Agent skill

Gamma Phase Associator

by benchflow-ai in benchflow-ai/skillsbench

An overview of the python package for running the GaMMA earthquake phase association algorithm.

Apache-2.0Auto-check passedData & Analytics

Install Gamma Phase Associator

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .claude/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .claude/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .agents/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .agents/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .cursor/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .cursor/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/earthquake-phase-association/environment/skills/gamma-phase-associator

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .gemini/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .gemini/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .github/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .github/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install benchflow-ai/skillsbench gamma-phase-associator --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator .opencode/skills/gamma-phase-associator && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "gamma-phase-associator" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/earthquake-phase-association/environment/skills/gamma-phase-associator into .opencode/skills/gamma-phase-associator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gamma-phase-associator", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: gamma-phase-associator
GitHub stars: 1.8k
Token cost: ~2.5k tokens
SKILL.md length: 969 words
Files: 1
Skills in repo: 189
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

An overview of the python package for running the GaMMA earthquake phase association algorithm.

Works in 10 steps: Input Parameters → Required DataFrame Columns → Config Dictionary Keys → …
Tasks that involve DataFrames
SKILL.md covers What is GaMMA?, Installing GaMMA and GaMMA core API
Calls pip; reaches github.com

What it does

Gamma Phase Associator is an agent skill from benchflow-ai/skillsbench. An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering DataFrames. It works with Python. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

Tasks that involve DataFrames

Example prompts

“/gamma-phase-associator”

Requirements

Python 3

Workflow steps

10 steps, taken from the step headings in SKILL.md.

Input Parameters
Required DataFrame Columns
Config Dictionary Keys
Return Values
Input Parameters
Required DataFrame Columns
Return Value
Example Usage
Practical Notes
Related Configuration

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gamma Phase Associator loads about 2.5k tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 969 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~102
When it runs · the whole SKILL.md, loaded when a task matches: ~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 969 words, ~2,527 tokens.

Download SKILL.mdSave it as .claude/skills/gamma-phase-associator/SKILL.md (or your agent's skills folder).

name: gamma-phase-associator
description: An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

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

python

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`

Notes:

Timestamps must be in UTC or converted to UTC
Phase types are forced to lowercase internally
Picks with amp == 0 or amp == -1 are filtered when use_amplitude=True
The DataFrame index is used to track pick identities in the output

`stations` DataFrame

Column	Type	Description	Example
`id`	str	Station identifier	`"CI.CCC..BH"`
`x(km)`	float	X coordinate in km (projected)	`-35.6`
`y(km)`	float	Y coordinate in km (projected)	`45.2`
`z(km)`	float	Z coordinate (elevation, typically negative)	`-0.67`

Notes:

Coordinates should be in a projected local coordinate system (e.g., you can use the pyproj package)
The id column must match the id values in the picks DataFrame (e.g., network.station. or network.station.location.channel)
Group stations by unique id, identical attribute are collapsed to a single value and conflicting metadata are preseved as a sorted list.

3. Config Dictionary Keys

Required Keys

Key	Type	Description	Example
`dims`	list[str]	Location dimensions to solve for	`["x(km)", "y(km)", "z(km)"]`
`min_picks_per_eq`	int	Minimum picks required per earthquake	`5`
`max_sigma11`	float	Maximum allowed time residual in seconds	`2.0`
`use_amplitude`	bool	Whether to use amplitude in clustering	`True`
`bfgs_bounds`	tuple	Bounds for BFGS optimization	`((-35, 92), (-128, 78), (0, 21), (None, None))`
`oversample_factor`	float	Factor for oversampling initial GMM components	`5.0` for `BGMM`, `1.0` for `GMM`

Notes on dims:

Options: ["x(km)", "y(km)", "z(km)"], ["x(km)", "y(km)"], or ["x(km)"]

Notes on bfgs_bounds:

Format: ((x_min, x_max), (y_min, y_max), (z_min, z_max), (None, None))
The last tuple is for time (unbounded)

