Metabolic Study Planner
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
$ npx skills add google-deepmind/science-skills --skill clinvar-database -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google-deepmind/science-skills clinvar-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/clinvar_database .claude/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .claude/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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/clinvar_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 clinvar-database -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google-deepmind/science-skills clinvar-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/clinvar_database .agents/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .agents/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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 clinvar-database -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google-deepmind/science-skills clinvar-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/clinvar_database .cursor/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .cursor/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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/clinvar_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 clinvar-database -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google-deepmind/science-skills clinvar-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/clinvar_database .gemini/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .gemini/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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 clinvar-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 clinvar-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/clinvar_database .github/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .github/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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 clinvar-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 clinvar-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/clinvar_database .opencode/skills/clinvar-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 "clinvar-database" agent skill from https://github.com/google-deepmind/science-skills/tree/main/skills/clinvar_database into .opencode/skills/clinvar-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "clinvar-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.
clinvar-databaseA skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
Clinvar Database is an agent skill from google-deepmind/science-skills. Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls for human genomic variants.
Its SKILL.md is about 3.9k 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/clinvar_api.py`).
It sits in Research & Science, covering Bioinformatics. 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.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6883275. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uvFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
ncbi.nlm.nih.govFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
NCBI_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Clinvar Database loads about 3.9k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 1,719 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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
3. **`.env` file**: Make sure the `.env` file exists in your home directory.I_KEY` to help the user add it to their `.env` file.user add it to their `.env` file, then retry.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.
The full file from google-deepmind/science-skills at commit 6883275, republished under its Apache-2.0 licence (© google-deepmind). 1,719 words, ~3,902 tokens.
.claude/skills/clinvar-database/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH..env file: Make sure the .env file exists in your home directory.
Create one if it does not exist.NCBI_API_KEY (optional): Raises the NCBI rate limit from 3 to 10
requests/second. The skill works without it, but a key is recommended if the
user plans many queries or encounters a 429 error. You can register for a
key for free at https://www.ncbi.nlm.nih.gov/account/settings/. You MUST
use the safe credentials protocol in the credentials skill to check for
and request this key if this skill looks relevant to the user's request.ClinVar is the primary consensus record for clinical classifications of human genomic variations. It provides the "clinical ground truth" for pathogenicity labels (Pathogenic, Likely Pathogenic, Benign, VUS) based on assertions from global laboratories.
Use when you need to:
Do NOT use when you need to:
ClinVar queries are executed via a robust Python wrapper script to handle strict rate limiting and XML/JSON parsing.
Example: Search for BRCA1 variants
uv run scripts/clinvar_api.py search --query "BRCA1[gene]" --output results.json--retmax 200. For
any "List all" or gene-wide request, you MUST explicitly set --retmax
higher (e.g., 1000) to ensure data completeness.credentials skill to check for
and request the NCBI_API_KEY to help the user add it to their .env file.count — Count Matching VariantsPurpose: Check how many variants match a query without fetching IDs. Use to
decide whether a full search is warranted.
Arguments:
--query: (Required) NCBI Entrez search query string.--output: (Required) Output JSON file path.Example: uv run scripts/clinvar_api.py count \ --query "TP53[gene] AND \"uncertain significance\"[clinsig]" \ --output count.json Output:
{"total_count": <int>}
search — Search VariantsPurpose: Identify variants based on genomic location, gene symbols, or clinical attributes using NCBI Entrez search syntax. The search command automatically paginates through all matching results to ensure complete, deterministic retrieval.
# Fetch ALL matching variants (default behavior)
uv run scripts/clinvar_api.py search \
--query "BRCA1[gene]" --output results.json
# Search by Chromosome and Position Range
uv run scripts/clinvar_api.py search \
--query "11[chr] AND 5225000:5226000[chrpos]" --output results.json
# Combine terms using Entrez syntax
uv run scripts/clinvar_api.py search \
--query "HBB[gene] AND pathogenic[clinsig]" --output results.json
# Cap results at 50
uv run scripts/clinvar_api.py search \
--query "TP53[gene]" --retmax 50 --output results.jsonArguments:
--query: (Required) NCBI Entrez search query string.--retmax: Maximum total number of variant IDs to return. Default is 0,
which means "fetch all matching results." Set to a positive integer to cap
the result set.--page_size: Number of IDs to fetch per API request (default: 500, max:
10000 per NCBI limits).--output: (Required) Output JSON file path.Output: A JSON object containing:
total_count — Total number of matching variants in ClinVar.fetched_count — Number of IDs actually retrieved.variant_ids — List of ClinVar Variation ID strings.summary — Get Interpretation SummaryPurpose: Retrieve top-line clinical significance labels, star ratings (review status), and basic phenotype data for rapid variant screening.
