version: "1.0.1" name: emem-find-similar description: Returns the top-K places most similar to a seed place by cosine over a stored 128-D surface-texture embedding (Sentinel-1 plus Sentinel-2, annual). Use when the user asks for analogues or look-alikes ("find places like Bangalore", "where else looks like the Sundarbans"). The embedding band is retired on emem.dev, so the index is frozen at what was already stored; the skill says how to tell "no analogue" from "this seed has no vector and cannot get one".

emem-find-similar

Network use. The commands in this skill send and receive JSON (and, where a step says so, an image or a raster file) to https://emem.dev only, the service the plugin's MCP server connects to. Nothing they download is executed. The files under scripts/ read local files and make no network calls.

A nearest-neighbour search over a stored 128-D embedding of surface texture (Sentinel-1 SAR plus Sentinel-2 optical, aggregated per year). Two cells above about 0.85 cosine are usually the same physical archetype.

Read this before you promise an answer

The embedding band (geotessera) is retired on emem.dev. The responder serves vectors it already stored and computes no new ones:

• A seed that has a stored vector works as before.
• A seed without one cannot be given one. /v1/find_similar answers

cid_not_found, and /v1/recall on the band answers a note with reason band_retired_at_this_responder. That is a deployment decision, and retrying never changes it.

• Coverage is whatever was stored before the retirement: dense around

places people asked about, empty elsewhere.

"No analogue found" and "this place has no vector" are different answers. Only report the one that happened.

When to invoke

• "Find cities globally that look like Bangalore."
• "Where else has the same forest signature as the Western Ghats?"

For predicate matching ("all places with NDVI above 0.7") this is the wrong tool: use POST /v1/query_region or POST /v1/compare_bands. For climate similarity, compare koppen classes from recall instead; this embedding aliases very different climates that share a texture.

Step 1: resolve the seed

CELL=$(curl -sf -X POST https://emem.dev/v1/locate \
  -H 'content-type: application/json' \
  -d '{"q":"Bengaluru, India"}' | jq -r '.cell64')

Step 2: query, and branch on the answer

curl -s -X POST https://emem.dev/v1/find_similar \
  -H 'content-type: application/json' \
  -d "{\"key\":\"$CELL\",\"k\":12}" > sim.json
jq 'if .code then {code, message}
    else [.neighbors[] | {cell, score, place: .place_label_cached, lat, lng}] end' sim.json

Call it directly rather than probing with recall first. On 2026-09-28 a Timbuktu seed returned no geotessera fact from /v1/recall (a retired-band note) yet /v1/find_similar answered from its stored vector, so recall is not a reliable coverage test. cid_not_found from find_similar is.

Each neighbour carries cell, score (cosine), lat, lng, place_label_cached, band_used, similarity_method, scene_png_url and deep_recall_url. The response also carries a signed receipt over the neighbours' fact cids and an interpretation block that says what the similarity does and does not mean. Pass that on.

Recorded on 2026-09-28: seed defi.zb493.zezo.zcb35 (Bengaluru) with k: 5 returned four neighbours, all in Bengaluru, top score 0.926. A seed at 44.123 S, 120.456 W (open Pacific) returned cid_not_found.

Options

• band: "geotessera.2018" (any year 2017 to 2024) searches that

vintage; geotessera.multi_year searches the stacked 1024-D vector, which picks up places that changed in similar ways. Each vintage answers only where it was stored.

• mode: "hamming" runs the binary sign-bit index instead of cosine

(similarity_method: "hamming" on each neighbour). Do not pass band: "geotessera.bin128": on 2026-09-28 that returned an internal error.

Pitfalls

• Nearby is not similar. The top neighbours of a city cell are often

other cells of the same city. Ask for a larger k and filter by distance if the user wants places elsewhere.

• Archetype, not society. A "similar" city looks the same from

space (density, vegetation, water) and may be nothing alike otherwise.

• Thresholds are heuristic. Above 0.85 same archetype, 0.7 to 0.85

related, below 0.7 weak; the cutoff depends on the seed.

Before you report the neighbours

Similarity is a ranking over a frozen index, not a measurement. Name the neighbours with their cell64 and score, say the index is frozen, and cite band facts (recall at each neighbour) for anything you claim about them. See `emem-referential-drift` and `emem-verify-before-publish`.