Velocity Model Keys

Key	Type	Default	Description
`vel`	dict	`{"p": 6.0, "s": 3.47}`	Uniform velocity model (km/s)
`eikonal`	dict/None	`None`	1D velocity model for travel times

DBSCAN Pre-clustering Keys (Optional)

Key	Type	Default	Description
`use_dbscan`	bool	`True`	Enable DBSCAN pre-clustering
`dbscan_eps`	float	`25`	Max time between picks (seconds)
`dbscan_min_samples`	int	`3`	Min samples in DBSCAN neighborhood
`dbscan_min_cluster_size`	int	`500`	Min cluster size for hierarchical splitting
`dbscan_max_time_space_ratio`	float	`10`	Max time/space ratio for splitting

dbscan_eps is obtained from estimate_eps Function

Show full SKILL.md (385 more words)Show less

Filtering Keys (Optional)

| Key | Type | Default | Description | |-----|------|-------------| | max_sigma22 | float | 1.0 | Max phase amplitude residual in log scale (required if use_amplitude=True) | | max_sigma12 | float | 1.0 | Max covariance | | max_sigma11 | float | 2.0 | Max phase time residual (s) | | min_p_picks_per_eq | int | 0 | Min P-phase picks per event | | min_s_picks_per_eq | int | 0 |Min S-phase picks per event | | min_stations | int | 5 |Min unique stations per event |

Other Optional Keys

Key	Type	Default	Description
`covariance_prior`	list[float]	auto	Prior for covariance `[time, amp]`
`ncpu`	int	auto	Number of CPUs for parallel processing

4. Return Values

Returns a tuple (events, assignments):

`events` (list[dict])

List of dictionaries, each representing an associated earthquake:

Key	Type	Description
`time`	str	Origin time (ISO 8601 with milliseconds)
`magnitude`	float	Estimated magnitude (999 if `use_amplitude=False`)
`sigma_time`	float	Time uncertainty (seconds)
`sigma_amp`	float	Amplitude uncertainty (log10 scale)
`cov_time_amp`	float	Time-amplitude covariance
`gamma_score`	float	Association quality score
`num_picks`	int	Total picks assigned
`num_p_picks`	int	P-phase picks assigned
`num_s_picks`	int	S-phase picks assigned
`event_index`	int	Unique event index
`x(km)`	float	X coordinate of hypocenter
`y(km)`	float	Y coordinate of hypocenter
`z(km)`	float	Z coordinate (depth)

`assignments` (list[tuple])

List of tuples (pick_index, event_index, gamma_score):

pick_index: Index in the original picks DataFrame
event_index: Associated event index
gamma_score: Probability/confidence of assignment

`estimate_eps` Function Documentation

Function Signature

python

def estimate_eps(stations, vp, sigma=2.0)

Purpose

Estimates an appropriate DBSCAN epsilon (eps) parameter for clustering seismic phase picks based on station spacing. The eps parameter controls the maximum time distance between picks that should be considered neighbors in the DBSCAN clustering algorithm.

1. Input Parameters

Parameter	Type	Default	Description
`stations`	DataFrame	required	Station metadata with 3D coordinates
`vp`	float	required	P-wave velocity in km/s
`sigma`	float	`2.0`	Number of standard deviations above the mean

2. Required DataFrame Columns

`stations` DataFrame

Column	Type	Description	Example
`x(km)`	float	X coordinate in km	`-35.6`
`y(km)`	float	Y coordinate in km	`45.2`
`z(km)`	float	Z coordinate in km	`-0.67`

3. Return Value

Type	Description
float	Epsilon value in seconds for use with DBSCAN clustering

4. Example Usage

python

from gamma.utils import estimate_eps

# Assuming stations DataFrame is already prepared with x(km), y(km), z(km) columns
vp = 6.0  # P-wave velocity in km/s

# Estimate eps automatically based on station spacing
eps = estimate_eps(stations, vp, sigma=2.0)

# Use in config
config = {
    "use_dbscan": True,
    "dbscan_eps": eps,  # or use estimate_eps(stations, config["vel"]["p"])
    "dbscan_min_samples": 3,
    # ... other config options
}