# Get summary for one or more Variation IDs
uv run scripts/clinvar_api.py summary \
--variant_ids 12345 67890 --output summary.jsonArguments:
--variant_ids: (Required) One or more ClinVar Variation IDs.--output: (Required) Output JSON file path.Output: A JSON list of summary objects, each containing:
variant_id, title, clinical_significance, review_status, last_evaluated, phenotypesgenes — list of {gene_id, symbol, strand}variation_type — e.g., single nucleotide variant, Deletion, Insertionmolecular_consequences — list of strings (e.g., ["missense variant", evidence — Get Clinical EvidencePurpose: Fetch the full clinical record for a single variant, including free-text clinician rationales, assertion methods, and specific submitter notes.
# Get full evidence for a single Variation ID
uv run scripts/clinvar_api.py evidence \
--variant_id 12345 --output evidence.jsonArguments:
--variant_id: (Required) A single ClinVar Variation ID.--output: (Required) Output JSON file path.Output: A JSON object containing:
variant_idallele_info — {chromosome, position_start, position_stop, reference_allele, alternate_allele, cytogenetic_band, dbsnp_rsid} (GRCh38
preferred)conditions — list of {name, medgen_cui, omim_id, orphanet_id, hpo_terms}functional_consequences — list of {value, sequence_ontology_id}structural_variant_details — {outer_start, inner_start, inner_stop, outer_stop, copy_number} (present only for CNVs, otherwise null)citation_references — list of PubMed IDs cited in the global "Citations"
sectionsubmissions — list of per-submitter records, each containing:submitter_name, classification, curator_notes,
assertion_criteriadate_last_evaluated — when the submitter last reviewed the
classificationFor large or unknown result sets, use count first to decide whether to
proceed, then search (which auto-paginates and returns total_count /
fetched_count), then summary to screen.
# Step 1: Gauge size (optional — search also returns total_count)
uv run scripts/clinvar_api.py count \
--query "HBB[gene] AND pathogenic[clinsig]" --output count.json
# Step 2: Fetch all variant IDs (auto-paginates)
uv run scripts/clinvar_api.py search \
--query "HBB[gene] AND pathogenic[clinsig]" --output ids.json
# Step 3: Get summaries (extract variant_ids from search output)
uv run scripts/clinvar_api.py summary \
--variant_ids 12345 67890 --output summary.jsonWhen you need the full clinical picture for a specific variant — including
submitter rationales, PubMed citations, ontology-linked conditions, and allele
coordinates — use evidence.
uv run scripts/clinvar_api.py evidence \
--variant_id 12345 --output evidence.jsonClinVar metadata is inconsistent. To fulfill "List all" requests, do not rely on a single filter. Perform the following in a single turn and merge results:
"3 prime UTR variant"[molecular_consequence]).c.*).[chrpos]).This "triangulation" ensures structural variants with missing labels are not overlooked.
molecular_consequences alone can be ambiguous (e.g., splice donor variant
appears in both coding and non-coding contexts). Always cross-check the title
field for HGVS patterns:
c.-… — 5' UTR (non-coding)c.*… — 3' UTR (non-coding)c.123+N / c.123-N — intronic (non-coding)p.Trp146Arg etc. — protein effect (coding)A variant with UTR/intronic HGVS and no p. annotation is non-coding, even with
splicing labels. Conversely, any p. annotation indicates a coding effect.
"3 prime UTR variant"[mol_consequence]c.*"5 prime UTR variant"[mol_consequence]c.-review_status filter. This is the most efficient way to distinguish
between single-laboratory assertions and panel-reviewed ground truth.summary → clinical_significancesummary → genessummary → variation_typesummary → molecular_consequencesevidence → allele_infoevidence → conditionsevidence → functional_consequencesevidence →
structural_variant_detailsevidence → citation_referenceslast_evaluatedevidence → submissions[].curator_notesTo get precise genomic coordinates in the format <chrom>:<pos>:<ref>><alt>
(e.g., chr5:70951945:G>A), you must use the evidence command, as these
details are not available in the summary output.
You MUST always include genomic coordinates in the format
<chrom>:<pos>:<ref>><alt> when listing or presenting variants, even if not
explicitly requested by the user. If coordinates are missing from the summary,
use the evidence command or dbSNP fallback to retrieve them.
uv run scripts/clinvar_api.py evidence --variant_id <ID> --output evidence.json.evidence command parses the XML. Extract:ChrpositionVCF (or start)referenceAlleleVCF (or referenceAllele)alternateAlleleVCF (or alternateAllele) from the
SequenceLocation element with Assembly="GRCh38".Fallback for Imprecise Coordinates (Gene Range): ClinVar often returns the
full gene range for non-coding variants. If the extracted coordinates correspond
to the gene range instead of a specific position, use the dbsnp-database skill
to resolve the precise coordinates using the dbsnp_rsid or HGVS title: 1.Check
for dbsnp_rsid in the evidence output. 2. Run uv run scripts/dbsnp_cli.py resolve-rsid {rsid} to get precise GRCh38 coordinates. 3. Format as
<chrom>:<pos>:<ref>><alt> using the SPDI or HGVS data from dbSNP.
