Elliott Wave Signal Engine
HKUDS/Vibe-Trading
Detects Elliott Wave structures in price data with a Zigzag swing finder and Fibonacci checks, and turns completed waves into long, short or flat signals.
Data conditioning techniques for gravitational wave detector data.
Apache-2.0Auto-check passed
Install Conditioning
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench conditioning --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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/conditioning .claude/skills/conditioning && rm -rf skills-srcUse ~/.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/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .claude/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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 install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioningType 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.
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench conditioning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/conditioning .agents/skills/conditioning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .agents/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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.
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench conditioning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/conditioning .cursor/skills/conditioning && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .cursor/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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 https://github.com/benchflow-ai/skillsbench.git --path tasks/gravitational-wave-detection/environment/skills/conditioning--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench conditioning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/conditioning .gemini/skills/conditioning && rm -rf skills-srcUse ~/.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/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .gemini/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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.
$ gh skill install benchflow-ai/skillsbench conditioningInstalls 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).
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ 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/conditioning .github/skills/conditioning && rm -rf skills-srcUse ~/.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/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .github/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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.
$ npx skills add benchflow-ai/skillsbench --skill conditioning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench conditioning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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/conditioning .opencode/skills/conditioning && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "conditioning" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/gravitational-wave-detection/environment/skills/conditioning into .opencode/skills/conditioning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conditioning", 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
conditioning- GitHub stars
- 1.8k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 376 words
- Files
- 1
- Skills in repo
- 178
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Data conditioning techniques for gravitational wave detector data.
- Works in 4 steps: High-pass filtering (remove… → Resampling (downsample to appropriate… → Crop filter wraparound (remove edge… → …
- Preprocessing raw detector strain data before matched filtering
- SKILL.md covers Overview, High-Pass Filtering, Resampling and Crop Filter Wraparound, plus 5 more sections
- Calls pip
What it does
Conditioning is an agent skill from benchflow-ai/skillsbench. Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries data.
Its SKILL.md is about 1.3k 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
- Preprocessing raw detector strain data before matched filtering
- Including high-pass filtering
- Removing filter wraparound artifacts
- Estimating power spectral density (PSD)
Example prompts
- “/conditioning”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- High-pass filtering (remove low-frequency noise below ~15 Hz)
- Resampling (downsample to appropriate sampling rate)
- Crop filter wraparound (remove edge artifacts from filtering)
- PSD estimation (calculate power spectral density for matched filtering)
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.orgcolab.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
Conditioning loads about 1.3k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 376 words of instructions outside code blocks.
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). 376 words, ~1,344 tokens.
Download SKILL.mdSave it as
.claude/skills/conditioning/SKILL.md (or your agent's skills folder).- name
- conditioning
- description
- Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries data.
Gravitational Wave Data Conditioning
Data conditioning is essential before matched filtering. Raw gravitational wave detector data contains low-frequency noise, instrumental artifacts, and needs proper sampling rates for computational efficiency.
Overview
The conditioning pipeline typically involves:
- High-pass filtering (remove low-frequency noise below ~15 Hz)
- Resampling (downsample to appropriate sampling rate)
- Crop filter wraparound (remove edge artifacts from filtering)
- PSD estimation (calculate power spectral density for matched filtering)
High-Pass Filtering
Remove low-frequency noise and instrumental artifacts:
from pycbc.filter import highpass
# High-pass filter at 15 Hz (typical for LIGO/Virgo data)
strain_filtered = highpass(strain, 15.0)
# Common cutoff frequencies:
# 15 Hz: Standard for ground-based detectors
# 20 Hz: Higher cutoff, more aggressive noise removal
# 10 Hz: Lower cutoff, preserves more low-frequency contentWhy 15 Hz? Ground-based detectors like LIGO/Virgo have significant low-frequency noise. High-pass filtering removes this noise while preserving the gravitational wave signal (typically >20 Hz for binary mergers).
Resampling
Downsample the data to reduce computational cost:
from pycbc.filter import resample_to_delta_t
# Resample to 2048 Hz (common for matched filtering)
delta_t = 1.0 / 2048
strain_resampled = resample_to_delta_t(strain_filtered, delta_t)
# Or to 4096 Hz for higher resolution
delta_t = 1.0 / 4096
strain_resampled = resample_to_delta_t(strain_filtered, delta_t)
# Common sampling rates:
# 2048 Hz: Standard, computationally efficient
# 4096 Hz: Higher resolution, better for high-mass systemsNote: Resampling should happen AFTER high-pass filtering to avoid aliasing. The Nyquist frequency (half the sampling rate) must be above the signal frequency of interest.
