Pixi Environment Builder
xuzhougeng/wisp-science
A skill your agent uses when creating, migrating, or debugging pixi environments, especially for scientific Python, bioinformatics, single-cell analysis, CUDA/PyTorch, Jupyter/VS Code kernels…
Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant…
Install Alphagenome Predictions
$ npx skills add genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install genomicsxai/alphagenome-pytorch alphagenome-predictions --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/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .claude/skills/alphagenome-predictions && 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 "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .claude/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictionsType 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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install genomicsxai/alphagenome-pytorch alphagenome-predictions --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .agents/skills/alphagenome-predictions && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .agents/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install genomicsxai/alphagenome-pytorch alphagenome-predictions --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .cursor/skills/alphagenome-predictions && 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 "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .cursor/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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/genomicsxai/alphagenome-pytorch.git --path .claude/skills/alphagenome-predictions--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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install genomicsxai/alphagenome-pytorch alphagenome-predictions --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .gemini/skills/alphagenome-predictions && 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 "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .gemini/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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 genomicsxai/alphagenome-pytorch alphagenome-predictionsInstalls 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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .github/skills/alphagenome-predictions && 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 "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .github/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install genomicsxai/alphagenome-pytorch alphagenome-predictions --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/genomicsxai/alphagenome-pytorch.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/alphagenome-predictions .opencode/skills/alphagenome-predictions && 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 "alphagenome-predictions" agent skill from https://github.com/genomicsxai/alphagenome-pytorch/tree/main/.claude/skills/alphagenome-predictions into .opencode/skills/alphagenome-predictions/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphagenome-predictions", 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
alphagenome-predictions- GitHub stars
- 162
- Token cost
- ~868 tokens
- SKILL.md length
- 301 words
- Files
- 1
- Skills in repo
- 2
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant…
- The task is about USING the model for inference/predictions
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Not developing the package
What it does
Alphagenome Predictions is an agent skill from genomicsxai/alphagenome-pytorch. Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant effect scoring (agt score), or the Python API. Covers picking a specific assay, cell type, or resolution, e.g. "get DNase predictions from GM12878 at 128bp", "write a wrapper for all K562 predictions", filtering tracks by metadata (biosample, assay, ontology, strand). Use when the task is about USING the model for…
Its SKILL.md is about 870 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Deep learning and Bioinformatics. It works with PyTorch, Python and AnnData. The repository describes itself as: AlphaGenome PyTorch port. The licence is Apache-2.0.
When your agent uses it
- The task is about USING the model for inference/predictions
- Not developing the package
Example prompts
- “get DNase predictions from GM12878 at 128bp”
- “write a wrapper for all K562 predictions”
- “/alphagenome-predictions”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 72268c0. 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 bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Alphagenome Predictions loads about 868 tokens when it runs. Until then it costs about 147 tokens; SKILL.md has 301 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from genomicsxai/alphagenome-pytorch at commit 72268c0, republished under its Apache-2.0 licence (© genomicsxai). 301 words, ~868 tokens.
Download SKILL.mdSave it as
.claude/skills/alphagenome-predictions/SKILL.md (or your agent's skills folder).- name
- alphagenome-predictions
- description
- Run AlphaGenome-PyTorch to get genomic track predictions — via the `agt predict` CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant effect scoring (`agt score`), or the Python API. Covers picking a specific assay, cell type, or resolution, e.g. "get DNase predictions from GM12878 at 128bp", "write a wrapper for all K562 predictions", filtering tracks by metadata (biosample, assay, ontology, strand). Use when the task is about USING the model for inference/predictions, not developing the package.
Getting predictions from AlphaGenome-PyTorch
docs/alphagenome-usage.md is the canonical guide. Read only the relevant sections:
- Disk output:
Command line: agt predict - Variant effect scoring (SNV/VCF):
Variant scoring: agt score - Convert JAX weights / obtain a checkpoint:
Getting a checkpoint: agt convert - Python tensors and input shapes:
The 30-second versionandStep 1 - Assay/cell-type/resolution selection:
Step 2,Step 3, andRecipes - Exact counts and metadata literals:
Step 2andAvailable metadata fields - Per-gene counts / gene expression matrices:
Gene-level aggregation - Padding, custom metadata, precision, or raw outputs:
Gotchas
Try the CLI first — agt predict writes predictions to disk without any Python:
agt predict --model model.pth --output out/ --head dnase \
--locus chr1:1000000-1131072 --fasta hg38.fa --resolution 128Input modes (mutually exclusive): --locus (one interval), --bed (many regions),
--chromosomes (whole chromosomes, tiled), --sequences (raw FASTA → NPZ). Add
--anndata FILE --annotation GTF for a per-gene count table (AnnData); add --gene-strand match for RNA-seq so antisense tracks don't inflate counts. agt predict
is the same code path as the scripts/predict_*.py shims — prefer agt, which ships
with the package. See agt predict --help.
