Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
ToolUniverse workflow — Expression Data Retrieval. An agent skill from lamm-mit/scienceclaw.
Install Expression Data Retrieval
$ npx skills add lamm-mit/scienceclaw --skill expression-data-retrieval -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lamm-mit/scienceclaw expression-data-retrieval --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/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/expression-data-retrieval .claude/skills/expression-data-retrieval && 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 "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .claude/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrievalType 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 lamm-mit/scienceclaw --skill expression-data-retrieval -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lamm-mit/scienceclaw expression-data-retrieval --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/expression-data-retrieval .agents/skills/expression-data-retrieval && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .agents/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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 lamm-mit/scienceclaw --skill expression-data-retrieval -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lamm-mit/scienceclaw expression-data-retrieval --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/expression-data-retrieval .cursor/skills/expression-data-retrieval && 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 "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .cursor/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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/lamm-mit/scienceclaw.git --path skills/expression-data-retrieval--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 lamm-mit/scienceclaw --skill expression-data-retrieval -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lamm-mit/scienceclaw expression-data-retrieval --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/expression-data-retrieval .gemini/skills/expression-data-retrieval && 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 "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .gemini/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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 lamm-mit/scienceclaw expression-data-retrievalInstalls 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 lamm-mit/scienceclaw --skill expression-data-retrieval -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/expression-data-retrieval .github/skills/expression-data-retrieval && 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 "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .github/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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 lamm-mit/scienceclaw --skill expression-data-retrieval -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lamm-mit/scienceclaw expression-data-retrieval --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/expression-data-retrieval .opencode/skills/expression-data-retrieval && 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 "expression-data-retrieval" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/expression-data-retrieval into .opencode/skills/expression-data-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "expression-data-retrieval", 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
expression-data-retrieval- GitHub stars
- 244
- Token cost
- ~2.6k tokens
- SKILL.md length
- 607 words
- Files
- 3 (incl. scripts)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
ToolUniverse workflow — Expression Data Retrieval. An agent skill from lamm-mit/scienceclaw.
- Works in 4 steps: Clarification (When Needed) → Query Disambiguation → Data Retrieval (Internal) → …
- Tasks that involve Bioinformatics
- SKILL.md covers Workflow Overview, Phase 0: Clarification (When…, Phase 1: Query Disambiguation and Phase 2: Data Retrieval…, plus 7 more sections
- Runs Python scripts from its folder; reaches ebi.ac.uk
What it does
Expression Data Retrieval is an agent skill from lamm-mit/scienceclaw. ToolUniverse workflow — Expression Data Retrieval
Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/run.py`).
It sits in Research & Science, covering Bioinformatics. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Bioinformatics
Example prompts
- “/expression-data-retrieval”
Requirements
- Python 3
Workflow steps
4 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 ab9aba1. 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 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
ebi.ac.uk
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
Expression Data Retrieval loads about 2.6k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 607 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 lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 607 words, ~2,612 tokens.
Download SKILL.mdSave it as
.claude/skills/expression-data-retrieval/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- expression-data-retrieval
- description
- ToolUniverse workflow — Expression Data Retrieval
- source
- https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-expression-data-retrieval
name: tooluniverse-expression-data-retrieval description: Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
Gene Expression & Omics Data Retrieval
Retrieve gene expression experiments and multi-omics datasets with proper disambiguation and quality assessment.
IMPORTANT: Always use English terms in tool calls (gene names, tissue names, condition descriptions), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Workflow Overview
Phase 0: Clarify Query (if ambiguous)
↓
Phase 1: Disambiguate Gene/Condition
↓
Phase 2: Search & Retrieve (Internal)
↓
Phase 3: Report Dataset ProfilePhase 0: Clarification (When Needed)
Ask the user ONLY if:
- Gene name is ambiguous (e.g., "p53" → TP53 or MDM2 studies?)
- Tissue/condition unclear for comparative studies
- Organism not specified for non-human research
Skip clarification for:
- Specific accession numbers (E-MTAB-, E-GEOD-, S-BSST*)
- Clear disease/tissue + organism combinations
- Explicit platform requests (RNA-seq, microarray)
Phase 1: Query Disambiguation
1.1 Gene Name Resolution
If searching by gene, first resolve official identifiers:
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# For gene-focused searches, resolve official symbol first
# This helps construct better search queries
# Example: "p53" → "TP53" (official HGNC symbol)Gene Disambiguation Checklist:
- Official gene symbol identified (HGNC for human, MGI for mouse)
- Common aliases noted for search expansion
- Species confirmed
1.2 Construct Search Strategy
| User Query Type | Search Strategy |
|---|---|
| Specific accession | Direct retrieval |
| Gene + condition | "[gene] [condition]" + species filter |
| Disease only | "[disease]" + species filter |
| Technology-specific | Add platform keywords (RNA-seq, microarray) |
Phase 2: Data Retrieval (Internal)
Search silently. Do NOT narrate the process.
