# target-prioritization

The skill builds a per-gene dossier from UniProt, OpenTargets, PubMed, HPA and ChEMBL, then re-ranks your candidate genes by a composite score.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2900 installs · ★ 92
- Author: Agents365-ai
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/Agents365-ai/365-skills/tree/HEAD/plugins/target-prioritization/skills/target-prioritization/SKILL.md
- Updated: 2026-10-16
- HTML: https://affpapa.org/en/nevibecode/skills/target-prioritization

## What it does

Feed it a ranked gene list — a CSV with a `gene` column, a plain text file with one symbol per line, or symbols inline in your message — and the skill queries five sources per gene. UniProt returns protein localization, surface and MHC flags. OpenTargets brings tractability, approved drugs and disease associations, plus DepMap CRISPR essentiality and gnomAD constraint. PubMed counts papers, HPA covers tissue and single-cell specificity, ChEMBL surfaces tool compounds with pIC50 and IC50.

Two scripts do the work. `orchestrate.py` runs the five fetchers in parallel threads and writes raw responses to `raw_data/<source>.json`. Then `aggregate.py` merges them, computes the composite score from the weights in `weights.yaml`, and emits `targets_summary.csv` along with a `targets_report.md` skeleton where the rationale fields sit blank. The agent fills those in, 2–3 sentences per gene: strongest evidence, main risk, suggested next step. The last step is `build_html_report.py`, which produces an interactive report with a sortable table and per-gene cards.

You need `python3` and `curl` in the environment, or the pipeline will not run. Weights can be overridden with `--weights`, and `--top` caps the dossier at the first N input genes, 50 by default. Note that ChEMBL contributes dossier columns only — it carries no score component. If your environment cannot reach external APIs, the fetches fail, so check network access before you start.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Agents365-ai/365-skills --skill target-prioritization -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/target-prioritization/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/target-prioritization/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Agents365-ai/365-skills --skill target-prioritization -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/target-prioritization/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Agents365-ai/365-skills --skill target-prioritization -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/target-prioritization/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Agents365-ai/365-skills target-prioritization
```

2. **Manually: put the files in place**
- File: `.github/skills/target-prioritization/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Agents365-ai/365-skills --skill target-prioritization -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Agents365-ai/365-skills --path plugins/target-prioritization/skills/target-prioritization
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/target-prioritization/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Agents365-ai/365-skills --skill target-prioritization -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Agents365-ai/365-skills/tree/HEAD/plugins/target-prioritization/skills/target-prioritization/SKILL.md

## When to use it
- re-rank candidate genes after a DE analysis by a combined score
- build a per-gene dossier with protein localization, tractability and approved drugs
- filter genes by CRISPR essentiality and gnomAD constraint
- find ChEMBL tool compounds for the next experiment
- judge therapeutic window from HPA tissue specificity

## Example prompts
- here's my DE output CSV, rank these genes for drug discovery
- make a target dossier for these candidates and suggest next steps
- filter these genes for druggability and build a target report

## Similar
- [chembl-database](https://affpapa.org/en/nevibecode/skills/chembl-database.md) — skill · 2200 installs
- [tooluniverse-target-research](https://affpapa.org/en/nevibecode/skills/tooluniverse-target-research.md) — skill · 409 installs
- [opentargets-database](https://affpapa.org/en/nevibecode/skills/opentargets-database.md) — skill · 2200 installs
- [epigraphdb-skill](https://affpapa.org/en/nevibecode/skills/epigraphdb-skill.md) — skill · 13 installs
- [alphafold-skill](https://affpapa.org/en/nevibecode/skills/alphafold-skill.md) — skill · 14 installs

From the same set: [agents365-ai/365-skills](https://affpapa.org/en/nevibecode/by/agents365-ai/365-skills.md) — 13 cards

---
NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md