SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill gamma-phase-associatorgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/gamma-phase-associator)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/gamma-phase-associator"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/gamma-phase-associator/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/benchflow-ai/skillsbench/gamma-phase-associator"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/gamma-phase-associator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 18 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
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 | $0.00070 | $0.02684 |
| Opus 5 | $0.00035 | $0.01342 |
| Sonnet 5 | $0.00014 | $0.00537 |
| Haiku 4.5 | $0.00007 | $0.00268 |
Grade A, and why
gamma-phase-associator 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gamma-phase-associator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GaMMA Associator Library
What is GaMMA?
GaMMA is an earthquake phase association algorithm that treats association as an unsupervised clustering problem. It uses multivariate Gaussian distribution to model the collection of phase picks of an event, and uses Expectation-Maximization to carry out pick assignment and estimate source parameters i.e., earthquake location, origin time, and magnitude.
GaMMA is a python library implementing the algorithm. For the input earthquake traces, this library assumes P/S wave picks have already been extracted. We provide documentation of its core API.
Zhu, W., McBrearty, I. W., Mousavi, S. M., Ellsworth, W. L., & Beroza, G. C. (2022). Earthquake phase association using a Bayesian Gaussian mixture model. Journal of Geophysical Research: Solid Earth, 127(5).
The skill is a derivative of the repo https://github.com/AI4EPS/GaMMA
Installing GaMMA
pip install git+https://github.com/wayneweiqiang/GaMMA.git
GaMMA core API
association
Function Signature
def association(picks, stations, config, event_idx0=0, method="BGMM", **kwargs)
Purpose
Associates seismic phase picks (P and S waves) to earthquake events using Bayesian or standard Gaussian Mixture Models. It clusters picks based on arrival time and amplitude information, then fits GMMs to estimate earthquake locations, times, and magnitudes.
1. Input Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
picks |
DataFrame | required | Seismic phase pick data |
stations |
DataFrame | required | Station metadata with locations |
config |
dict | required | Configuration parameters |
event_idx0 |
int | 0 |
Starting event index for numbering |
method |
str | "BGMM" |
"BGMM" (Bayesian) or "GMM" (standard) |
2. Required DataFrame Columns
picks DataFrame
| Column | Type | Description | Example |
|---|---|---|---|
id |
str | Station identifier (must match stations) |
network.station. or network.station.location.channel |
timestamp |
datetime/str | Pick arrival time (ISO format or datetime) | "2019-07-04T22:00:06.084" |
type |
str | Phase type: "p" or "s" (lowercase) |
"p" |
prob |
float | Pick probability/weight (0-1) | 0.94 |
amp |
float | Amplitude in m/s (required if use_amplitude=True) |
0.000017 |
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 · 251 lines · 70 tokens per session scan A ce2c7375fccb
gamma-phase-associator is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 70 tokens to every session and 2,684 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 2026-09-03.
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