Native Subtitle Quote Image
chengyi-ai/native-subtitle-quote-image
将本地视频或用户有权处理的在线视频，经过来源获取、文字稿定位、选题选句、精确取帧、紧凑裁切、拼图和逐张质检，制作成 3:4 或保留画面原比例的视频字幕长图。支持两种明确分开的输出：保留画面内已烧录字幕的原生字幕模式，以及把已审核的时间点与台词绘制到真实视频帧上的脚本字幕模式。用户要求原生字幕截图、字幕帧拼图、YouTube…
Queries the UniBind database for experimentally validated transcription factor (TF) binding sites.
Install Unibind Database
$ npx skills add google-deepmind/science-skills --skill unibind-database -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills unibind-database --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/unibind_database .claude/skills/unibind-database && rm -rf skills-srcUse ~/.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/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .claude/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_databaseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google-deepmind/science-skills --skill unibind-database -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills unibind-database --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/unibind_database .agents/skills/unibind-database && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .agents/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill unibind-database -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills unibind-database --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/unibind_database .cursor/skills/unibind-database && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .cursor/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google-deepmind/science-skills.git --path skills/unibind_database--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google-deepmind/science-skills --skill unibind-database -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills unibind-database --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/unibind_database .gemini/skills/unibind-database && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .gemini/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google-deepmind/science-skills unibind-databaseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google-deepmind/science-skills --skill unibind-database -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/unibind_database .github/skills/unibind-database && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .github/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google-deepmind/science-skills --skill unibind-database -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google-deepmind/science-skills unibind-database --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google-deepmind/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/unibind_database .opencode/skills/unibind-database && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "unibind-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/unibind_database into .opencode/skills/unibind-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unibind-database", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
unibind-database- GitHub stars
- 3.2k
- Used in
- 1 other repo
- Token cost
- ~1.3k tokens
- SKILL.md length
- 519 words
- Files
- 3 (incl. scripts, references)
- Skills in repo
- 40
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Queries the UniBind database for experimentally validated transcription factor (TF) binding sites.
- Works in 6 steps: List Species → List Collections → List Cell Lines & TFs (large output —… → …
- Retrieving direct TF-DNA interaction datasets
- SKILL.md covers Prerequisites, Quick Start, Core Rules and Utility Scripts, plus 1 more section
- Runs Python scripts from its folder; calls uv and uvx
What it does
Unibind Database is an agent skill from google-deepmind/science-skills. 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.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `scripts/unibind_api.py`).
It sits in Media & Creative, covering Transcription. The repository describes itself as: GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other… The licence is Apache-2.0.
When your agent uses it
- Retrieving direct TF-DNA interaction datasets
- Downloading binding site coordinates (BED/FASTA) for local analysis
- Listing available datasets by species
- Query specific intervals
Example prompts
- “Use the unibind-database skill to query the UniBind database for experimentally validated transcription factor (TF) binding sites”
- “/unibind-database”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6883275. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uvuvx
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
unibind.uio.no
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
Unibind Database loads about 1.3k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 519 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 519 words, ~1,327 tokens.
Download SKILL.mdSave it as
.claude/skills/unibind-database/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- 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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- 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.
uv run <SKILL DIR>/scripts/unibind_api.py list_speciesThe 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_linesandlist_tfsproduce large output. Pipe these throughjqto 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 withjq. - Pagination: Use
--pageand--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
Replace <SKILL DIR> with the absolute path to this skill's directory.
1. List Species
uv run <SKILL DIR>/scripts/unibind_api.py list_species2. List Collections
uv run <SKILL DIR>/scripts/unibind_api.py list_collections3. List Cell Lines & TFs (large output — use jp)
These commands return large datasets. Use uvx --from jmespath jp to extract
only the fields you need.
uv run <SKILL DIR>/scripts/unibind_api.py list_cell_lines | uvx --from jmespath jp "results[].name"
uv run <SKILL DIR>/scripts/unibind_api.py list_tfs | uvx --from jmespath jp "results[].tf_name"Show full SKILL.md (200 more words)Show less
4. List and Filter Datasets (and Profile-Specific Datasets)
Filter datasets using the following arguments:
--species(e.g., "Homo sapiens")--tf-name(e.g., "CTCF")--cell-line(e.g., "mESC")--collection(e.g., Permissive, Robust)--search(a search term)--biological-condition(biological condition or source)--data-source(source of data, e.g., "ENCODE")--has-pvalue("true" or "false")--identifier(e.g., "GSE60130")--jaspar-id(JASPAR database profile matrix ID)--model(prediction model)--summary(summary filter)--threshold-pvalue(p-value threshold)
Use list_datasets for standard datasets, or list_specific_datasets for
profile-specific queries.