For variant effect scoring, use agt score (not predict):
agt score --model model.pth --fasta hg38.fa --variant "chr22:36201698:A>C" --output scores.tsv--vcf for batches; --scorer recommended (default) or a comma-separated subset;
gene-centric scorers need --gtf. See the guide's Variant scoring: agt score.
Use the Python API when you need tensors in-process or metadata-based selection:
- Load:
AlphaGenome.from_pretrained("model.pth", device=...). - Predict with metadata:
model.predict(dna, organism_index, named_outputs=True)wherednais one-hot(B, 131072, 4)andorganism_indexis 0=human / 1=mouse. - Select tracks by biology, then index by resolution:
out.dnase.select(biosample_name="GM12878")[128].tensor. - Filter fields include
biosample_name,assay_title,biosample_type,histone_mark,transcription_factor,ontology_curie,strand. - Gene × track expression matrices:
alphagenome_pytorch.aggregation(submodule import, not the package root) —gene_expression(exons, log) oraggregate_genes(gene body, linear), then.to_anndata()/.to_dataframe().
Explore available tracks without weights: agt info --heads,
agt info --tracks dnase --filter biosample_name=K562 (prints track indices for
agt predict --tracks), or in Python TrackMetadataCatalog.load_builtin("human").
For the deeper API reference see docs/named_outputs.rst; for package
development conventions see CLAUDE.md.
© genomicsxai, 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
Just SKILL.md in .claude/skills/alphagenome-predictions of genomicsxai/alphagenome-pytorch.
Open the folder on GitHubat commit 72268c0
Compare with similar skills
Alphagenome Predictions 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 |
|---|---|---|---|---|---|---|
| Alphagenome Predictions this skillgenomicsxai/alphagenome-pytorch | 162 | — | ~868 | Automated safety check: Pass | Apache-2.0 | |
| Pixi Environment Builderxuzhougeng/wisp-science | 1k | — | ~3.7k | Automated safety check: Pass | AGPL-3.0 | |
| PyTorch Lightning TrainingOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2.3k | Automated safety check: Pass | MIT | |
| pyvene Causal InterventionsOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Uv Pypi PublishML4ITS/TimeVQVAE | 166 | — | ~178 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Alphagenome Predictions
What does Alphagenome Predictions do?
Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant…. Alphagenome Predictions is an agent skill from genomicsxai/alphagenome-pytorch. Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant effect scoring (agt score), or the Python API.
When should I use Alphagenome Predictions?
Alphagenome Predictions fits situations like: the task is about USING the model for inference/predictions; not developing the package.
How do I install Alphagenome Predictions in Claude Code?
Run `npx skills add genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a claude-code`. Or copy the skill folder (.claude/skills/alphagenome-predictions in genomicsxai/alphagenome-pytorch) into .claude/skills/alphagenome-predictions in your project. Claude Code loads it when a task matches its description.
How do I install Alphagenome Predictions in Codex?
Run `npx skills add genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a codex`. Or copy the skill folder (.claude/skills/alphagenome-predictions in genomicsxai/alphagenome-pytorch) into .agents/skills/alphagenome-predictions in your project. Codex loads it when a task matches its description.
Can I use Alphagenome Predictions 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 genomicsxai/alphagenome-pytorch --skill alphagenome-predictions -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/alphagenome-predictions, .gemini/skills/alphagenome-predictions, .github/skills/alphagenome-predictions and .opencode/skills/alphagenome-predictions in your project.
What does Alphagenome Predictions need to run?
SKILL.md names no scripts, command-line tools or credentials: Alphagenome Predictions is instructions for the agent only. Our summary lists: Python 3.
Does Alphagenome Predictions access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Alphagenome Predictions 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 Alphagenome Predictions use?
Alphagenome Predictions 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 Alphagenome Predictions use?
About 868 tokens (SKILL.md is roughly 3.5k 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 Alphagenome Predictions?
Skills that share tags, products or a category with Alphagenome Predictions: Pixi Environment Builder (xuzhougeng/wisp-science, 1k stars), PyTorch Lightning Training (Orchestra-Research/AI-Research-SKILLs, 13k stars), pyvene Causal Interventions (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Alphagenome Predictions?
genomicsxai (a GitHub organization) maintains it in genomicsxai/alphagenome-pytorch, which has 162 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on September 15, 2026.
Source: genomicsxai/alphagenome-pytorch on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.