2.1 Search Experiments
# ArrayExpress search
result = tu.tools.arrayexpress_search_experiments(
keywords="[gene/disease] [condition]",
species="[species]",
limit=20
)
# BioStudies for multi-omics
biostudies_result = tu.tools.biostudies_search_studies(
query="[keywords]",
limit=10
)2.2 Get Experiment Details
For top results, retrieve full metadata:
# Get details for each relevant experiment
details = tu.tools.arrayexpress_get_experiment_details(
accession=accession
)
# Get sample information
samples = tu.tools.arrayexpress_get_experiment_samples(
accession=accession
)
# Get available files
files = tu.tools.arrayexpress_get_experiment_files(
accession=accession
)2.3 BioStudies Retrieval
# Multi-omics study details
study_details = tu.tools.biostudies_get_study_details(
accession=study_accession
)
# Study structure
sections = tu.tools.biostudies_get_study_sections(
accession=study_accession
)
# Available files
files = tu.tools.biostudies_get_study_files(
accession=study_accession
)Fallback Chains
| Primary | Fallback | Notes |
|---|---|---|
| ArrayExpress search | BioStudies search | ArrayExpress empty |
| arrayexpress_get_experiment_details | biostudies_get_study_details | E-GEOD may have BioStudies mirror |
| arrayexpress_get_experiment_files | Note "Files unavailable" | Some studies restrict downloads |
Phase 3: Report Dataset Profile
Output Structure
Present as a Dataset Search Report. Hide search process.
# Expression Data: [Query Topic]
**Search Summary**
- Query: [gene/disease] in [species]
- Databases: ArrayExpress, BioStudies
- Results: [N] relevant experiments found
**Data Quality Overview**: [assessment based on criteria below]
---
## Top Experiments
### 1. [E-MTAB-XXXX]: [Title]
| Attribute | Value |
|-----------|-------|
| **Accession** | [accession with link] |
| **Organism** | [species] |
| **Experiment Type** | RNA-seq / Microarray |
| **Platform** | [specific platform] |
| **Samples** | [N] samples |
| **Release Date** | [date] |
**Description**: [Brief description from metadata]
**Experimental Design**:
- Conditions: [treatment vs control, etc.]
- Replicates: [N biological, M technical]
- Tissue/Cell type: [if specified]
**Sample Groups**:
| Group | Samples | Description |
|-------|---------|-------------|
| Control | [N] | [description] |
| Treatment | [N] | [description] |
**Data Files Available**:
| File | Type | Size |
|------|------|------|
| [filename] | Processed data | [size] |
| [filename] | Raw data | [size] |
| [filename] | Sample metadata | [size] |
**Quality Assessment**: ●●● High / ●●○ Medium / ●○○ Low
- Sample size: [adequate/limited]
- Replication: [yes/no]
- Metadata completeness: [complete/partial]
---
### 2. [E-GEOD-XXXXX]: [Title]
[Same structure as above]
---
## Multi-Omics Studies (from BioStudies)
### [S-BSST-XXXXX]: [Title]
| Attribute | Value |
|-----------|-------|
| **Accession** | [accession] |
| **Study Type** | [proteomics/metabolomics/integrated] |
| **Organism** | [species] |
| **Samples** | [N] |
**Data Types Included**:
- [ ] Transcriptomics
- [ ] Proteomics
- [ ] Metabolomics
- [ ] Other: [specify]
---
## Summary Table
| Accession | Type | Samples | Platform | Quality |
|-----------|------|---------|----------|---------|
| [E-MTAB-X] | RNA-seq | [N] | Illumina | ●●● |
| [E-GEOD-X] | Microarray | [N] | Affymetrix | ●●○ |
---
## Recommendations
**For [specific analysis type]**:
- Best experiment: [accession] - [reason]
- Alternative: [accession] - [reason]
**Data Integration Notes**:
- Platform compatibility: [notes on combining datasets]
- Batch considerations: [if applicable]
---
## Data Access
### Direct Download Links
- [E-MTAB-XXXX processed data](link)
- [E-MTAB-XXXX raw data](link)
### Database Links
- ArrayExpress: https://www.ebi.ac.uk/arrayexpress/experiments/[accession]
- BioStudies: https://www.ebi.ac.uk/biostudies/studies/[accession]
Retrieved: [date]Data Quality Tiers
Assessment criteria for expression experiments:
| Tier | Symbol | Criteria |
|---|---|---|
| High Quality | ●●● | ≥3 bio replicates, complete metadata, processed data available |
| Medium Quality | ●●○ | 2-3 replicates OR some metadata gaps, data accessible |
| Low Quality | ●○○ | No replicates, sparse metadata, or data access issues |
| Use with Caution | ○○○ | Single sample, no replication, outdated platform |
Include assessment rationale:
**Quality**: ●●● High
- ✓ 4 biological replicates per condition
- ✓ Complete sample annotations
- ✓ Processed and raw data available
- ✓ Recent RNA-seq platformCompleteness Checklist
Every dataset report MUST include:
Per Experiment (Required)
- Accession number with database link
- Organism
- Experiment type (RNA-seq/microarray/etc.)
