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Agent skill

Variant Functional Prediction

by InternScience in InternScience/scp

Query FAVOR API for variant functional prediction scores (CADD, SIFT, PolyPhen, REVEL, etc.) and gene annotation.

MITAuto-check passed

Install Variant Functional Prediction

skills CLI
$ npx skills add InternScience/scp --skill variant-functional-prediction -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install InternScience/scp variant-functional-prediction --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/variant-functional-prediction .claude/skills/variant-functional-prediction && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
variant-functional-prediction
GitHub stars
169
Used in
1 other repo
Token cost
~422 tokens
SKILL.md length
8 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Query FAVOR API for variant functional prediction scores (CADD, SIFT, PolyPhen, REVEL, etc.) and gene annotation.

  • Reaches api.genohub.org

What it does

Variant Functional Prediction is an agent skill from InternScience/scp. Query FAVOR API for variant functional prediction scores (CADD, SIFT, PolyPhen, REVEL, etc.) and gene annotation.

Its SKILL.md is about 420 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is MIT.

Example prompts

  • “/variant-functional-prediction”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are tex and python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.genohub.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Variant Functional Prediction loads about 422 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 8 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~422

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 8 words, ~422 tokens.

Download SKILL.mdSave it as .claude/skills/variant-functional-prediction/SKILL.md (or your agent's skills folder).
name
variant-functional-prediction
description
Query FAVOR API for variant functional prediction scores (CADD, SIFT, PolyPhen, REVEL, etc.) and gene annotation.
license
MIT license
metadata.skill-author
PJLab

FAVOR Functional Prediction

Usage

Tool Description
tex
Query FAVOR (GenoHub) API to get variant functional prediction scores.
API: GET https://api.genohub.org/v1/rsids/{rs_id}
Note: Returns a JSON list; use first element.
Args:
    rs_id (str): dbSNP rsID (e.g. "rs7412")
Return:
    Gene name, exonic category, functional prediction scores
    (CADD, SIFT, PolyPhen2, MutationTaster, GERP, etc.)
Query Example
python
import requests

rs_id = "rs7412"
url = f"https://api.genohub.org/v1/rsids/{rs_id}"
resp = requests.get(url, timeout=30).json()
d = resp[0] if isinstance(resp, list) else resp

print(f"[FAVOR] variant: {d.get('variant_vcf')}")
print(f"[FAVOR] 基因: {d.get('genecode_comprehensive_info')}")
print(f"[FAVOR] 区域: {d.get('genecode_comprehensive_category')}")
print(f"[FAVOR] 外显子变异类别: {d.get('genecode_comprehensive_exonic_category')}")
print(f"[FAVOR] CADD phred: {d.get('cadd_phred')} (>20=有害)")
print(f"[FAVOR] SIFT: {d.get('sift_cat')} (val={d.get('sift_val')})")
print(f"[FAVOR] PolyPhen2 HDIV: {d.get('polyphen2_hdiv_score')}")
print(f"[FAVOR] PolyPhen2 HVAR: {d.get('polyphen2_hvar_score')}")
print(f"[FAVOR] MutationTaster: {d.get('mutation_taster_score')}")
print(f"[FAVOR] MutationAssessor: {d.get('mutation_assessor_score')}")
print(f"[FAVOR] MetaSVM pred: {d.get('metasvm_pred')}")
print(f"[FAVOR] GERP_N: {d.get('gerp_n')}, GERP_S: {d.get('gerp_s')}")
print(f"[FAVOR] BRAVO AF: {d.get('bravo_af')}")

© InternScience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/variant-functional-prediction of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Variant Functional Prediction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Variant Functional Prediction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Variant Functional Prediction this skillInternScience/scp1691 repos~422Automated safety check: PassMIT
Alphagenome Variant Impact Scoregoogle-deepmind/science-skills3.2k—~4.2kAutomated safety check: NotesApache-2.0
Bio Microbiome Functional PredictionGPTomics/bioSkills1.2k1 repos~5.3kAutomated safety check: PassMIT
Bio Splice Variant PredictionGPTomics/bioSkills1.2k2 repos~6.4kAutomated safety check: PassMIT
Footballbin Predictionsdavila7/claude-code-templates32k—~634Automated safety check: PassMIT
Harness Scoreruvnet/ruflo74k—~605Automated safety check: NotesMIT

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  • Biomedical Web Search

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Questions about Variant Functional Prediction

What does Variant Functional Prediction do?

Query FAVOR API for variant functional prediction scores (CADD, SIFT, PolyPhen, REVEL, etc.) and gene annotation. Variant Functional Prediction is an agent skill from InternScience/scp.) and gene annotation.

How do I install Variant Functional Prediction in Claude Code?

Run `npx skills add InternScience/scp --skill variant-functional-prediction -a claude-code`. Or copy the skill folder (skills/variant-functional-prediction in InternScience/scp) into .claude/skills/variant-functional-prediction in your project. Claude Code loads it when a task matches its description.

How do I install Variant Functional Prediction in Codex?

Run `npx skills add InternScience/scp --skill variant-functional-prediction -a codex`. Or copy the skill folder (skills/variant-functional-prediction in InternScience/scp) into .agents/skills/variant-functional-prediction in your project. Codex loads it when a task matches its description.

Can I use Variant Functional Prediction in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add InternScience/scp --skill variant-functional-prediction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/variant-functional-prediction, .gemini/skills/variant-functional-prediction, .github/skills/variant-functional-prediction and .opencode/skills/variant-functional-prediction in your project.

What does Variant Functional Prediction need to run?

SKILL.md names no scripts, command-line tools or credentials: Variant Functional Prediction is instructions for the agent only. Our summary lists: Python 3.

Does Variant Functional Prediction access the network?

SKILL.md names 1 domain. In commands or code: api.genohub.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Variant Functional Prediction safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Variant Functional Prediction use?

Variant Functional Prediction is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Variant Functional Prediction use?

About 422 tokens (SKILL.md is roughly 1.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Variant Functional Prediction?

Skills that share tags, products or a category with Variant Functional Prediction: Alphagenome Variant Impact Score (google-deepmind/science-skills, 3.2k stars), Bio Microbiome Functional Prediction (GPTomics/bioSkills, 1.2k stars), Bio Splice Variant Prediction (GPTomics/bioSkills, 1.2k stars) and Footballbin Predictions (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Variant Functional Prediction?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 169 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.