tooluniverse-functional-genomics-screens

tooluniverse-functional-genomics-screens is a skill for Claude Code from mims-harvard/ToolUniverse. It costs 90 tokens per session (1,385 once invoked), scanned A, original, Apache-2.0.

A workflow for interpreting hits from CRISPR or RNA-interference screens, experiments that test which genes affect a cell or disease-related outcome. It combines essentiality, genetic constraint, pathways, druggability, and clinical evidence.

In plain words
What is it for?
Use it to prioritize screen hits, compare context-specific and broadly essential genes, examine pathway support, assess druggability, and connect findings with cancer or clinical evidence.
Why use it?
It treats screen hits as hypotheses rather than confirmed targets and helps identify false positives or genes that are essential in every cell. It also favors pathway-level evidence and targets that could realistically be changed with drugs.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Part of the tooluniverse plugin — 162 skills, 8 commands, 1 agent, 1 hook, 1 MCP server shipped together

Good fit Use it to prioritize screen hits, compare context-specific and broadly essential genes, examine pathway support, assess druggability, and connect findings with cancer or clinical evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens
About the project

ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.

mims-harvard/ToolUniverse · 1,744 stars · on GitHub · aiscientist.tools

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-functional-genomics-screens
Clone the repo
git clone --depth 1 https://github.com/mims-harvard/ToolUniverse

Made for: Claude Code.

Or install tooluniverse, the plugin that ships this one along with the rest of its 162 skills, 8 commands, 1 agent, 1 hook, 1 MCP server.

Wrote 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.

agentmods badge for tooluniverse-functional-genomics-screens

README.md
[![agentmods](https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens/github.svg)](https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens)
Your own site
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens/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.

agentmods 80×15 button for tooluniverse-functional-genomics-screens

Your own site · 80×15
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-functional-genomics-screens.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,385 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.01385
Opus 5.5 $0.00036 $0.00554
Sonnet 5.5 $0.00018 $0.00277
Haiku 4.5 $0.00009 $0.00138

Measured 1mo ago against content hash 0f19202a5d54, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tooluniverse-functional-genomics-screens scanned grade A with 0 findings 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 1mo 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/skills/tooluniverse-functional-genomics-screens/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Functional Genomics Screen Interpretation

Pipeline for validating and prioritizing hits from genetic screens (CRISPR-KO, CRISPRi, shRNA) by integrating essentiality (DepMap), constraint (gnomAD), pathways (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC).

Guiding principles:

  1. Hits are hypotheses -- screen results contain false positives; validate through orthogonal evidence
  2. Selectivity matters -- pan-essential genes are poor drug targets; context-specific essentiality is high-value
  3. Pathway over gene -- enriched pathways are more robust than individual hits
  4. Druggability is practical -- prioritize chemically modulable targets
  5. English-first queries -- use English gene names in tool calls

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first.


COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Workflow

Phase 0: Input Processing → gene list, screen type, cell line, disease context
Phase 1: Hit Validation → DepMap dependency, gnomAD constraint, UniProt function
Phase 2: Pathway & Network → Reactome enrichment, STRING network, functional clusters
Phase 3: Druggability → DGIdb interactions, druggable categories, PharmacoDB
Phase 4: Clinical Evidence → CIViC, COSMIC mutations
Phase 5: Literature → PubMed for key hits
Phase 6: Prioritized Report → ranked target list with multi-dimensional scoring

Phase Details

Phase 1: Hit Validation

Tools:

  • DepMap_get_gene_dependencies(gene_symbol=...) -- returns gene metadata only (NOT per-cell-line scores)
  • DepMap_search_cell_lines(query=...) -- cell line metadata
  • gnomad_get_gene_constraints(gene_symbol=...) -- pLI, LOEUF (may return "Service overloaded")
  • UniProt_get_function_by_accession(accession=...) -- function summary

Read the full file on GitHub · 107 lines

Changes

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.

  1. 1mo ago First seen · 107 lines · 90 tokens per session scan A 0f19202a5d54

Subscribe to this mod's changes

tooluniverse-functional-genomics-screens is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 90 tokens to every session and 1,385 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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