Borrowing it
Nothing to install: this file belongs to donbr/lifesciences-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/donbr/lifesciences-research/main/.claude/skills/lifesciences-crispr/SKILL.mdgit clone --depth 1 https://github.com/donbr/lifesciences-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/donbr/lifesciences-research/lifesciences-crispr)<a href="https://agentmods.dev/skills/donbr/lifesciences-research/lifesciences-crispr"><img src="https://agentmods.dev/badge/skills/donbr/lifesciences-research/lifesciences-crispr/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/donbr/lifesciences-research/lifesciences-crispr"><img src="https://agentmods.dev/badge/skills/donbr/lifesciences-research/lifesciences-crispr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.02993 |
| Opus 5 | $0.00051 | $0.01496 |
| Sonnet 5 | $0.00020 | $0.00599 |
| Haiku 4.5 | $0.00010 | $0.00299 |
Grade A, and why
lifesciences-crispr scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Validate synthetic lethality hypotheses using BioGRID ORCS CRISPR screen data via curl. How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRISPR Essentiality & Synthetic Lethality Validation
Validate synthetic lethality hypotheses using BioGRID ORCS CRISPR screen data via curl.
Quick Reference
| Task | Endpoint | Key Parameters |
|---|---|---|
| Get essential screens only | /gene/{entrez_id}?hit=yes |
hit=yes filters to essential |
| Get all gene screens | /gene/{entrez_id} |
Returns all screens |
| Get screen annotations | /screens/?screenID=1|2|3 |
Pipe-separated IDs |
| Find cell line screens | /screens/?cellLine={name} |
Cell line name |
BioGRID ORCS API
IMPORTANT: Use orcsws.thebiogrid.org (NOT orcs.thebiogrid.org)
- Base URL:
https://orcsws.thebiogrid.org - Auth: Requires
BIOGRID_API_KEYwhich is captured in.envfile - Rate Limit: ~10 req/s
- Formats:
format=tab(default) orformat=json
Critical Parameter: hit=yes
Always use hit=yes when querying gene essentiality. This filters results to only screens where the gene scored as a hit (essential), dramatically reducing data volume:
- Without filter: ~1,400 records (all screens)
- With
hit=yes: ~300-400 records (essential screens only)
Data Format (JSON)
{
"SCREEN_ID": "16",
"IDENTIFIER_ID": "7298",
"OFFICIAL_SYMBOL": "TYMS",
"SCORE.1": "100.84",
"SCORE.2": "-",
"HIT": "YES",
"SOURCE": "BioGRID ORCS"
}
Screen Annotation Fields
Query /screens/?screenID={ids} to get cell line and publication data:
SCREEN_ID: Unique screen identifierSOURCE_ID: PubMed IDAUTHOR: First author and yearCELL_LINE: Cell line namePHENOTYPE: Screen phenotype (e.g., "cell proliferation")
5-Phase Synthetic Lethality Validation Workflow
Phase 1: Resolve Gene Identifiers
Use Life Sciences MCPs to get Entrez IDs:
# Using HGNC MCP: search_genes → get_gene
# Extract "entrez" from cross_references object
# Example: DHODH → {"cross_references": {"entrez": "1723"}}
# Using Entrez MCP: search_genes
# Top result format: "NCBIGene:1723"
# Extract numeric ID: 1723
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 297 lines · 102 tokens per session scan A 0323890127db
lifesciences-crispr is a skill published in the GitHub repository donbr/lifesciences-research (7 stars, last pushed 9d ago), licensed MIT. It adds 102 tokens to every session and 2,993 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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