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 pykxgit 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/pykx)<a href="https://agentmods.dev/skills/kxsystems/kx-skills/pykx"><img src="https://agentmods.dev/badge/skills/kxsystems/kx-skills/pykx/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kxsystems/kx-skills/pykx"><img src="https://agentmods.dev/badge/skills/kxsystems/kx-skills/pykx.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.02158 |
| Opus 5 | $0.00028 | $0.01079 |
| Sonnet 5 | $0.00011 | $0.00432 |
| Haiku 4.5 | $0.00006 | $0.00216 |
Grade A, and why
pykx 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 10d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KDB-X Python
KDB-X Python is the Python-first interface to kdb+ and q. import pykx as kx
For full Column methods, DB API, IPC details, type mapping: see reference.md
Critical Patterns (Common Mistakes)
Column API for WHERE: Use Operators, Not Strings
# CORRECT: kx.Column with Python operators
table.select(where=kx.Column('price') > 100)
table.select(where=(kx.Column('sym') == 'AAPL') & (kx.Column('price') > 150))
# WRONG — string-based where does NOT work
table.select(where='price > 100') # WRONG!
table.select(where='sym = `AAPL') # WRONG!
Combining WHERE Conditions: Use & | ~ (Not and/or/not)
# CORRECT: bitwise operators with parentheses
where=(kx.Column('sym') == 'AAPL') & (kx.Column('price') > 100) # AND
where=(kx.Column('sym') == 'AAPL') | (kx.Column('sym') == 'GOOG') # OR
where=~(kx.Column('size') < 100) # NOT
# WRONG — Python keywords don't work with Column objects
where=kx.Column('sym') == 'AAPL' and kx.Column('price') > 100 # WRONG!
RawQConnection: Async + Queue Then poll_send/poll_recv
# CORRECT: async construction, queue query, then poll_send/poll_recv
q = await kx.RawQConnection(host='localhost', port=5000)
q('select from trades') # Queue the query (not sent yet)
q.poll_send() # Send queued queries
result = q.poll_recv() # Receive response
# WRONG — poll_send does NOT take a query string
q.poll_send('select from trades') # WRONG! poll_send takes amount, not query
# WRONG — RawQConnection requires async construction
with kx.RawQConnection(...) as q: # WRONG! use await, not sync with
Creating pykx Objects
qlist = kx.toq([1, 2, 3]) # LongVector (auto)
qlist = kx.toq([1, 2, 3], kx.FloatVector) # Explicit type
qtable = kx.toq(df) # DataFrame -> Table
kx.random.random(10, 100.0) # 10 random floats 0-100
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.
- 10d ago First seen · 227 lines · 55 tokens per session scan A 9e42c9912a7c
pykx is a skill published in the GitHub repository KxSystems/kx-skills (16 stars, last pushed 7d ago), licensed Apache-2.0. It adds 55 tokens to every session and 2,158 once invoked, about $0.0003 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.
Other skills, from other repositories
adding-personhog-rpc
Guide for adding a new RPC to personhog-replica and personhog-router. Covers eligibility checks, proto definition, code generation for Python and Node.js clients, Rust implementation (storage trait, postgres queries, service handler, router wiring), and index compatibility validation. Use when adding a new gRPC…
azure-cosmos-db-py
Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service...
azure-cosmos-py
Client library for Azure Cosmos DB NoSQL API — globally distributed, multi-model database.
azure-data-tables-py
NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).
neo4j-driver-python-skill
Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver…
alembic
Manage database migrations with Alembic. Use when a user asks to version database schemas, create migration scripts, handle schema changes in production, or manage SQLAlchemy model migrations.