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-lake-data-pipelinegit 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-lake-data-pipeline)<a href="https://agentmods.dev/skills/openlair/openskill/evo-lake-data-pipeline"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-lake-data-pipeline/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-lake-data-pipeline"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-lake-data-pipeline.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 | $0.00038 | $0.00489 |
| Opus 5 | $0.00019 | $0.00244 |
| Sonnet 5 | $0.00008 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
evo-lake-data-pipeline 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.
What it actually says
evo-lake-data-pipeline
Loads and merges four CSV datasets (water_temperature, climate, land_cover, hydrology) into analysis-ready DataFrames.
Category Mapping
| Category | Variables |
|---|---|
| Heat | AirTempLake, Shortwave, Longwave |
| Flow | Precip, Outflow, Inflow |
| Wind | WindSpeedLake |
| Human | DevelopedArea, AgricultureArea |
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-lake-data-pipeline/scripts')
from utils import load_all_datasets, merge_datasets, get_feature_columns, get_feature_columns_by_category, get_category_map
water_temp, climate, land_cover, hydrology = load_all_datasets('/root/data/')
merged = merge_datasets(water_temp, climate, land_cover, hydrology)
features = get_feature_columns()
cat_map = get_category_map()
Key Functions
load_all_datasets(data_dir)— loads all 4 CSVs, returns tuple of DataFramesmerge_datasets(wt, clim, lc, hydro)— inner-joins on Year columnget_feature_columns()— returns list of 9 predictor column namesget_feature_columns_by_category()— returns dict of category -> [features]get_category_map()— returns dict of feature_name -> category_name
Import Pattern (avoiding naming conflicts)
When using multiple skills that each have utils.py, use importlib to avoid conflicts:
import importlib.util
def load_module(name, path):
spec = importlib.util.spec_from_file_location(name, path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
return mod
data_utils = load_module('data_utils', '/app/environment/skills/evo-lake-data-pipeline/scripts/utils.py')
trend_utils = load_module('trend_utils', '/app/environment/skills/evo-lake-trend-analysis/scripts/utils.py')
factor_utils = load_module('factor_utils', '/app/environment/skills/evo-lake-factor-attribution/scripts/utils.py')
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 · 57 lines · 38 tokens per session scan A b9c3ee17a92a
evo-lake-data-pipeline is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 38 tokens to every session and 489 once invoked, about $0.0002 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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