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 Zhang-Henry/CoEvoSkills --skill evo-seismic-pickergit clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkillsWrote 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/zhang-henry/coevoskills/evo-seismic-picker)<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-seismic-picker"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-seismic-picker/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/zhang-henry/coevoskills/evo-seismic-picker"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-seismic-picker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.00615 |
| Opus 5 | $0.00030 | $0.00308 |
| Sonnet 5 | $0.00012 | $0.00123 |
| Haiku 4.5 | $0.00006 | $0.00061 |
Grade A, and why
evo-seismic-picker 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evo-seismic-picker
Seismic phase picking skill using PhaseNet (SeisBench) for P and S wave arrival detection.
Approach
- Load each trace's 3-component waveform data
- Reorder channels to ZNE orientation
- Handle single-channel data by replicating active channel to all 3 components
- Normalize data by dividing by max absolute value (critical for PhaseNet)
- Create ObsPy Stream with proper metadata
- Auto-calibrate probability thresholds from a data sample (or accept caller-supplied)
- Run PhaseNet classify() to get P and S picks
- Select best picks per trace (highest probability, S must follow P)
- For files where PhaseNet finds no picks, fall back to metadata-based travel time computation
- Write results to CSV with file_name, phase, pick_idx columns
Key Insights
- PhaseNet requires normalized data to produce picks (raw physical units are extremely small)
- Single-channel stations need signal replicated to all 3 components
- Accelerometer data works fine without integration when normalized
- Probability thresholds are calibrated from data sample, not hardcoded
- Metadata fallback uses caller-supplied Vp and Vp/Vs ratio with hypocentral distance
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-seismic-picker/scripts')
from utils import process_all_files
# Run full pipeline - thresholds auto-calibrated from data
df = process_all_files('/root/data/', '/root/results.csv')
# Or supply explicit thresholds and velocity model
df = process_all_files('/root/data/', '/root/results.csv',
p_threshold=0.3, s_threshold=0.3,
vp=6.0, vs_ratio=1.73)
Key Functions
load_trace(filepath)- Load npz trace file with metadataprepare_zne_normalized(data, channels)- Reorder to ZNE, handle single-channel, normalizemake_obspy_stream(zne_data, dt, start_time_str)- Convert to ObsPy Streamcalibrate_thresholds(data_dir, model, sample_size)- Derive thresholds from datametadata_based_pick(trace_info, vp, vs_ratio)- Compute P/S indices from metadatapick_with_phasenet(stream, model, dt, start_time_str, p_threshold, s_threshold)- Run PhaseNetselect_best_picks(picks)- Select best P and S from candidatesprocess_all_files(data_dir, output_csv, ...)- Full end-to-end pipeline
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.
- 9d ago First seen · 54 lines · 61 tokens per session scan A 52cda2edf548
evo-seismic-picker is a skill published in the GitHub repository Zhang-Henry/CoEvoSkills (66 stars, last pushed 22d ago), licensed Apache-2.0. It adds 61 tokens to every session and 615 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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