From kdbx-knowledge
Use when working with KDB-X modules, AI libraries (vector search, HNSW, IVF, BM25, time-series similarity, anomaly detection), GPU acceleration, Parquet, kURL REST client, object storage, or the module framework. Also use when encountering module-not-found errors, vector dimension mismatches, HNSW index issues, GPU setup issues, or `nyi` errors from using `\l` instead of `use`.
How this skill is triggered — by the user, by Claude, or both
Slash command
/kdbx-knowledge:kdbxThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Unified compute engine: time-series analytics + vector search + GPU-accelerated compute in one runtime. GA April 2026. Multi-language (q, Python, SQL), native Parquet/Arrow, 25+ community modules. Community Edition free at developer.kx.com.
Unified compute engine: time-series analytics + vector search + GPU-accelerated compute in one runtime. GA April 2026. Multi-language (q, Python, SQL), native Parquet/Arrow, 25+ community modules. Community Edition free at developer.kx.com.
| Need | Skill |
|---|---|
| KDB-X modules, AI libs, Parquet, kURL, GPU, objstor | This skill |
| q language syntax, operators, table queries | /q |
| PyKX Python-to-kdb+ integration | /pykx |
| KDB.AI cloud vector database | /kdbai |
| kdb Insights enterprise platform | /kdb-insights |
Always use, NEVER \l (\l doesn't bind native functions):
.pq:use`kx.pq / Parquet .pq.t:use`kx.pq.t / Virtual tables
.kurl:use`kx.kurl / REST client .ai:use`kx.ai / AI libraries
.gpu:use`kx.gpu / GPU accel .objstor:use`kx.objstor / Object storage
.rest:use`kx.rest / REST server
trades: pq `:trades.parquet / Virtual table (row-group pruning)
select from trades where date > 2024.01.01
meta: op `:data.parquet / File metadata
vt: (.pq.t:use`kx.pq.t)[`tt] tbl / Wrap kdb+ table as virtual
Compression: snappy, gzip, brotli, lz4, zstd.
/ .kurl.sync (url; method; options) -> (statusCode; body)
resp:.kurl.sync ("https://api.example.com/data"; `GET; ::)
resp:.kurl.sync ("https://api.example.com"; `POST; enlist[`body]!enlist .j.j data)
resp:.kurl.sync ("https://s3.example.com/f"; `PUT; enlist[`file]!enlist `:path/to/file.csv)
opts:`headers`body!(("Content-Type";"x-api-key")!("application/json";"key123"); .j.j data)
.kurl.async ("https://api.example.com/data"; `GET; enlist[`callback]!enlist (`;{show x}))
/ Auth: .kurl.init`aws then .kurl.register (type; domain; tenant; info)
/ Types: `aws_cred`aws_sts`oauth2`oauth2_jwt`azure`basic
.kurl.aws.registerByCredentialsFile `:~/.aws/credentials / or register manually
Options: `headers`body`file`callback`service`region
.objstor.init[] / Register all vendors (or .objstor.init`aws)
/ URI schemes: :s3:// :ms:// :gs:// — standard kdb+ file ops work:
hcount `$":s3://bucket/path/file" / read1, key, get also work
Env vars: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, AZURE_STORAGE_ACCOUNT, AZURE_STORAGE_SHARED_KEY, GCP_TOKEN. S3-compatible: KX_S3_ENDPOINT.
| Index | Namespace | Use Case |
|---|---|---|
| HNSW | .ai.hnsw | Fast approximate search (default) |
| Flat | .ai.flat | Exact search, small datasets |
| IVF | .ai.ivf | Large-scale partitioned |
| IVFPQ | .ai.pq | Compressed, memory-constrained |
| BM25 | .ai.bm25 | Text/keyword search |
| Fuzzy | .ai.fuzzy | Approximate string match |
| Hybrid | .ai.hybrid | Vector + BM25 via RRF |
| TSS | .ai.tss | Time series similarity + anomaly |
| DTW | .ai.dtw | Dynamic time warping |
/ HNSW (most common): embs and hnsw are SEPARATE objects
vecs:{(x;y)#(x*y)?1e}[1000;10]
hnsw:.ai.hnsw.put[();();vecs;`L2;32;1%log 32;64]
res:.ai.hnsw.search[vecs;hnsw;first vecs;5;`L2;32] / metric MUST match put
merged:.ai.hybrid.rrf[(vecResults; bm25Results); 60] / RRF fusion
Full API signatures: See ai-reference.md
T:.gpu.to trades / Move to GPU (all columns)
T:.gpu.xto[`price`size] trades / Mixed residency
.gpu.select[T;enlist(=;`sym;enlist`AAPL);0b;()] / GPU qSQL (functional select)
.gpu.aj[`sym`time; Trade; Quote] / GPU as-of join (needs `g#)
.gpu.xasc[`time] T / GPU sort
Requires NVIDIA data center GPUs, CUDA 13.1, driver v590+. 10x-25x speedups, near-linear multi-GPU scaling. Full API: See gpu-reference.md
select avg price by 5 xbar time from trades where sym=`AAPL / q
s)SELECT AVG(price) FROM trades WHERE sym='AAPL' GROUP BY time/5 / SQL
| Error | Cause | Fix |
|---|---|---|
nyi | \l instead of use | .mod:use\kx.mod` |
module not found | QHOME not set | getenv\QHOME` |
length on search | Dim mismatch | count queryVec must match index |
| Wrong results | Metric mismatch put/search | Same metric in both |
rank on search | Empty index | Verify data populated |
type on hnsw.put | Wrong vector type | Cast to real |
mcp__qmcp__connect_to_q — Connect to KDB-Xmcp__qmcp__query_q — Execute queriesGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.
npx claudepluginhub kxsystems/kx-skills --plugin kdbx-knowledge