinaai___jina-embeddings-v5-text-nano
uv run ls-umap    mydata embedding-001 25 0.1
uv run ls-cluster mydata umap-001 25 5 0.0 --method hdbscan
uv run ls-scope   mydata embedding-001 umap-001 cluster-001 default "My scope" "desc"
uv run ls-serve   $LATENT_SCOPE_DATA          # http://localhost:5001 — show the user

(ls-cluster auto-writes default labels, so no LLM is required for a first map. HDBSCAN/EVoC noise becomes an explicit "Unclustered" cluster — that's expected, not a bug; --assign-noise restores nearest-centroid reassignment.)

Fast path — images (point at a folder)

uv run ls-ingest       shots --path ~/Desktop     # a directory ⇒ image dataset (bytes + filename/date/size_kb)
uv run ls-embed        shots image clip-openai___clip-vit-base-patch32   # MPS/GPU automatic
uv run ls-umap         shots embedding-001 25 0.1
uv run ls-cluster      shots umap-001 25 5 0.0 --method hdbscan
uv run ls-scope        shots embedding-001 umap-001 cluster-001 default "Shots" "desc"
uv run ls-sprite-atlas shots scopes-001 image      # image-map tiles — do not skip
uv run ls-serve        $LATENT_SCOPE_DATA

Images referenced from a table must be raw bytes / HF dicts or http… URLs — local file paths are not detected as images; point ingest at the folder instead.

The five things that trip agents up (details in AGENTS.md)

A source checkout has no built web UI — ls-serve's API works but UI routes return 503 (older: JSON 404). Build once: cd web && npm install && npm run production && cd .. && mkdir -p latentscope/web/dist && cp -r web/dist/production/* latentscope/web/dist/ (no restart needed; npm ci may fail — use npm install). Pip-installed latentscope ships it pre-built.
Embedding is slow on a loaded CPU — check uptime/nvidia-smi first; use GPU (LATENT_SCOPE_DEVICE=cuda + latentscope[gpu]), a Mac (MPS is automatic), or a small model (bge-small).
jina-v5 is task-conditioned — it auto-defaults to task=retrieval (override with --task), auto-applies the document prompt, and needs peft. Leave --prefix blank.
Model ids are huggingface-<org>___<model> (/→___, no emoji). HF search accepts any sentence-transformers model.
Verify before claiming success — GET /api/datasets/<ds>/scopes; for images GET …/scopes/<scope>/atlas/status?column=<image_col> (param is column) → "generated": true; curl -sw '%{http_code}' localhost:5001/ must be 200; best: screenshot …/explore/<scope> and confirm the map renders.

Latent Scope

When to use

A user has a table/corpus of text, or a folder of images, and wants to see its structure — clusters, themes, outliers, similarity — not just a single number.

Fast path — text (drive it for the user)

export LATENT_SCOPE_DATA=~/latent-scope-data HF_HOME=~/hf-cache uv run ls-ingest mydata --path data.csv --text_column text uv run ls-embed mydata text huggingface-jinaai___jina-embeddings-v5-text-nano uv run ls-umap mydata embedding-001 25 0.1 uv run ls-cluster mydata umap-001 25 5 0.0 --method hdbscan uv run ls-scope mydata embedding-001 umap-001 cluster-001 default "My scope" "desc" uv run ls-serve $LATENT_SCOPE_DATA # http://localhost:5001 — show the user

Fast path — images (point at a folder)

uv run ls-ingest shots --path ~/Desktop # a directory ⇒ image dataset (bytes + filename/date/size_kb) uv run ls-embed shots image clip-openai___clip-vit-base-patch32 # MPS/GPU automatic uv run ls-umap shots embedding-001 25 0.1 uv run ls-cluster shots umap-001 25 5 0.0 --method hdbscan uv run ls-scope shots embedding-001 umap-001 cluster-001 default "Shots" "desc" uv run ls-sprite-atlas shots scopes-001 image # image-map tiles — do not skip uv run ls-serve $LATENT_SCOPE_DATA

Images referenced from a table must be raw bytes / HF dicts or http… URLs — local file paths are not detected as images; point ingest at the folder instead.

The five things that trip agents up (details in AGENTS.md)

A source checkout has no built web UI — ls-serve's API works but UI routes return 503 (older: JSON 404). Build once:

cd web && npm install && npm run production && cd .. && mkdir -p latentscope/web/dist && cp -r web/dist/production/* latentscope/web/dist/

(no restart needed; npm ci may fail — use npm install). Pip-installed latentscope ships it pre-built.

Embedding is slow on a loaded CPU — check uptime/nvidia-smi first; use GPU (LATENT_SCOPE_DEVICE=cuda + latentscope[gpu]), a Mac (MPS is automatic), or a small model (bge-small).

jina-v5 is task-conditioned — it auto-defaults to task=retrieval (override with --task), auto-applies the document prompt, and needs peft. Leave --prefix blank.

Model ids are huggingface-<org>___<model> (/→___, no emoji). HF search accepts any sentence-transformers model.

Verify before claiming success — GET /api/datasets/<ds>/scopes; for images GET …/scopes/<scope>/atlas/status?column=<image_col> (param is column) → "generated": true; curl -sw '%{http_code}' localhost:5001/ must be 200; best: screenshot …/explore/<scope> and confirm the map renders.