# unibind-database

This skill queries UniBind for experimentally validated transcription-factor binding sites and downloads BED or FASTA coordinates for local analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2100 installs · ★ 3329
- Author: google-deepmind
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/google-deepmind/science-skills/tree/HEAD/skills/unibind_database/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/unibind-database

## What it does

unibind-database queries UniBind for direct TF-DNA interaction data. It can list species, collections, cell lines, transcription factors, and datasets, filter datasets by supplied fields, show details for a selected dataset, and download TFBS files as BED or FASTA.

The skill runs `scripts/unibind_api.py` through `uv run`; the wrapper handles the required request rate limit. Large responses can be narrowed with `jq` or `uvx --from jmespath jp`, while `--output` saves query results and `--page` with `--page-size` splits long lists.

\`uv` must be installed and available on PATH. On first use, the workflow also creates `.licenses/unibind_database_LICENSE.txt` after notifying the user about the UniBind terms. This is the point to check your plan: the skill does not query specific genes, genomic intervals, motif models, or expression data, so it is the wrong tool for those requests.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add google-deepmind/science-skills --skill unibind-database -a claude-code
```
```bash
npx skills add google-deepmind/science-skills --skill unibind_database -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add google-deepmind/science-skills --skill unibind_database -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add google-deepmind/science-skills --skill unibind_database -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install google-deepmind/science-skills unibind_database
```

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

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add google-deepmind/science-skills --skill unibind_database -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/google-deepmind/science-skills --path skills/unibind_database
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add google-deepmind/science-skills --skill unibind_database -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/google-deepmind/science-skills/tree/HEAD/skills/unibind_database/SKILL.md

## When to use it
- find datasets by species, TF, or cell line
- inspect details for a UniBind dataset
- download TFBS coordinates as BED or FASTA
- list available species and collections
- filter datasets by data source or p-value

## Example prompts
- Use the `unibind-database` skill to find `Homo sapiens` datasets for TF `CTCF` from `ENCODE`.
- Use `unibind-database` to list available cell lines and extract their names with `uvx --from jmespath jp`.
- Find `EXP047889.HMLE-Twist-ER_breast_cancer.SMAD3` with `unibind-database` and download its TFBS files to `/tmp/tfbs` in `bed` format.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8db0708d136e809a.

BEGIN THIRD-PARTY FILE 8db0708d136e809a
````text
---
name: unibind-database
description: >-
  Queries the UniBind database for experimentally validated transcription factor
  (TF) binding sites. Use when retrieving direct TF-DNA interaction datasets,
  downloading binding site coordinates (BED/FASTA) for local analysis, or
  listing available datasets by species, cell line, or TF name. Don't use to
  query specific intervals, locations, genes, motif models or expression
  data.
---

# UniBind Database Skill

UniBind is a database of direct TF–DNA interactions across 9 species,
integrating ChIP-seq peaks with JASPAR TF binding profiles via the DAMO
framework.

## Prerequisites

1.  **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure
    `uv` is installed and on PATH.
2.  **User Notification**: If .licenses/unibind_database_LICENSE.txt does not
    already exist in the workspace root directory then (1) prominently notify
    the user to check the terms at https://unibind.uio.no/ and
    https://unibind.uio.no/api/overview, then (2) create the file recording the
    notification text and timestamp.

## Quick Start

Query commands print JSON to stdout by default. Most outputs are small enough to
read directly. For large outputs (`list_cell_lines`, `list_tfs`), pipe through
`jq` to extract only the fields you need.

```bash
uv run <SKILL DIR>/scripts/unibind_api.py list_species
```

The `download_tfbs` command writes BED/FASTA files to `--output-dir` instead.
You may optionally use `--output <path>` on any query command to save results to
a file if needed.

## Core Rules

-   **Use the Wrapper**: ALWAYS execute the provided helper scripts to query the
    database rather than accessing the database directly. The scripts
    automatically enforce the required rate limit gracefully.
-   **Output**: Query commands print JSON to stdout. Most responses are compact
    and can be read directly.
-   **Large Results**: `list_cell_lines` and `list_tfs` produce large output.
    Pipe these through `jq` to extract specific fields rather than reading the
    full output into context.
-   **Saving to File**: Use `--output <path>` when you need to reference the
    data later or when processing very large results with `jq`.
-   **Pagination**: Use `--page` and `--page-size` (max 1000) to chunk large
    result sets.
-   **Ordering**: Use `--order field_name` (prefix with `-` for descending) on
    any list command.
-   **Notification**: If this skill is used, ensure this is mentioned in the
    output.

## Utility Scripts
````
END THIRD-PARTY FILE 8db0708d136e809a

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First 2524 of 5311 bytes. © google-deepmind · Apache-2.0 · https://github.com/google-deepmind/science-skills/tree/HEAD/skills/unibind_database/SKILL.md

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