Agent skill

Matched Filtering

by benchflow-ai in benchflow-ai/skillsbench

Matched filtering techniques for gravitational wave detection.

Apache-2.0Auto-check passed

Install Matched Filtering

skills CLI

$ npx skills add benchflow-ai/skillsbench --skill matched-filtering -a claude-code

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench matched-filtering --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/gravitational-wave-detection/environment/skills/matched-filtering .claude/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .claude/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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/gravitational-wave-detection/environment/skills/matched-filtering

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 matched-filtering -a codex

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench matched-filtering --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/gravitational-wave-detection/environment/skills/matched-filtering .agents/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .agents/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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 matched-filtering -a cursor

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench matched-filtering --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/gravitational-wave-detection/environment/skills/matched-filtering .cursor/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .cursor/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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/gravitational-wave-detection/environment/skills/matched-filtering

--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 matched-filtering -a gemini-cli

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

GitHub CLI

$ gh skill install benchflow-ai/skillsbench matched-filtering --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/gravitational-wave-detection/environment/skills/matched-filtering .gemini/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .gemini/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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 matched-filtering

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 matched-filtering -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/gravitational-wave-detection/environment/skills/matched-filtering .github/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .github/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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 matched-filtering -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 matched-filtering --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/gravitational-wave-detection/environment/skills/matched-filtering .opencode/skills/matched-filtering && 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 "matched-filtering" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/matched-filtering into .opencode/skills/matched-filtering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "matched-filtering", 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: matched-filtering
GitHub stars: 1.8k
Token cost: ~1.6k tokens
SKILL.md length: 449 words
Files: 1
Skills in repo: 178
Repo updated: Jul 23, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Matched filtering techniques for gravitational wave detection.

Works in 4 steps: Template waveform (expected signal shape) → Conditioned detector data (preprocessed… → Power spectral density (PSD) of the noise → …
Searching for signals in detector data using template waveforms
SKILL.md covers Overview, Time-Domain Waveforms, Frequency-Domain Waveforms and Key Differences: Time vs…, plus 6 more sections
Calls pip

What it does

Matched Filtering is an agent skill from benchflow-ai/skillsbench. Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

Searching for signals in detector data using template waveforms
Including both time-domain and frequency-domain approaches

Example prompts

“/matched-filtering”

Requirements

Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Template waveform (expected signal shape)
Conditioned detector data (preprocessed strain)
Power spectral density (PSD) of the noise
SNR calculation and peak finding

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
Links to these hosts (documentation or services it may open):
- pycbc.org
- colab.research.google.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

Matched Filtering loads about 1.6k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 449 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~73
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

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). 449 words, ~1,600 tokens.

Download SKILL.mdSave it as .claude/skills/matched-filtering/SKILL.md (or your agent's skills folder).

name: matched-filtering
description: Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

Matched Filtering for Gravitational Wave Detection

Matched filtering is the primary technique for detecting gravitational wave signals in noisy detector data. It correlates known template waveforms with the detector data to find signals with high signal-to-noise ratio (SNR).

Overview

Matched filtering requires:

Template waveform (expected signal shape)
Conditioned detector data (preprocessed strain)
Power spectral density (PSD) of the noise
SNR calculation and peak finding

PyCBC supports both time-domain and frequency-domain approaches.

Time-Domain Waveforms

Generate templates in time domain using get_td_waveform:

python

from pycbc.waveform import get_td_waveform
from pycbc.filter import matched_filter

# Generate time-domain waveform
hp, hc = get_td_waveform(
    approximant='IMRPhenomD',  # or 'SEOBNRv4_opt', 'TaylorT4'
    mass1=25,                  # Primary mass (solar masses)
    mass2=20,                  # Secondary mass (solar masses)
    delta_t=conditioned.delta_t,  # Must match data sampling
    f_lower=20                 # Lower frequency cutoff (Hz)
)

# Resize template to match data length
hp.resize(len(conditioned))

# Align template: cyclic shift so merger is at the start
template = hp.cyclic_time_shift(hp.start_time)

# Perform matched filtering
snr = matched_filter(
    template,
    conditioned,
    psd=psd,
    low_frequency_cutoff=20
)

# Crop edges corrupted by filtering
# Remove 4 seconds for PSD + 4 seconds for template length at start
# Remove 4 seconds at end for PSD
snr = snr.crop(4 + 4, 4)

# Find peak SNR
import numpy as np
peak_idx = np.argmax(abs(snr).numpy())
peak_snr = abs(snr[peak_idx])

Why Cyclic Shift?

Waveforms from get_td_waveform have the merger at time zero. For matched filtering, we typically want the merger aligned at the start of the template. cyclic_time_shift rotates the waveform appropriately.

Frequency-Domain Waveforms

Generate templates in frequency domain using get_fd_waveform:

python

from pycbc.waveform import get_fd_waveform
from pycbc.filter import matched_filter

# Calculate frequency resolution
delta_f = 1.0 / conditioned.duration

# Generate frequency-domain waveform
hp, hc = get_fd_waveform(
    approximant='IMRPhenomD',
    mass1=25,
    mass2=20,
    delta_f=delta_f,           # Frequency resolution (must match data)
    f_lower=20                 # Lower frequency cutoff (Hz)
)

# Resize template to match PSD length
hp.resize(len(psd))

# Perform matched filtering
snr = matched_filter(
    hp,
    conditioned,
    psd=psd,
    low_frequency_cutoff=20
)

# Find peak SNR
import numpy as np
peak_idx = np.argmax(abs(snr).numpy())
peak_snr = abs(snr[peak_idx])

Key Differences: Time vs Frequency Domain

Time Domain (`get_td_waveform`)