uv run <SKILL DIR>/scripts/unibind_api.py list_datasets --species "Homo sapiens" --tf-name "CTCF" --data-source "ENCODE"
uv run <SKILL DIR>/scripts/unibind_api.py list_specific_datasets --species "Mus musculus" --cell-line "mESC"5. Get Dataset Details
uv run <SKILL DIR>/scripts/unibind_api.py get_dataset "EXP047889.HMLE-Twist-ER_breast_cancer.SMAD3"6. Download TFBS Files (BED / FASTA)
Downloads all TFBS files for a dataset to a local directory. Use --format bed
(default) or --format fasta.
uv run <SKILL DIR>/scripts/unibind_api.py download_tfbs "EXP047889.HMLE-Twist-ER_breast_cancer.SMAD3" --output-dir /tmp/tfbs --format bedAnti-Patterns
- DON'T attempt to use the UniBind API to query specific genomic intervals, locations, or genes.
- DON'T guess or hallucinate genome coordinates. Always use
ensembl-databaseas an external check if you're pulling local BED tracks for offline bedtools intersection. - DON'T use for motif models (PFMs). Use the jaspar-database skill instead.
- DON'T use for gene expression data. UniBind only stores binding events.
- DON'T assume tissue-specific expression from dataset lists alone.
- DON'T use
catto read large JSON output files into context. The output is too large. Usejqor write your own code to parse the output files.
© google-deepmind, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (scripts, references) in skills/unibind_database of google-deepmind/science-skills.
- SKILL.md
- references/citation.bib
- scripts/unibind_api.py
Open the folder on GitHubat commit 6883275
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in google-deepmind/science-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Unibind Database 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Unibind Database this skillgoogle-deepmind/science-skills | 3.2k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Native Subtitle Quote Imagechengyi-ai/native-subtitle-quote-image | 2.2k | 2 repos | ~1.8k | Automated safety check: Pass | MIT | |
| HyperFrames Media Useheygen-com/hyperframes | 59k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Videodbaffaan-m/ECC | 275k | 3 repos | ~3.5k | Automated safety check: Notes | MIT | |
| Edu Math Videowy51ai/edulab | 1.4k | — | ~2.5k | Automated safety check: Notes | Apache-2.0 | |
| Bilibili Transcribechubbyguan/chubbyskills | 1.2k | 1 repos | ~578 | Automated safety check: Notes | MIT |
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Related
Categories
Questions about Unibind Database
What does Unibind Database do?
Queries the UniBind database for experimentally validated transcription factor (TF) binding sites. Unibind Database is an agent skill from google-deepmind/science-skills. Queries the UniBind database for experimentally validated transcription factor (TF) binding sites.
When should I use Unibind Database?
Unibind Database fits situations like: retrieving direct TF-DNA interaction datasets; downloading binding site coordinates (BED/FASTA) for local analysis; listing available datasets by species; query specific intervals.
How do I install Unibind Database in Claude Code?
Run `npx skills add google-deepmind/science-skills --skill unibind-database -a claude-code`. Or copy the skill folder (skills/unibind_database in google-deepmind/science-skills) into .claude/skills/unibind-database in your project. Claude Code loads it when a task matches its description.
How do I install Unibind Database in Codex?
Run `npx skills add google-deepmind/science-skills --skill unibind-database -a codex`. Or copy the skill folder (skills/unibind_database in google-deepmind/science-skills) into .agents/skills/unibind-database in your project. Codex loads it when a task matches its description.
Can I use Unibind Database 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 google-deepmind/science-skills --skill unibind-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/unibind-database, .gemini/skills/unibind-database, .github/skills/unibind-database and .opencode/skills/unibind-database in your project.
What does Unibind Database need to run?
Going by SKILL.md and its folder, Unibind Database needs Python for the scripts in its folder and the command-line tools its instructions call (uv and uvx). Our summary lists: Python 3.
Does Unibind Database access the network?
SKILL.md names 1 domain. As links in the text: unibind.uio.no. This is read from the text; nothing was executed.
Is Unibind Database 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Unibind Database use?
Unibind Database is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Unibind Database use?
About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 187 tokens, read only when the agent opens those files.
What are the alternatives to Unibind Database?
Skills that share tags, products or a category with Unibind Database: Native Subtitle Quote Image (chengyi-ai/native-subtitle-quote-image, 2.2k stars), HyperFrames Media Use (heygen-com/hyperframes, 59k stars), Videodb (affaan-m/ECC, 275k stars) and Edu Math Video (wy51ai/edulab, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Unibind Database?
google-deepmind (a GitHub organization) maintains it in google-deepmind/science-skills, which has 3,220 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on September 15, 2026.
Source: google-deepmind/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.