Typical Usage Pattern

python

from gamma.utils import association, estimate_eps

# Automatic eps estimation
config["dbscan_eps"] = estimate_eps(stations, config["vel"]["p"])

# Or manual override (common in practice)
config["dbscan_eps"] = 15  # seconds

5. Practical Notes

In example notebooks, the function is often commented out in favor of hardcoded values (10-15 seconds)
Practitioners may prefer manual tuning for specific networks/regions
Typical output values range from 10-20 seconds depending on station density
Useful when optimal eps is unknown or when working with new networks

The output is typically used with these config parameters:

python

config["dbscan_eps"] = estimate_eps(stations, config["vel"]["p"])
config["dbscan_min_samples"] = 3
config["dbscan_min_cluster_size"] = 500
config["dbscan_max_time_space_ratio"] = 10

Files

Just SKILL.md in tasks/earthquake-phase-association/environment/skills/gamma-phase-associator of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

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Questions about Gamma Phase Associator

What does Gamma Phase Associator do?

An overview of the python package for running the GaMMA earthquake phase association algorithm. Gamma Phase Associator is an agent skill from benchflow-ai/skillsbench. An overview of the python package for running the GaMMA earthquake phase association algorithm.

When should I use Gamma Phase Associator?

Gamma Phase Associator fits situations like: tasks that involve DataFrames.

How do I install Gamma Phase Associator in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a claude-code`. Or copy the skill folder (tasks/earthquake-phase-association/environment/skills/gamma-phase-associator in benchflow-ai/skillsbench) into .claude/skills/gamma-phase-associator in your project. Claude Code loads it when a task matches its description.

How do I install Gamma Phase Associator in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a codex`. Or copy the skill folder (tasks/earthquake-phase-association/environment/skills/gamma-phase-associator in benchflow-ai/skillsbench) into .agents/skills/gamma-phase-associator in your project. Codex loads it when a task matches its description.

Can I use Gamma Phase Associator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill gamma-phase-associator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gamma-phase-associator, .gemini/skills/gamma-phase-associator, .github/skills/gamma-phase-associator and .opencode/skills/gamma-phase-associator in your project.

What does Gamma Phase Associator need to run?

Going by SKILL.md and its folder, Gamma Phase Associator needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Gamma Phase Associator access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Gamma Phase Associator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gamma Phase Associator use?

Gamma Phase Associator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gamma Phase Associator use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gamma Phase Associator?

Skills that share tags, products or a category with Gamma Phase Associator: Chdb Datastore (vemetric/vemetric, 395 stars), Polar Python SDK (polarsource/polar, 10k stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gamma Phase Associator?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Gamma Phase Associator

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

GaMMA Associator Library

What is GaMMA?

Installing GaMMA

GaMMA core API

association

Function Signature

Purpose

1. Input Parameters

2. Required DataFrame Columns

picks DataFrame

stations DataFrame

3. Config Dictionary Keys

Required Keys

Velocity Model Keys

DBSCAN Pre-clustering Keys (Optional)

Filtering Keys (Optional)

Other Optional Keys

4. Return Values

events (list[dict])

assignments (list[tuple])

estimate_eps Function Documentation

Function Signature

Purpose

1. Input Parameters

2. Required DataFrame Columns

stations DataFrame

3. Return Value

4. Example Usage

Typical Usage Pattern

5. Practical Notes

6. Related Configuration

Files

Compare with similar skills

Similar skills

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Related

Questions about Gamma Phase Associator

`association`

`picks` DataFrame

`stations` DataFrame

`events` (list[dict])

`assignments` (list[tuple])

`estimate_eps` Function Documentation

`stations` DataFrame