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 skillmds/skillmd --skill graphsignal-profilergit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/graphsignal-profiler)<a href="https://agentmods.dev/skills/skillmds/skillmd/graphsignal-profiler"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/graphsignal-profiler/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/skillmds/skillmd/graphsignal-profiler"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/graphsignal-profiler.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 | $0.00074 | $0.01777 |
| Opus 5.5 | $0.00030 | $0.00711 |
| Sonnet 5 | $0.00015 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
graphsignal-profiler scanned grade A with 1 finding 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 4d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
It enables GPU profiling in this process and starts the profiler sidecar to observe it. Returns the `subprocess.Popen` so the caller can `wait()` or `terminate()` it. This is a copy
100% identical to graphsignal-profiler — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graphsignal Profiler
Graphsignal observes inference workloads from a sidecar process — the profiler. It never shares a process with CUDA: the profiler watches the workload externally via CUPTI, OTLP/gRPC, Prometheus scraping, and NVML. Auto-instrumentation covers vLLM, SGLang, and PyTorch out of the box.
Install
Two install patterns depending on how you'll launch the profiler.
For graphsignal-run (CLI, recommended): install as a uv tool, isolated from your workload env.
UV_TOOL_BIN_DIR=/usr/local/bin uv tool install 'graphsignal[cu12]' # CUDA 12.x
# or
UV_TOOL_BIN_DIR=/usr/local/bin uv tool install 'graphsignal[cu13]' # CUDA 13.x
UV_TOOL_BIN_DIR=/usr/local/bin puts graphsignal-run in a directory that is already on PATH for every shell, including non-interactive scripts and containers.
For graphsignal.watch() (in-process Python entry point): install into the app's own env.
uv add 'graphsignal[cu12]' # or pip install -U 'graphsignal[cu12]'
The cu12 / cu13 extras are Linux-only and only needed for GPU profiling.
Configure
The profiler reads its config from environment variables.
| Variable | Purpose |
|---|---|
GRAPHSIGNAL_API_KEY (required) |
Account API key. |
GRAPHSIGNAL_API_BASE |
Override the API endpoint (defaults to https://api.graphsignal.com). |
GRAPHSIGNAL_TAG_<KEY>=<value> |
Arbitrary tag attached to all signals (e.g. GRAPHSIGNAL_TAG_DEPLOYMENT=us-prod). |
Set these before invoking graphsignal-run or calling graphsignal.watch().
Run
Option A — graphsignal-run CLI (recommended)
Wrap the launch command for your workload.
export GRAPHSIGNAL_API_KEY="..."
graphsignal-run vllm serve Qwen/Qwen1.5-7B-Chat --port 8000
What ships with it
7 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.
- 4d ago First seen · 206 lines · 74 tokens per session scan A 5a4a8446bac7
graphsignal-profiler is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,777 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to graphsignal-profiler, differing in 1 line, and is treated as a copy.
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