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 wentorai/research-plugins --skill species-distribution-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/species-distribution-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/species-distribution-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/species-distribution-guide/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/wentorai/research-plugins/species-distribution-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/species-distribution-guide.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 | $0.00020 | $0.02694 |
| Opus 5 | $0.00010 | $0.01347 |
| Sonnet 5 | $0.00004 | $0.00539 |
| Haiku 4.5 | $0.00002 | $0.00269 |
Grade A, and why
species-distribution-guide scanned grade A with 1 finding 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 6d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(cmd, capture_output=True, text=True) How it starts
The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Species Distribution Modeling Guide
A skill for building and evaluating species distribution models (SDMs), covering occurrence data acquisition from biodiversity databases, environmental predictor preparation, model fitting with MaxEnt and ensemble methods, model evaluation, and projection under climate change scenarios.
Occurrence Data
Accessing GBIF Data
The Global Biodiversity Information Facility (GBIF) is the primary source of species occurrence records:
from pygbif import occurrences, species
def download_occurrences(species_name: str, country: str = None,
limit: int = 5000,
has_coordinate: bool = True) -> dict:
"""
Download species occurrence records from GBIF.
species_name: scientific name (e.g., 'Panthera tigris')
Returns cleaned occurrence records with coordinates.
"""
# Get GBIF species key
name_result = species.name_backbone(name=species_name)
if "usageKey" not in name_result:
return {"error": f"Species not found: {species_name}"}
species_key = name_result["usageKey"]
# Search occurrences
params = {
"taxonKey": species_key,
"hasCoordinate": has_coordinate,
"hasGeospatialIssue": False,
"limit": limit,
}
if country:
params["country"] = country
results = occurrences.search(**params)
# Clean records
records = []
seen_coords = set()
for rec in results.get("results", []):
lat = rec.get("decimalLatitude")
lon = rec.get("decimalLongitude")
if lat is None or lon is None:
continue
# Remove exact duplicates
coord_key = (round(lat, 4), round(lon, 4))
if coord_key in seen_coords:
continue
seen_coords.add(coord_key)
records.append({
"species": rec.get("species", species_name),
"latitude": lat,
"longitude": lon,
"year": rec.get("year"),
"basis_of_record": rec.get("basisOfRecord"),
"institution": rec.get("institutionCode"),
"country": rec.get("country"),
})
return {
"species": species_name,
"gbif_key": species_key,
"n_records": len(records),
"records": records,
}
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.
- 6d ago First seen · 344 lines · 20 tokens per session scan A c787e1dfd63b
species-distribution-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 2,694 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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