- Sample count
- Brief description
- Quality assessment
Show full SKILL.md (234 more words)Show less
Search Summary (Required)
- Query parameters stated
- Number of results
- Databases searched
Recommendations (Required)
- Best dataset for user's purpose (or "No suitable data found")
- Data access notes
Include Even If Empty
- Multi-omics studies section (or "No multi-omics studies found")
- Data integration notes (or "Single-platform data, no integration needed")
Common Use Cases
Disease Gene Expression
User: "Find breast cancer RNA-seq data"
result = tu.tools.arrayexpress_search_experiments(
keywords="breast cancer RNA-seq",
species="Homo sapiens",
limit=20
)→ Report top experiments with quality assessment
Gene-Specific Studies
User: "Find TP53 expression experiments in mouse"
result = tu.tools.arrayexpress_search_experiments(
keywords="TP53 p53", # Include aliases
species="Mus musculus",
limit=15
)→ Report experiments studying this gene
Specific Accession Lookup
User: "Get details for E-MTAB-5214" → Single experiment profile with all details and files
Multi-Omics Integration
User: "Find proteomics and transcriptomics studies for liver disease" → Search both ArrayExpress and BioStudies, note integration potential
Error Handling
| Error | Response |
|---|---|
| "No experiments found" | Broaden keywords, remove species filter, try synonyms |
| "Accession not found" | Verify format (E-MTAB-, E-GEOD-, S-BSST*), check if withdrawn |
| "Files not available" | Note in report: "Data files restricted by submitter" |
| "API timeout" | Retry once, then note: "(metadata retrieval incomplete)" |
Tool Reference
ArrayExpress (Gene Expression)
| Tool | Purpose |
|---|---|
arrayexpress_search_experiments | Keyword/species search |
arrayexpress_get_experiment_details | Full metadata |
arrayexpress_get_experiment_files | Download links |
arrayexpress_get_experiment_samples | Sample annotations |
BioStudies (Multi-Omics)
| Tool | Purpose |
|---|---|
biostudies_search_studies | Multi-omics search |
biostudies_get_study_details | Study metadata |
biostudies_get_study_files | Data files |
biostudies_get_study_sections | Study structure |
Search Parameters Reference
ArrayExpress
| Parameter | Description | Example |
|---|---|---|
keywords | Free text search | "breast cancer RNA-seq" |
species | Scientific name | "Homo sapiens" |
array | Platform filter | "Illumina" |
limit | Max results | 20 |
BioStudies
| Parameter | Description | Example |
|---|---|---|
query | Free text | "proteomics liver" |
limit | Max results | 10 |
© lamm-mit, 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) in skills/expression-data-retrieval of lamm-mit/scienceclaw.
- SKILL.md
- scripts/__pycache__/run.cpython-313.pyc
- scripts/run.py
Open the folder on GitHubat commit ab9aba1
Compare with similar skills
Expression Data Retrieval 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 |
|---|---|---|---|---|---|---|
| Expression Data Retrieval this skilllamm-mit/scienceclaw | 244 | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
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Categories
Questions about Expression Data Retrieval
What does Expression Data Retrieval do?
ToolUniverse workflow — Expression Data Retrieval. An agent skill from lamm-mit/scienceclaw. Expression Data Retrieval is an agent skill from lamm-mit/scienceclaw.
When should I use Expression Data Retrieval?
Expression Data Retrieval fits situations like: tasks that involve Bioinformatics.
How do I install Expression Data Retrieval in Claude Code?
Run `npx skills add lamm-mit/scienceclaw --skill expression-data-retrieval -a claude-code`. Or copy the skill folder (skills/expression-data-retrieval in lamm-mit/scienceclaw) into .claude/skills/expression-data-retrieval in your project. Claude Code loads it when a task matches its description.
How do I install Expression Data Retrieval in Codex?
Run `npx skills add lamm-mit/scienceclaw --skill expression-data-retrieval -a codex`. Or copy the skill folder (skills/expression-data-retrieval in lamm-mit/scienceclaw) into .agents/skills/expression-data-retrieval in your project. Codex loads it when a task matches its description.
Can I use Expression Data Retrieval 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 lamm-mit/scienceclaw --skill expression-data-retrieval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/expression-data-retrieval, .gemini/skills/expression-data-retrieval, .github/skills/expression-data-retrieval and .opencode/skills/expression-data-retrieval in your project.
What does Expression Data Retrieval need to run?
Going by SKILL.md and its folder, Expression Data Retrieval needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Expression Data Retrieval access the network?
SKILL.md names 1 domain. In commands or code: ebi.ac.uk; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Expression Data Retrieval 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 Expression Data Retrieval use?
Expression Data Retrieval 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 Expression Data Retrieval use?
About 2.6k tokens (SKILL.md is roughly 10k 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 Expression Data Retrieval?
Skills that share tags, products or a category with Expression Data Retrieval: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Expression Data Retrieval?
lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.
Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.