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Bio Database Evidence

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foryourhealth111-pixel
bio-database-evidence

Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.

Overview

Publisherforyourhealth111-pixel
RepositoryVibe-Skills
Skill namebio-database-evidence
Stars
3.3K
Forks
288
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by foryourhealth111-pixel on GitHub. Read the source before you install it.

Installation

Install the Bio Database Evidence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills.git /tmp/Vibe-Skills
mkdir -p .claude/skills
cp -r /tmp/Vibe-Skills/bundled/skills/bio-database-evidence .claude/skills/bio-database-evidence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Database Evidence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bio Database Evidence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bio Database Evidence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Bio Database Evidence

Use This Skill For

Use this skill when the main task is biological database lookup, annotation, or evidence gathering across one or more biological sources:

  • Gene annotation, identifiers, RefSeq, Ensembl IDs, orthologs, VEP, GO, and genomic coordinates.
  • Variant clinical significance, VUS interpretation support, ClinVar review status, cancer mutations, and COSMIC evidence.
  • GWAS Catalog trait associations, rs IDs, p-values, summary statistics, and genetic epidemiology evidence.
  • Pathway mapping, ID conversion, KEGG pathways, Reactome enrichment, disease pathways, and pathway evidence.
  • Target-disease association evidence, tractability, safety, known drugs, and Open Targets evidence.
  • Protein structure evidence from AlphaFold DB or RCSB PDB, including UniProt IDs, mmCIF/PDB downloads, pLDDT, PAE, and structure metadata.
  • Protein-protein interaction evidence, STRING networks, hub proteins, and enrichment evidence.
  • Reference single-cell data lookup from CELLxGENE Census when the user asks for census metadata or expression data, not full downstream analysis.
  • Cross-database biological ID mapping and evidence tables across multiple resources.

Do Not Use This Skill For

  • Single-cell RNA-seq analysis, clustering, UMAP, marker genes, cell annotation, AnnData/h5ad container editing, or scVI/scANVI batch-correction planning. Use scanpy.
  • Bulk RNA-seq differential expression. Use pydeseq2.
  • BAM, SAM, CRAM, VCF, pileup, coverage, or region extraction as a primary file-processing task.
  • deepTools signal-track processing and heatmaps.
  • Protein language models, embeddings, inverse folding, or protein-design workflows.
  • Constraint-based metabolic modeling, FBA, or metabolic-engineering simulation.
  • BED/genomic interval embeddings, genomic-region ML, or gene regulatory network inference.
  • FCS or flow-cytometry file parsing.

Workflow

  1. Identify the biological entity type: gene, transcript, variant, pathway, target, protein structure, protein interaction, trait association, or reference cell population.
  2. Pick the narrowest source that answers the evidence question.
  3. Preserve source names, query terms, access dates, identifiers, and API caveats in the result.
  4. Return evidence in a table when comparing multiple sources.
  5. State when authentication, license, rate limits, or non-public access restricts a source.

Source Guide

See references/database-evidence-sources.md for source-specific boundaries and query notes.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bio Database Evidence AI skill do?

Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.

Why use Bio Database Evidence on TypingMind?

Because you install it once and use it with any model. Bio Database Evidence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bio Database Evidence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/main/bundled/skills/bio-database-evidence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bio Database Evidence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bio Database Evidence?

As many as you like. As long as a model supports skills, you can use Bio Database Evidence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bio Database Evidence AI skill free?

Yes. It is published on GitHub by foryourhealth111-pixel under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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