Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add KxSystems/kx-skills --skill pipeline-devgit clone --depth 1 https://github.com/KxSystems/kx-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kxsystems/kx-skills/pipeline-dev)<a href="https://agentmods.dev/skills/kxsystems/kx-skills/pipeline-dev"><img src="https://agentmods.dev/badge/skills/kxsystems/kx-skills/pipeline-dev.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.01080 |
| Opus 5 | $0.00057 | $0.00540 |
| Sonnet 5 | $0.00023 | $0.00216 |
| Haiku 4.5 | $0.00012 | $0.00108 |
Grade A, and why
pipeline-dev scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SP Pipeline Writer
A pipeline is a DAG: readers → operators → writers, chained in q with bracket notation or in
Python with |.
Step 1 — Clarify (required before generating)
Before writing any code, confirm all four of these. Ask together in a single message for anything not already stated:
| # | What to confirm | Examples |
|---|---|---|
| 1 | Source | Kafka, S3, HTTP, callback, database, expression |
| 2 | Sink | stream, database, Kafka, console, variable |
| 3 | Language | q, Python, or both |
| 4 | Transform | filter, map, window, join, ML, schema rename, none |
Do not generate a pipeline until all four are clear. If the user's request answers some but not all, ask only about the gaps. Skip optional details (schema registry, auth) unless the user raises them.
Step 2 — Look up operator signatures
Don't guess API signatures — fetch them before generating code.
Primary: kx-docs MCP (use mcp__kx-docs__search_kx_knowledge_sources if available):
"SP readers fromKafka fromAmazonS3"
"SP writers toDatabase toStream toKafka"
"SP decode encode avro json csv"
"SP operators map filter apply merge split"
"SP window tumbling sliding"
"SP transform renameColumns replaceNull"
"SP ml minMaxScaler"
Fallback: public docs (if MCP unavailable — switch silently, only report if both fail):
Base URL: https://code.kx.com/insights/api/stream-processor/
| Need | Page |
|---|---|
| Read connectors | readers.html |
| Write connectors | writers.html |
| Decoders / Encoders | decoders.html / encoders.html |
| map / filter / apply / merge / split | operators.html |
| Window operators | windows.html |
| Transform utilities | transform.html |
| ML operators | ml.html |
.qsp.use / operator config |
configuring-operators.html |
Step 3 — Generate the pipeline
Hard rules:
.qsp.run/sp.run(..)called exactly once- Readers have no upstream nodes; writers have no downstream nodes
- In q: always bracket notation (
.qsp.map[fn]) — never prefix form - Operator config via
.qsp.usein q; keyword args in Python - Prefer
v2namespace when available (.qsp.v2.read.fromAmazonS3,api_version=2in Python)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 131 lines · 115 tokens per session scan A b6fa23389c90
pipeline-dev is a skill published in the GitHub repository KxSystems/kx-skills (16 stars, last pushed 6d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,080 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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