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 OpenLAIR/OpenSkill --skill evo-gw-matched-filter-enginegit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-gw-matched-filter-engine)<a href="https://agentmods.dev/skills/openlair/openskill/evo-gw-matched-filter-engine"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-gw-matched-filter-engine/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/openlair/openskill/evo-gw-matched-filter-engine"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-gw-matched-filter-engine.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 | $0.00054 | $0.00681 |
| Opus 5 | $0.00027 | $0.00341 |
| Sonnet 5 | $0.00011 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
evo-gw-matched-filter-engine 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evo-gw-matched-filter-engine
Core GW data processing and matched filtering engine for PyCBC-based gravitational wave detection.
Functions
read_and_condition_data(gwf_path, channel)
Reads a GWF frame file and conditions the data:
- Read entire frame (no time bounds needed)
- Highpass filter at 15 Hz
- Crop 2s from both ends (FIR wraparound removal)
- Resample to 2048 Hz
estimate_psd(conditioned_data)
Estimates PSD using Welch's method (4s segments), interpolates to data resolution, applies inverse spectrum truncation (4s max filter, 15 Hz cutoff).
generate_template(approximant, mass1, mass2, delta_t, f_lower=20.0)
Generates a waveform template using get_td_waveform. Returns hp (plus polarization). Supported approximants: SEOBNRv4_opt, IMRPhenomD, TaylorT4.
compute_matched_filter_snr(conditioned_data, psd, hp, f_lower=20.0)
Performs matched filtering:
- Resizes template to data length
- Cyclic shifts template (hp.start_time)
- Runs matched_filter with PSD weighting
- Takes abs() of complex SNR
- Crops edges (8s start, 4s end)
- Returns (peak_snr, peak_time)
find_peak_snr(gwf_path, channel, mass1, mass2, approximant, conditioned_data=None, psd=None)
High-level function combining all steps. Accepts pre-conditioned data/PSD to avoid recomputation.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-gw-matched-filter-engine/scripts')
from utils import read_and_condition_data, estimate_psd, generate_template, compute_matched_filter_snr, find_peak_snr
# Option 1: All-in-one
snr, time = find_peak_snr('data.gwf', 'H1:TEST-STRAIN', 30, 30, 'SEOBNRv4_opt')
# Option 2: Reuse conditioned data across many templates
data = read_and_condition_data('data.gwf', 'H1:TEST-STRAIN')
psd = estimate_psd(data)
hp = generate_template('SEOBNRv4_opt', 30, 30, data.delta_t)
snr, time = compute_matched_filter_snr(data, psd, hp)
Key Parameters
- Highpass cutoff: 15 Hz
- Resample rate: 2048 Hz
- Data crop: 2s both ends
- PSD Welch segment: 4s
- Inverse spectrum truncation: 4s max filter, 15 Hz cutoff
- Template f_lower: 20 Hz
- SNR crop: 8s start, 4s end
- Only hp polarization used for single-detector filtering
What ships with it
1 file 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.
- yesterday First seen · 64 lines · 54 tokens per session scan A c9e46978990b
evo-gw-matched-filter-engine is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 681 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 2026-09-11.
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