Pros: Works for all approximants, simpler to understand
Cons: Can be slower for long waveforms
Use when: Approximant doesn't support frequency domain, or you need time-domain manipulation

Frequency Domain (`get_fd_waveform`)

Pros: Faster for matched filtering, directly in frequency space
Cons: Not all approximants support it (e.g., SEOBNRv4_opt may not be available)
Use when: Approximant supports it and you want computational efficiency

Approximants

Common waveform approximants:

python

# Phenomenological models (fast, good accuracy)
'IMRPhenomD'      # Good for most binary black hole systems
'IMRPhenomPv2'    # More accurate for precessing systems

# Effective One-Body models (very accurate, slower)
'SEOBNRv4_opt'    # Optimized EOB model (time-domain only typically)

# Post-Newtonian models (approximate, fast)
'TaylorT4'        # Post-Newtonian expansion

Note: Some approximants may not be available in frequency domain. If get_fd_waveform fails, use get_td_waveform instead.

Matched Filter Parameters

`low_frequency_cutoff`

Should match your high-pass filter cutoff (typically 15-20 Hz)
Templates are only meaningful above this frequency
Lower values = more signal, but more noise

Template Resizing

Time domain: hp.resize(len(conditioned)) - match data length
Frequency domain: hp.resize(len(psd)) - match PSD length
Critical for proper correlation

Show full SKILL.md (189 more words)Show less

Crop Amounts

After matched filtering, crop edges corrupted by:

PSD filtering: 4 seconds at both ends
Template length: Additional 4 seconds at start (for time-domain)
Total: snr.crop(8, 4) for time-domain, snr.crop(4, 4) for frequency-domain

Best Practices

Match sampling/frequency resolution: Template delta_t/delta_f must match data
Resize templates correctly: Time-domain → data length, Frequency-domain → PSD length
Crop after filtering: Always crop edges corrupted by filtering
Use abs() for SNR: Matched filter returns complex SNR; use magnitude
Handle failures gracefully: Some approximants may not work for certain mass combinations

Common Issues

Problem: "Approximant not available" error

Solution: Try time-domain instead of frequency-domain, or use different approximant

Problem: Template size mismatch

Solution: Ensure template is resized to match data length (TD) or PSD length (FD)

Problem: Poor SNR even with correct masses

Solution: Check that PSD low_frequency_cutoff matches your high-pass filter, verify data conditioning

Problem: Edge artifacts in SNR time series

Solution: Increase crop amounts or verify filtering pipeline order

Dependencies

bash

pip install pycbc numpy

References

PyCBC Tutorial: Waveform & Matched Filter - Comprehensive tutorial demonstrating waveform generation, matched filtering in both time and frequency domains, and SNR analysis
PyCBC Waveform Documentation
PyCBC Filter Documentation

Files

Just SKILL.md in tasks/gravitational-wave-detection/environment/skills/matched-filtering of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Matched Filtering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Matched Filtering

What does Matched Filtering do?

Matched filtering techniques for gravitational wave detection. Matched Filtering is an agent skill from benchflow-ai/skillsbench. Matched filtering techniques for gravitational wave detection.

When should I use Matched Filtering?

Matched Filtering fits situations like: searching for signals in detector data using template waveforms; including both time-domain and frequency-domain approaches.

How do I install Matched Filtering in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill matched-filtering -a claude-code`. Or copy the skill folder (tasks/gravitational-wave-detection/environment/skills/matched-filtering in benchflow-ai/skillsbench) into .claude/skills/matched-filtering in your project. Claude Code loads it when a task matches its description.

How do I install Matched Filtering in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill matched-filtering -a codex`. Or copy the skill folder (tasks/gravitational-wave-detection/environment/skills/matched-filtering in benchflow-ai/skillsbench) into .agents/skills/matched-filtering in your project. Codex loads it when a task matches its description.

Can I use Matched Filtering 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 matched-filtering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/matched-filtering, .gemini/skills/matched-filtering, .github/skills/matched-filtering and .opencode/skills/matched-filtering in your project.

What does Matched Filtering need to run?

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

Does Matched Filtering access the network?

SKILL.md names 2 domains. As links in the text: pycbc.org and colab.research.google.com. This is read from the text; nothing was executed.

Is Matched Filtering 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 Matched Filtering use?

Matched Filtering 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 Matched Filtering use?

About 1.6k tokens (SKILL.md is roughly 6.4k 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 Matched Filtering?

Skills that share tags, products or a category with Matched Filtering: Detecting Process Injection Techniques (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Detecting Evasion Techniques In Endpoint Logs (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Search (davila7/claude-code-templates, 32k stars) and Detecting Credential Dumping Techniques (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Matched Filtering?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 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 Matched Filtering

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

Matched Filtering for Gravitational Wave Detection

Overview

Time-Domain Waveforms

Why Cyclic Shift?

Frequency-Domain Waveforms

Key Differences: Time vs Frequency Domain

Time Domain (get_td_waveform)

Frequency Domain (get_fd_waveform)

Approximants

Matched Filter Parameters

low_frequency_cutoff

Template Resizing

Crop Amounts

Best Practices

Common Issues

Dependencies

References

Files

Compare with similar skills

Similar skills

More from benchflow-ai/skillsbench

Questions about Matched Filtering

Time Domain (`get_td_waveform`)

Frequency Domain (`get_fd_waveform`)

`low_frequency_cutoff`