Crop Filter Wraparound
Remove edge artifacts introduced by filtering:
# Crop 2 seconds from both ends to remove filter wraparound
conditioned = strain_resampled.crop(2, 2)
# The crop() method removes time from start and end:
# crop(start_seconds, end_seconds)
# Common values: 2-4 seconds on each end
# Verify the duration
print(f"Original duration: {strain_resampled.duration} s")
print(f"Cropped duration: {conditioned.duration} s")Why crop? Digital filters introduce artifacts at the edges of the time series. These artifacts can cause false triggers in matched filtering.
Power Spectral Density (PSD) Estimation
Calculate the PSD needed for matched filtering:
from pycbc.psd import interpolate, inverse_spectrum_truncation
# Estimate PSD using Welch's method
# seg_len: segment length in seconds (typically 4 seconds)
psd = conditioned.psd(4)
# Interpolate PSD to match data frequency resolution
psd = interpolate(psd, conditioned.delta_f)
# Inverse spectrum truncation for numerical stability
# This limits the effective filter length
psd = inverse_spectrum_truncation(
psd,
int(4 * conditioned.sample_rate),
low_frequency_cutoff=15
)
# Check PSD properties
print(f"PSD length: {len(psd)}")
print(f"PSD delta_f: {psd.delta_f}")
print(f"PSD frequency range: {psd.sample_frequencies[0]:.2f} - {psd.sample_frequencies[-1]:.2f} Hz")PSD Parameters Explained
- Segment length (4 seconds): Longer segments give better frequency resolution but fewer averages. 4 seconds is a good balance.
- Low frequency cutoff (15 Hz): Should match your high-pass filter cutoff. Frequencies below this are not well-characterized.
Show full SKILL.md (155 more words)Show less
Best Practices
- Always high-pass filter first: Remove low-frequency noise before resampling
- Choose appropriate sampling rate: 2048 Hz is standard, 4096 Hz for high-mass systems
- Crop enough time: 2 seconds is minimum, but may need more for longer templates
- Match PSD cutoff to filter: PSD low-frequency cutoff should match high-pass filter frequency
- Verify data quality: Plot the conditioned strain to check for issues
Dependencies
pip install pycbcReferences
- PyCBC Tutorial: Waveform & Matched Filter - Practical tutorial covering data conditioning, PSD estimation, and matched filtering
- PyCBC Filter Documentation
- PyCBC PSD Documentation
Common Issues
Problem: PSD estimation fails with "must contain at least one sample" error
- Solution: Ensure data is long enough after cropping (need several segments for Welch method)
Problem: Filter wraparound artifacts in matched filtering
- Solution: Increase crop amount or check that filtering happened before cropping
Problem: Poor SNR due to low-frequency noise
- Solution: Increase high-pass filter cutoff frequency or check PSD inverse spectrum truncation
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/gravitational-wave-detection/environment/skills/conditioning of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Conditioning 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Conditioning this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Elliott Wave Signal EngineHKUDS/Vibe-Trading | 35k | — | ~482 | Automated safety check: Pass | MIT | |
| Exploiting Race Condition Vulnerabilitiesmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Hunt Exceptional Conditionssickn33/agentic-awesome-skills | 47k | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Hunt Race Conditionsickn33/agentic-awesome-skills | 47k | 1 repos | ~5.7k | Automated safety check: Pass | MIT | |
| Mapping Mitre Attack Techniquesmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 |
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Hunt Exceptional Conditions
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Questions about Conditioning
What does Conditioning do?
Data conditioning techniques for gravitational wave detector data. Conditioning is an agent skill from benchflow-ai/skillsbench. Data conditioning techniques for gravitational wave detector data.
When should I use Conditioning?
Conditioning fits situations like: preprocessing raw detector strain data before matched filtering; including high-pass filtering; removing filter wraparound artifacts; estimating power spectral density (PSD).
How do I install Conditioning in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill conditioning -a claude-code`. Or copy the skill folder (tasks/gravitational-wave-detection/environment/skills/conditioning in benchflow-ai/skillsbench) into .claude/skills/conditioning in your project. Claude Code loads it when a task matches its description.
How do I install Conditioning in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill conditioning -a codex`. Or copy the skill folder (tasks/gravitational-wave-detection/environment/skills/conditioning in benchflow-ai/skillsbench) into .agents/skills/conditioning in your project. Codex loads it when a task matches its description.
Can I use Conditioning 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 conditioning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/conditioning, .gemini/skills/conditioning, .github/skills/conditioning and .opencode/skills/conditioning in your project.
What does Conditioning need to run?
Going by SKILL.md and its folder, Conditioning needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Conditioning 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 Conditioning 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 Conditioning use?
Conditioning 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 Conditioning use?
About 1.3k tokens (SKILL.md is roughly 5.4k 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 Conditioning?
Skills that share tags, products or a category with Conditioning: Elliott Wave Signal Engine (HKUDS/Vibe-Trading, 35k stars), Exploiting Race Condition Vulnerabilities (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Hunt Exceptional Conditions (sickn33/agentic-awesome-skills, 47k stars) and Hunt Race Condition (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Conditioning?
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.