species-distribution-guide

species-distribution-guide is a skill for Claude Code, Codex from wentorai/research-plugins. It costs 20 tokens per session (2,694 once invoked), scanned A, original, MIT.

A guide to species distribution modeling, which estimates where species may occur using observations, environmental conditions, and statistical models. It uses biodiversity records from GBIF and methods such as MaxEnt.

In plain words
What is it for?
Use it to study species ranges, assess climate-change scenarios, support conservation planning, or analyze environmental impacts.
Why use it?
It brings the steps for cleaning species observations, preparing environmental data, fitting models, and checking their results into one workflow.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to study species ranges, assess climate-change scenarios, support conservation planning, or analyze environmental impacts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wentorai/research-plugins/species-distribution-guide
Install

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.

Any agent
npx skills add wentorai/research-plugins --skill species-distribution-guide
Clone the repo
git clone --depth 1 https://github.com/wentorai/research-plugins

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for species-distribution-guide

README.md
[![agentmods](https://agentmods.dev/badge/skills/wentorai/research-plugins/species-distribution-guide/github.svg)](https://agentmods.dev/skills/wentorai/research-plugins/species-distribution-guide)
Your own site
<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.

agentmods 80×15 button for species-distribution-guide

Your own site · 80×15
<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>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,694 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 6d ago against content hash c787e1dfd63b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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)
skills/domains/ecology/species-distribution-guide/SKILL.md · 344 lines

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,
    }

Read the full file on GitHub · 344 lines

Changes

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.

  1. 6d ago First seen · 344 lines · 20 tokens per session scan A c787e1dfd63b

Subscribe to this mod's changes

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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