The structural_variant_details field is only populated for copy number
variants (CNVs). For standard SNVs and small indels this field will be null.
Use the allele_info fields (position_start, position_stop,
reference_allele, alternate_allele) instead.
Large copy-number variants (CNVs) frequently have empty
molecular_consequences. If a variant title mentions "del" and coordinates
overlap your target region, it is relevant regardless of missing labels.
You can register for a key for free at
https://www.ncbi.nlm.nih.gov/account/settings/. You MUST use the safe
credentials protocol in the credentials skill to check for and request this
key if this skill looks relevant to the user's request.
uv run to execute python.jq is unavailable pivot immediately to using Python one-liners for
processing JSON (e.g., uv run python3 -c "import json; ...").count before search to understand the result set size.search command fetches all results by default and includes
total_count and fetched_count in the output — always verify these match
to confirm complete retrieval.last_evaluated.clinvar_api.py client which handles the unpredictable XML schemas
robustly.credentials skill to check for and request the NCBI_API_KEY to help the
user add it to their .env file, then retry.11[chr] AND 1234[chrpos]), not raw ATCG strings..lower()) when filtering.search output as a bare list — search returns a JSON object
with total_count, fetched_count, and variant_ids — not a bare list.© 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
SKILL.md and 2 other files (scripts, references) in skills/clinvar_database of google-deepmind/science-skills.
Open the folder on GitHubat commit 6883275
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in google-deepmind/science-skills, which our catalogue first saw on October 7, 2026.
Clinvar 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 |
|---|---|---|---|---|---|---|
| Clinvar Database this skillgoogle-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 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| MFA Pipeline Orchestratoraiming-lab/AutoResearchClaw | 15k | — | ~923 | Automated safety check: Pass | MIT | |
| Singlecell Qcxuzhougeng/wisp-science | 1k | — | ~1.6k | Automated safety check: Pass | AGPL-3.0 | |
| Paper Expert Generatorguhaohao0991/PaperClaw | 250 | — | ~2k | Automated safety check: Pass | None |
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
xuzhougeng/wisp-science
A skill your agent uses when designing, reviewing, or implementing single-cell RNA-seq QC in Python or R with a human-in-the-loop, data-driven approach.
guhaohao0991/PaperClaw
Generate a specialized domain-expert research agent modeled on PaperClaw architecture.
JimLiu/science-skills
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi.
google-deepmind/science-skills
Query ClinicalTrials.gov via APIv2. An agent skill from google-deepmind/science-skills.
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
google-deepmind/science-skills
A skill your agent uses when you want to retrieve quantitative RNA expression data and variant eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54 non-diseased tissue sites.
google-deepmind/science-skills
A skill your agent uses when you want to retrieve semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA).
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
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.
Categories
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…. Clinvar Database is an agent skill from google-deepmind/science-skills., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls for human genomic variants.
Clinvar Database fits situations like: needing clinical significance; pathogenicity classifications (e.g; clinical evidence rationales; finding hard positive benchmark controls for human genomic variants.
Run `npx skills add google-deepmind/science-skills --skill clinvar-database -a claude-code`. Or copy the skill folder (skills/clinvar_database in google-deepmind/science-skills) into .claude/skills/clinvar-database in your project. Claude Code loads it when a task matches its description.
Run `npx skills add google-deepmind/science-skills --skill clinvar-database -a codex`. Or copy the skill folder (skills/clinvar_database in google-deepmind/science-skills) into .agents/skills/clinvar-database in your project. Codex loads it when a task matches its description.
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 clinvar-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/clinvar-database, .gemini/skills/clinvar-database, .github/skills/clinvar-database and .opencode/skills/clinvar-database in your project.
Going by SKILL.md and its folder, Clinvar Database needs Python for the scripts in its folder, the command-line tools its instructions call (uv) and credentials named NCBI_API_KEY. Our summary lists: Python 3; A credential in NCBI_API_KEY.
SKILL.md names 1 domain. As links in the text: ncbi.nlm.nih.gov. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. 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.
Clinvar 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.
About 3.9k tokens (SKILL.md is roughly 16k 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 275 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Clinvar Database: Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), MFA Pipeline Orchestrator (aiming-lab/AutoResearchClaw, 15k stars) and Singlecell Qc (xuzhougeng/wisp-science, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.