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 PKU-YuanGroup/OpenAI4S --skill bio-database-access-ortholog-inferencegit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-database-access-ortholog-inference)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-ortholog-inference"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-ortholog-inference/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/pku-yuangroup/openai4s/bio-database-access-ortholog-inference"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-ortholog-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 73 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 85 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00141 | $0.04981 |
| Opus 5 | $0.00071 | $0.02491 |
| Sonnet 5 | $0.00028 | $0.00996 |
| Haiku 4.5 | $0.00014 | $0.00498 |
Grade A, and why
bio-ortholog-inference 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python: `requests.get()` against REST endpoints; `pandas` for parsing How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+; OrthoDB v12 API, Ensembl REST (Ensembl release 112+), OMA REST API, eggNOG 6.0+, PANTHER v18+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show requests pandas - API surface: confirm endpoint URLs and JSON schema match the current API docs
If endpoints return 404 or unexpected JSON, check release notes for the resource; schema migrations happen with each major version (Ensembl release is the biggest moving target).
Ortholog Inference (Database Access)
"What is the X ortholog of gene Y?" -> Many ortholog resources have already done the inference at scale. Pulling their answers is faster and often more reliable than re-computing. This skill is the database-access view: how to query the major orthology resources programmatically, what their confidence semantics mean, and when their disagreements matter.
For de novo orthology inference (running OrthoFinder, SonicParanoid, OMA standalone on local proteomes), see comparative-genomics/ortholog-inference — that's a much deeper treatment of the computational side.
This skill is about pulling answers from:
-
OrthoDB v12 — broadest coverage (1700+ species), levels from species-specific to deep
-
Ensembl Compara — vertebrate-focused, tree-reconciled, confidence scores
-
OMA browser — high precision, HOG (Hierarchical Orthologous Group) framework
-
eggNOG 6.0 — pre-computed functional groups, deepest functional annotation
-
PANTHER — protein family + ortholog calls with experimentally validated curation
-
KEGG Orthology (KO) — pathway-centric orthologous functional units
-
HomoloGene — deprecated since 2014, but data still queryable for legacy comparison
-
Python:
requests.get()against REST endpoints;pandasfor parsing -
CLI:
curlagainst the same endpoints; OrthoDB also has bulk downloads
Required Setup
import requests
import pandas as pd
import time
What ships with it
4 files 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 · 379 lines · 141 tokens per session scan A e7d96fa8a3a1
bio-ortholog-inference is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 141 tokens to every session and 4,981 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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