Agentic skills

/plugin marketplace add lancedb/lancedb-agent-plugins
/plugin install lancedb@lancedb

codex plugin marketplace add lancedb/lancedb-agent-plugins
codex plugin add lancedb@lancedb

npx plugins add lancedb/lancedb-agent-plugins

/plugin marketplace update lancedb

codex plugin marketplace upgrade

npx plugins add lancedb/lancedb-agent-plugins

Import the HuggingFace dataset `lance-format/koch_pick_place_5_lego-lance`
into LanceDB.

Create a Function `extract_frames(video, timestamp)` that extracts each frame
from the videos table and stores the output as JPEG in a Lance blob column.
Use `torchcodec` to decode frames on a GPU, and stream the video through the
blob input instead of loading it into memory. Install dependencies with pip,
not conda.

Create a table `koch_frames` from `extract_frames`, populated from the videos
and frames metadata in the existing tables.

Create a Function that uses a vision-language model to caption each frame,
and add its output as a column named `caption` on `koch_frames`. It needs a
GPU.

Create a full-text search index on `koch_frames.caption`.

Import the HuggingFace dataset `lance-format/BDD100K-enriched` into LanceDB.

Create a Function `phash(image)` that computes a perceptual hash with the
`ImageHash` package, and add it as a column named `phash`.

Build a vector index on `phash` using Hamming distance, and pick good index
parameters for this dataset size.

Create a materialized view that clusters images whose `phash` Hamming
distance is less than 1, using `duplicate_pairs`.

Summarize the result: cluster count, cluster size distribution, and the 10
largest clusters with sample image ids.

Import the C, C++, and Python metadata from the HuggingFace dataset
`bigcode/the-stack-v2` into a single table `stack_v2_code`, with an extra
`language` column recording which of the three it is.

Add a `content` column and backfill it with each file's source, downloaded
from `s3://softwareheritage/content/{blob_id}` using the `blob_id` already in
the table. Compress `content` with the best codec LanceDB supports for code
text.

Use `bigcode/starcoder2-3b` to add a `perplexity_starcoder2` column, then
build an index on it.