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 lucifer1004/VeloQ --skill nsys-profile-analysisgit clone --depth 1 https://github.com/lucifer1004/VeloQWrote 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/lucifer1004/veloq/nsys-profile-analysis)<a href="https://agentmods.dev/skills/lucifer1004/veloq/nsys-profile-analysis"><img src="https://agentmods.dev/badge/skills/lucifer1004/veloq/nsys-profile-analysis/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/lucifer1004/veloq/nsys-profile-analysis"><img src="https://agentmods.dev/badge/skills/lucifer1004/veloq/nsys-profile-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00060 | $0.01408 |
| Opus 5 | $0.00030 | $0.00704 |
| Sonnet 5 | $0.00012 | $0.00282 |
| Haiku 4.5 | $0.00006 | $0.00141 |
Grade C, and why
nsys-profile-analysis scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/lucifer1004/veloq/main/scripts/install.sh | bash Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/lucifer1004/veloq/main/scripts/install.sh | bash How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nsight Systems Profile Analysis
Use veloq as the evidence extractor for NSys timeline traces: what ran,
when, how CPU work caused GPU work, where idle gaps are, and whether
captured metric streams are trustworthy. Requires veloq on PATH.
For Nsight Compute .ncu-rep kernel reports use ncu-profile-analysis;
for PyTorch/Kineto Chrome traces use pytorch-profile-analysis.
Quickstart
veloq info trace.nsys-rep # detect source, trace map, applicable recipes
veloq summary trace.nsys-rep | jq '.data.auxiliary.capabilities'
Then query with stats / search / inspect / correlate / slices /
gaps / timeline / concurrency / graph-replays / metrics /
hardware. Per-command flags and the response schema live in
veloq <cmd> --help and veloq schema <cmd>; canonical multi-step
workflows live in veloq recipes / veloq recipes <id>. Those are the
SSOT — do not duplicate them here.
stdout is always one JSON envelope: data.rows[] on success (every row
carries a stable key), error on failure. Parse .data/.error,
never stderr.
What veloq gives you over raw tables
Querying the exported sqlite/parquet directly is possible but you re-implement — and can silently get wrong — things veloq already does:
- Correlation decode: runtime↔kernel/memcpy/memset joins through
process-local
correlationIdbridged byTARGET_INFO_CUDA_CONTEXT_INFOand theglobalTidPID mask (correlate/inspectdo this for you). - NVTX attribution: forward (range → GPU work) and reverse (kernel →
enclosing ranges) trees, incl. nesting depth and
--nvtxscoping. - A correlation index + sidecar caches (
<trace>.veloq/) reused across queries; first.nsys-repaccess runsnsys exportfor you. - Stable row keys (
kernel:1234,gap|pid:..|@..) — diff two captures withINDEX(.data.rows; .key)in jq.
If you do read raw tables anyway (user asked, or veloq lacks the query), you own the invariants in the pitfalls list below — every one of them has produced a plausible-but-wrong answer in practice.
What ships with it
3 files 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 · 114 lines · 60 tokens per session scan C 2e6b3544bd87
nsys-profile-analysis is a skill published in the GitHub repository lucifer1004/VeloQ (120 stars, last pushed 19d ago), licensed MIT. It adds 60 tokens to every session and 1,408 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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